Number Service URL Catalogue URL Title Content 1 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-health-urban-heat-islands-current-climate https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-health-urban-heat-islands-current-climate app-health-urban-heat-islands-current-climate This application presents visualisations of Urban Heat Island (UHI) effect over the ten year period of 2008-2017. Users can select from 100 European cities for each year from 2008-2017, for both Summer (June, July August) and Winter (December, January, February) seasons. UHI maps are provided for the annual mean daytime and night-time UHI for the selected year, and the mean daytime and night-time UHI for the 10 year period 2008 to 2017. The urban environment experiences higher temperatures than rural areas for many reasons, including the higher amount of paved surfaces and higher anthropogenic heat. The so-called Urban Heat Island (UHI) is the difference between the temperature at a location and the average temperature in the surrounding rural areas, and can range from a few degrees up to more than 10°C. The input data comes from the UrbClim model, which utilises ERA 5 variables, namely air temperature, specific humidity, relative humidity and wind speed. The UrbClim model provides 100m resolution data for urban scale applications, specifically addressing the phenomena of urban heat isalnds. User-selectable parameters User-selectable parameters City: European city for which to visualise urban heat island effect. Season: visualise either Summer or Winter heat island intensity. Year: heat island intensity is available for each year in the period of 2008-2017. City: European city for which to visualise urban heat island effect. Season: visualise either Summer or Winter heat island intensity. Year: heat island intensity is available for each year in the period of 2008-2017. More details about the products are given in the Documentation section. INPUT VARIABLES Name Units Description Source Air temperature K Air temperature at the height of 2m above the surface. UrbClim INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Air temperature K Air temperature at the height of 2m above the surface. UrbClim Air temperature K Air temperature at the height of 2m above the surface. UrbClim UrbClim OUTPUT VARIABLES Name Units Description Urban Heat Island (UHI) effect °C Temperature difference with respect to the mean rural temperature. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Urban Heat Island (UHI) effect °C Temperature difference with respect to the mean rural temperature. Urban Heat Island (UHI) effect °C Temperature difference with respect to the mean rural temperature. 2 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-health-urban-heat-related-mortality-projections https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-health-urban-heat-related-mortality-projections app-health-urban-heat-related-mortality-projections This application presents the number of heat wave days accross Europe and the corresponding number of deaths attributable to heat waves for nine European cities. Estimates of the number of heat wave days are provided for current and future climate considering the RCP 4.5 and RCP 8.5 scenarios. The heat-related mortality estimates are split further to provide estimates considering no socio-economic change, adaptation and population socio-economic scenarios. Estimates of heat-related mortality are derived from estimates of heat waves based on the EURO-CORDEX projections and population projections provided by Eurostat. no socio-economic change adaptation population This is of great use for public health users to raise awareness of health risks related to climate change and heat waves increases and for the definition and planning of adaptation plans and future policy. The graphical output of the application consists of a livemap and a child app. The livemap shows the European distribution of the number of heat wave days based on the Euroheat definition. Several layers are selectable in the livemap: Historical; Near future 2031-2060 with RCP4.5; Near future 2031-2060 with RCP8.5; Far future 2071-2100 with RCP4.5; Far future 2071-2100 with RCP8.5. Historical; Near future 2031-2060 with RCP4.5; Near future 2031-2060 with RCP8.5; Far future 2071-2100 with RCP4.5; Far future 2071-2100 with RCP8.5. Users can click on one of 9 European cities included in the study. When a city is selected, a child app appears consisting of two different outputs: A timeseries of the number of heat wave days from 1986 to 2085; A boxplot of heat realted mortality. A timeseries of the number of heat wave days from 1986 to 2085; A boxplot of heat realted mortality. The timeseries represents the number of heat wave days for each year from 1986 to 2085. Each year represents a 30-year average from year-15 to year+15. Therefore, the period 1986 to 2085 covers computed values of 1971 to 2100, but averaged over 30-year periods and represented as a moving average. The graph includes both RCP4.5 and RCP8.5. The boxplot presents estimates of heat-related mortality for the selected city for a range of time-periods, RCP scenarios and socio-economic scenarios. The spread of the boxplot is determined by the climate model ensemble used in the mortality calculation. INPUT VARIABLES Name Units Description Source Heat wave days days Number of heat wave days in a year for given definitions: climatological EURO-CORDEX, Euroheat project & national definitions. Heat wave and cold spell projections Mortality Number of deaths per year Number of deaths caused by heat waves Unpublished CDS dataset (please refer to documentation) INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Heat wave days days Number of heat wave days in a year for given definitions: climatological EURO-CORDEX, Euroheat project & national definitions. Heat wave and cold spell projections Heat wave days days Number of heat wave days in a year for given definitions: climatological EURO-CORDEX, Euroheat project & national definitions. Heat wave and cold spell projections Heat wave and cold spell projections Mortality Number of deaths per year Number of deaths caused by heat waves Unpublished CDS dataset (please refer to documentation) Mortality Number of deaths per year Number of deaths caused by heat waves Unpublished CDS dataset (please refer to documentation) 3 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/northward-heat-transport-global-ocean-atlantic-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_WMHE_northward_mht Northward Heat Transport for Global Ocean, Atlantic and Indian+Pacific basins from Reanalysis DEFINITION Meridional Heat Transport is computed by integrating the heat fluxes along the zonal direction and from top to bottom of the ocean. They are given over 3 basins (Global Ocean, Atlantic Ocean and Indian+Pacific Ocean) and for all the grid points in the meridional grid of each basin. The mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW). CONTEXT The ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth’s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth’s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. CMEMS KEY FINDINGS After an anusual 2016 year (Bricaud 2016), with a higher global meridional heat transport in the tropical band explained by, the increase of northward heat transport at 5-10 ° N in the Pacific Ocean during the El Niño event, 2017 northward heat transport is lower than the 1993-2014 reference value in the tropical band, for both Atlantic and Indian + Pacific Oceans. At the higher latitudes, 2017 northward heat transport is closed to 1993-2014 values. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00246 https://doi.org/10.48670/moi-00246 4 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-urban-climate-cities https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-urban-climate-cities sis-urban-climate-cities The dataset contains air temperature, specific humidity, relative humidity and wind speed for 100 European cities for the current climate. The data were generated using the urban climate model UrbClim, developed at VITO. This model was designed to simulate and study the urban heat island effect (UHI) and other urban climate variables at a spatial resolution of 100 metres. The unique capabilities of UrbClim allow to generate spatially explicit timeseries of hourly variables from which a variety of indicators can be retrieved in postprocessing at the scale of a city neighbourhood. For this specific dataset, the ERA5 reanalysis large-scale weather conditions are downscaled to agglomeration-scale. UrbClim then computes the impact of urban development on the most frequent weather parameters, such as temperature and humidity. The 100 European cities for the urban simulations were selected based on user requirements within the health community. Furthermore, a high spatial distribution was aimed with specific focus on Eastern European countries that often lack access to relevant information. The data was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage European Horizontal resolution 100m x 100m Temporal coverage From January 2008 to December 2017 Temporal resolution Hourly File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency No updates expected. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage European Horizontal coverage European Horizontal resolution 100m x 100m Horizontal resolution 100m x 100m Temporal coverage From January 2008 to December 2017 Temporal coverage From January 2008 to December 2017 Temporal resolution Hourly Temporal resolution Hourly File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency No updates expected. Update frequency No updates expected. MAIN VARIABLES Name Units Description Air temperature K Air temperature valid for a grid cell at the height of 2m above the surface. Land-sea mask Dimensionless The land cover classes from CORINE that represent land areas are masked with value 1 and land cover classes that represent water surfaces are masked as NaN. Relative humidity % Relation between actual humidity and saturation humidity at 2m height. Values are in the interval [0,100]: 0% means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. Rural-urban mask Dimensionless The land cover classes from CORINE that represent rural areas are masked with value 1 and land cover classes that represent urban areas are masked as NaN. Specific humidity kg kg-1 Mass of water vapour in a unit mass of moist air at 2m height. Wind speed m s-1 Wind speed valid for a grid cell at the height of 2m above the surface. It is computed from both the zonal (u) and the meridional (v) wind components by sqrt(u2 + v2 ). MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Air temperature K Air temperature valid for a grid cell at the height of 2m above the surface. Air temperature K Air temperature valid for a grid cell at the height of 2m above the surface. Land-sea mask Dimensionless The land cover classes from CORINE that represent land areas are masked with value 1 and land cover classes that represent water surfaces are masked as NaN. Land-sea mask Dimensionless The land cover classes from CORINE that represent land areas are masked with value 1 and land cover classes that represent water surfaces are masked as NaN. Relative humidity % Relation between actual humidity and saturation humidity at 2m height. Values are in the interval [0,100]: 0% means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. Relative humidity % Relation between actual humidity and saturation humidity at 2m height. Values are in the interval [0,100]: 0% means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. Rural-urban mask Dimensionless The land cover classes from CORINE that represent rural areas are masked with value 1 and land cover classes that represent urban areas are masked as NaN. Rural-urban mask Dimensionless The land cover classes from CORINE that represent rural areas are masked with value 1 and land cover classes that represent urban areas are masked as NaN. Specific humidity kg kg-1 Mass of water vapour in a unit mass of moist air at 2m height. Specific humidity kg kg-1 Mass of water vapour in a unit mass of moist air at 2m height. Wind speed m s-1 Wind speed valid for a grid cell at the height of 2m above the surface. It is computed from both the zonal (u) and the meridional (v) wind components by sqrt(u2 + v2 ). Wind speed m s-1 Wind speed valid for a grid cell at the height of 2m above the surface. It is computed from both the zonal (u) and the meridional (v) wind components by sqrt(u2 + v2 ). 5 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/derived-utci-historical https://cds.climate.copernicus.eu/cdsapp#!/dataset/derived-utci-historical derived-utci-historical This dataset provides a complete historical reconstruction for a set of indices representing human thermal stress and discomfort in outdoor conditions. This dataset, also known as ERA5-HEAT (Human thErmAl comforT) represents the current state-of-the-art for bioclimatology data record production. The dataset is organised around two main variables: the mean radiant temperature (MRT) the universal thermal climate index (UTCI) the mean radiant temperature (MRT) the universal thermal climate index (UTCI) These variables describe how the human body experiences atmospheric conditions, specifically air temperature, humidity, ventilation and radiation. The dataset is computed using the ERA5 reanalysis from the European Centre for Medium-Range Forecasts (ECMWF). ERA5 combines model data with observations from across the world to provide a globally complete and consistent description of the Earth’s climate and its evolution in recent decades. ERA5 is regarded as a good proxy for observed atmospheric conditions. The dataset currently covers 01/01/1940 to near real time and is regularly extended as ERA5 data become available. The dataset is produced by the European Centre for Medium-range Weather Forecasts. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal resolution 0.25° x 0.25° Vertical resolution Surface level Temporal coverage From January 1940 to near real time for the most recent version. Temporal resolution Hourly data File format NetCDF Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions v1.1 (latest), v1.0 (deprecated). A new version is expected to be released in early 2023. This new version uses the new thermofeel library. The data will be made available in GRIB format. Update frequency Intermediate dataset updated daily in near real time. Consolidated dataset monthly updates with 2-3 month delay behind real time. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal resolution 0.25° x 0.25° Horizontal resolution 0.25° x 0.25° Vertical resolution Surface level Vertical resolution Surface level Temporal coverage From January 1940 to near real time for the most recent version. Temporal coverage From January 1940 to near real time for the most recent version. Temporal resolution Hourly data Temporal resolution Hourly data File format NetCDF File format NetCDF Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions v1.1 (latest), v1.0 (deprecated). A new version is expected to be released in early 2023. This new version uses the new thermofeel library. The data will be made available in GRIB format. Versions v1.1 (latest), v1.0 (deprecated). A new version is expected to be released in early 2023. This new version uses the new thermofeel library. The data will be made available in GRIB format. Update frequency Intermediate dataset updated daily in near real time. Consolidated dataset monthly updates with 2-3 month delay behind real time. Update frequency Intermediate dataset updated daily in near real time. Consolidated dataset monthly updates with 2-3 month delay behind real time. MAIN VARIABLES Name Units Description Mean radiant temperature K The mean radiant temperature, in relation to a given person placed in a given environment, in a given body posture and clothing, is defined as that uniform temperature of a fictive black-body radiation enclosure (emission coefficient ε = 1) which would result in the same net radiation energy exchange with the subject as the actual, more complex radiation environment. Universal thermal climate index K For any combination of air temperature, wind, radiation and humidity, the Universal thermal climate index is defined as the air temperature of a reference outdoor environment that would elicit in the human body the same physiological model’s response (sweat production, shivering, skin wettedness, skin blood flow and rectal, mean skin and face temperatures) as the actual environment. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Mean radiant temperature K The mean radiant temperature, in relation to a given person placed in a given environment, in a given body posture and clothing, is defined as that uniform temperature of a fictive black-body radiation enclosure (emission coefficient ε = 1) which would result in the same net radiation energy exchange with the subject as the actual, more complex radiation environment. Mean radiant temperature K The mean radiant temperature, in relation to a given person placed in a given environment, in a given body posture and clothing, is defined as that uniform temperature of a fictive black-body radiation enclosure (emission coefficient ε = 1) which would result in the same net radiation energy exchange with the subject as the actual, more complex radiation environment. Universal thermal climate index K For any combination of air temperature, wind, radiation and humidity, the Universal thermal climate index is defined as the air temperature of a reference outdoor environment that would elicit in the human body the same physiological model’s response (sweat production, shivering, skin wettedness, skin blood flow and rectal, mean skin and face temperatures) as the actual environment. Universal thermal climate index K For any combination of air temperature, wind, radiation and humidity, the Universal thermal climate index is defined as the air temperature of a reference outdoor environment that would elicit in the human body the same physiological model’s response (sweat production, shivering, skin wettedness, skin blood flow and rectal, mean skin and face temperatures) as the actual environment. 6 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-biodiversity-thermal-suitability-fish https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-biodiversity-thermal-suitability-fish app-biodiversity-thermal-suitability-fish This application explores the thermal suitability for selected fish species habitats over the world’s oceans and inner seas, based on CMIP5 climate projections (bias-adjusted to ERA5) from 10 Global Circulation Models. The interactive maps show habitat-specific thermal suitability averages alongside sea surface temperature for 20-year time frames (1981-2000 (historical), 2041-2060 (near future), 2061-2080 (mid future), 2081-2100 (far future)) for two climate scenarios: the Representative Concentration Pathways RCP4.5 (moderate scenario) and RCP8.5 (pessimistic scenario) at standard resolution (0.5° x 0.5° ~ 50km x 50km). Thermal suitability for hosting the habitat of selected fish species is calculated based on species-specific thermal climate envelopes (i.e. species preferences in terms of SST) originating from the Aquamaps habitat models (Kesner-Reyes et al., 2012) - see documentation for more information. You can use this application to visualise thermal suitability for 14 widely commercially relevant fish species and sea surface temperature based on annual, summer (June, July and August) and winter (December, January, February) means. With the selection of a marine and coastal ecoregion (The International Council for the Exploration of the Seas (ICES) European ecoregions and the Marine Ecoregion of the World (MEOW) global coastal ecoregions) you can view a time series of the selected variable for the period 1981-2100 for both RCPs. Once the species selection has been made, the thermal suitability map also provides the historical suitable thermal regions (as a red dotted line) for that species, allowing comparison between future reference periods and historical period in a single map. A suitability cut-off value of 0.5 has been chosen as a representative threshold to compare suitable and unsuitable regions. User-selectable parameters User-selectable parameters Season: select the season of interest for exploring sea surface temperatures and associated fish habitat suitability. Regions: select the ocean regions to explore for fish habitat suitability. Must be one of: Marine Ecoregions of the World (MEOW) - global International Council for the Exploration of the Seas (ICES) - Europe only Variable: select the data visualised in the interactive map - either sea surface temperature or thermal suitability. Fish species: select the species of fish for which to explore habitat suitability. Season: select the season of interest for exploring sea surface temperatures and associated fish habitat suitability. Regions: select the ocean regions to explore for fish habitat suitability. Must be one of: Marine Ecoregions of the World (MEOW) - global International Council for the Exploration of the Seas (ICES) - Europe only Marine Ecoregions of the World (MEOW) - global International Council for the Exploration of the Seas (ICES) - Europe only Marine Ecoregions of the World (MEOW) - global International Council for the Exploration of the Seas (ICES) - Europe only Variable: select the data visualised in the interactive map - either sea surface temperature or thermal suitability. Fish species: select the species of fish for which to explore habitat suitability. INPUT VARIABLES Name Units Description Source Sea surface temperature K Temperature of sea water near the surface. Bioclimatic indicators INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Sea surface temperature K Temperature of sea water near the surface. Bioclimatic indicators Sea surface temperature K Temperature of sea water near the surface. Bioclimatic indicators Bioclimatic indicators OUTPUT VARIABLES Name Units Description Thermal suitability for fish species habitat Dimensionless The suitability of the sea surface temperature for the habitat of various fish species on a 0-1 scale (0 completely unsuitable, 1 completely suitable). Derived from species-specific thermal climate envelopes originating from the Aquamaps habitat models (Kesner-Reyes et al., 2012) - see documentation for more information. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Thermal suitability for fish species habitat Dimensionless The suitability of the sea surface temperature for the habitat of various fish species on a 0-1 scale (0 completely unsuitable, 1 completely suitable). Derived from species-specific thermal climate envelopes originating from the Aquamaps habitat models (Kesner-Reyes et al., 2012) - see documentation for more information. Thermal suitability for fish species habitat Dimensionless The suitability of the sea surface temperature for the habitat of various fish species on a 0-1 scale (0 completely unsuitable, 1 completely suitable). Derived from species-specific thermal climate envelopes originating from the Aquamaps habitat models (Kesner-Reyes et al., 2012) - see documentation for more information. 7 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/iberia-biscay-ireland-sea-surface-temperature-extreme http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_VAR_EXTREME_SST_IBI_sst_mean_and_anomaly_obs Iberia Biscay Ireland Sea Surface Temperature extreme from Observations Reprocessing DEFINITION The OMI_VAR_EXTREME_SST_IBI_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). CONTEXT Sea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years. An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial. The Iberia Biscay Ireland area is characterized by a great complexity in terms of processes that take place in it. The sea surface temperature varies depending on the latitude with higher values to the South. In this area, the clear warming trend observed in other European Seas is not so evident. The northwest part is influenced by the refreshing trend in the North Atlantic, and a mild warming trend has been observed in the last decade (Pisano et al. 2020). CMEMS KEY FINDINGS The mean 99th percentiles showed in the area present a range from 16-17ºC in the Southwest of the British Isles and English Channel, 19ºC in the West of Galician Coast, 21-23ºC in the south of Bay of Biscay, 23.5ºC in the Gulf of Cadiz to 24ºC in the Canary Island. The standard deviations are between 0.4ºC and 0.8ºC. Results for this year show a general slight positive anomaly in the South of the British Isles (+0.2/+0.8ºC). In the North of Spain, the anomaly is slightly or moderately positive between 0.3ºC and +1.2ºC. In the Gulf of Cadiz the anomaly is also positive (+0.9ºC) and in Canary Island is close to zero. DOI (product):https://doi.org/10.48670/moi-00255 https://doi.org/10.48670/moi-00255 8 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/urban-atlas-land-coverland-use-2006-vector-europe-6 https://land.copernicus.eu/local/urban-atlas/urban-atlas-2006/view Urban Atlas Land Cover/Land Use 2006 (vector), Europe, 6-yearly, Jul. 2015 The European Urban Atlas provides reliable, inter-comparable, high-resolution land use maps for 305 Large Urban Zones and their surroundings (more than 100.000 inhabitants as defined by the Urban Audit) for the reference year 2006 in EU member states. The GIS data can be downloaded together with a map for each covered urban area and a report with the metadata for the respective area. Additional information (product description, mapping guidance and class description) can be found at https://www.eea.europa.eu/data-and-maps/data/urban-atlas/#tab-methodolo…. https://www.eea.europa.eu/data-and-maps/data/urban-atlas/#tab-methodolo… Urban Atlas is a joint initiative of the European Commission Directorate-General for Regional and Urban Policy and the Directorate-General for Enterprise and Industry in the frame of the EU Copernicus programme, with the support of the European Space Agency and the European Environment Agency. 9 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/urban-atlas-land-coverland-use-2012-vector-europe-6 https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012/view Urban Atlas Land Cover/Land Use 2012 (vector), Europe, 6-yearly, Jan. 2021 The European Urban Atlas provides reliable, inter-comparable, high-resolution land use and land cover data for 785 Functional Urban Area (FUA) for the 2012 reference year in EEA38 countries (EU, EFTA and Western Balkan countries as well as Türkiye) and United Kingdom. The spatial data can be downloaded together with a map for each FUA covered and a report with the metadata for the respective area. Urban Atlas is a joint initiative of the Commission Directorate-General for Regional and Urban Policy and the Directorate-General for Defence Industry and Space (DEFIS) in the frame of the EU Copernicus programme, with the support of the European Space Agency and the European Environment Agency. 10 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-health-heat-waves-projections https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-health-heat-waves-projections app-health-heat-waves-projections This application is an exploratory tool for the Heat waves and cold spells in Europe derived from climate projections, which is based on bias adjusted output from the EURO-CORDEX ensemble of climate models. Heat waves and cold spells in Europe derived from climate projections A heat wave is a prolonged period of high temperature, relative to the region. A number of qualifying definitions of heat waves are used in the climate and health communities. This application, and underlying dataset, presents the following heat-wave defintions: Climatological EURO-CORDEX: a period of at least three consecutive days on which the daily maximum temperature exceeds the 99th percentile of the daily maximum temperatures of the May to September months for the control period of 1971 to 2000. Euroheat project: the maximum apparent temperature exceeds the threshold (90th percentile of maximum apparent temperature for each month) and the minimum temperature exceeds its threshold (90th percentile of minimum temperature for each month) for at least two days over the summer period of June to August. National definitions: 8 member states and 1 former member state of the European Union have provided heat wave definitions which are applied to the EURO-CORDEX data within the national boundary of the nation's definition. For a full description of the national definitions please refer to the documentation. Climatological EURO-CORDEX: a period of at least three consecutive days on which the daily maximum temperature exceeds the 99th percentile of the daily maximum temperatures of the May to September months for the control period of 1971 to 2000. Climatological EURO-CORDEX: a period of at least three consecutive days on which the daily maximum temperature exceeds the 99th percentile of the daily maximum temperatures of the May to September months for the control period of 1971 to 2000. Climatological EURO-CORDEX Euroheat project: the maximum apparent temperature exceeds the threshold (90th percentile of maximum apparent temperature for each month) and the minimum temperature exceeds its threshold (90th percentile of minimum temperature for each month) for at least two days over the summer period of June to August. Euroheat project: the maximum apparent temperature exceeds the threshold (90th percentile of maximum apparent temperature for each month) and the minimum temperature exceeds its threshold (90th percentile of minimum temperature for each month) for at least two days over the summer period of June to August. Euroheat project National definitions: 8 member states and 1 former member state of the European Union have provided heat wave definitions which are applied to the EURO-CORDEX data within the national boundary of the nation's definition. For a full description of the national definitions please refer to the documentation. National definitions: 8 member states and 1 former member state of the European Union have provided heat wave definitions which are applied to the EURO-CORDEX data within the national boundary of the nation's definition. For a full description of the national definitions please refer to the documentation. National definitions Users can select the representative concentration pathway to view and use the time-slider to see how the number of heat wave days is predicted to change throughout the 21st Century. The interactive map data has been averaged on to the NUTS level 3 administrative regions. By clicking on a country or region it is possible to view time-series of the number of heat wave days for that country/region. Zoom in/out to click on smaller/larger regions. INPUT VARIABLES Name Units Description Source Heat wave days days Number of heat wave days in a year for given definitions: climatological EURO-CORDEX, Euroheat project & national definitions. Heat wave and cold spell projections INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Heat wave days days Number of heat wave days in a year for given definitions: climatological EURO-CORDEX, Euroheat project & national definitions. Heat wave and cold spell projections Heat wave days days Number of heat wave days in a year for given definitions: climatological EURO-CORDEX, Euroheat project & national definitions. Heat wave and cold spell projections Heat wave and cold spell projections 11 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-utci-explorer https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-utci-explorer app-utci-explorer The application presents the average of daily maximum, average and minimum values of the Universal Thermal Climate Index (UTCI) for any given month and location, in the period from 1979 to 2020. The UTCI is calculated from the combination of air temperature, humidity, ventilation and radiation. By applying a set of values of those variables, to human heat balance models, the index provides what would be the equivalent temperature that a human would feel, being in a reference environment defined by standard values of those same variables. It expresses therefore the thermal stress level on a human body, given a set of atmospheric conditions. The input data comes from the ERA5 reanalysis from the European Centre for Medium-Range Forecast (ECMWF). It combines model data with observations from across the world to provide a globally complete and consistent description of the Earth’s climate and its evolution in recent decades. The interactive live map shows the global Universal Thermal Climate index for the selected year and month in the period from 1979 to 2020. Users can view the monthly maximum, average and minimum UTCI value by selecting the year and month of interest in the upper right of the map window. By clicking on the live map or searching a location in the provided search bar, a window will open on the right side of the map. This new window contains a time series of the selected location with the UTCI: Average of daily maximum Average of daily value Average daily minimum Average of daily maximum Average of daily value Average daily minimum Users can select which statistics to view by clicking on them in the legend. INPUT VARIABLES Name Units Description Source Universal thermal climate index K For any combination of air temperature, wind, radiation and humidity, the Universal thermal climate index is defined as the air temperature of a reference outdoor environment that would elicit in the human body the same physiological model’s response (sweat production, shivering, skin wettedness, skin blood flow and rectal, mean skin and face temperatures) as the actual environment. Thermal comfort indices derived from ERA5 reanalysis INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Universal thermal climate index K For any combination of air temperature, wind, radiation and humidity, the Universal thermal climate index is defined as the air temperature of a reference outdoor environment that would elicit in the human body the same physiological model’s response (sweat production, shivering, skin wettedness, skin blood flow and rectal, mean skin and face temperatures) as the actual environment. Thermal comfort indices derived from ERA5 reanalysis Universal thermal climate index K For any combination of air temperature, wind, radiation and humidity, the Universal thermal climate index is defined as the air temperature of a reference outdoor environment that would elicit in the human body the same physiological model’s response (sweat production, shivering, skin wettedness, skin blood flow and rectal, mean skin and face temperatures) as the actual environment. Thermal comfort indices derived from ERA5 reanalysis Thermal comfort indices derived from ERA5 reanalysis OUTPUT VARIABLES Name Units Description Thermal sensation indices °C The monthly mean, maximum and minimum thermal sensation indices for each calendar month at the selected year. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Thermal sensation indices °C The monthly mean, maximum and minimum thermal sensation indices for each calendar month at the selected year. Thermal sensation indices °C The monthly mean, maximum and minimum thermal sensation indices for each calendar month at the selected year. 12 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-health-heat-waves-current-climate https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-health-heat-waves-current-climate app-health-heat-waves-current-climate This application allows users to explore the number of heat waves in European countries based on the ERA5 hourly data on single levels dataset. A heat wave is a prolonged period of high temperature, relative to the region. A number of qualifying definitions of heat waves are used in the climate and health communities. This application, and underlying dataset, presents the following heat-wave defintions: Climatological EURO-CORDEX: a period of at least three consecutive days on which the daily maximum temperature exceeds the 99th percentile of the daily maximum temperatures of the May to September months for the control period of 1971 to 2000. Euroheat project: the maximum apparent temperature exceeds the threshold (90th percentile of maximum apparent temperature for each month) and the minimum temperature exceeds its threshold (90th percentile of minimum temperature for each month) for at least two days over the summer period of June to August. National definitions: 8 member states and 1 former member state of the European Union have provided heat wave definitions which are applied to the EURO-CORDEX data within the national boundary of the nation's definition. For a full description of the national definitions please refer to the documentation. eat project , and one set of National definitions which are available for a limited number of European countries. Climatological EURO-CORDEX: a period of at least three consecutive days on which the daily maximum temperature exceeds the 99th percentile of the daily maximum temperatures of the May to September months for the control period of 1971 to 2000. Climatological EURO-CORDEX: a period of at least three consecutive days on which the daily maximum temperature exceeds the 99th percentile of the daily maximum temperatures of the May to September months for the control period of 1971 to 2000. Climatological EURO-CORDEX Euroheat project: the maximum apparent temperature exceeds the threshold (90th percentile of maximum apparent temperature for each month) and the minimum temperature exceeds its threshold (90th percentile of minimum temperature for each month) for at least two days over the summer period of June to August. Euroheat project: the maximum apparent temperature exceeds the threshold (90th percentile of maximum apparent temperature for each month) and the minimum temperature exceeds its threshold (90th percentile of minimum temperature for each month) for at least two days over the summer period of June to August. Euroheat project National definitions: 8 member states and 1 former member state of the European Union have provided heat wave definitions which are applied to the EURO-CORDEX data within the national boundary of the nation's definition. For a full description of the national definitions please refer to the documentation. eat project , and one set of National definitions which are available for a limited number of European countries. National definitions: 8 member states and 1 former member state of the European Union have provided heat wave definitions which are applied to the EURO-CORDEX data within the national boundary of the nation's definition. For a full description of the national definitions please refer to the documentation. eat project , and one set of National definitions which are available for a limited number of European countries. National definitions The time-slider allows users to see how the number of heat wave days has changed from 1979 to 2022. The interactive map data has been averaged on to the NUTS level 3 administrative regions. By clicking on a country or region it is possible to view time-series of the number of heat wave days for that country/region. Zoom in/out to click on smaller/larger regions. INPUT VARIABLES Name Units Description Source Air temperature K The temperature of air at 2m above the surface of land, sea or inland waters. ERA5 Dew-point temperature K The temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. ERA5 INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Air temperature K The temperature of air at 2m above the surface of land, sea or inland waters. ERA5 Air temperature K The temperature of air at 2m above the surface of land, sea or inland waters. ERA5 ERA5 Dew-point temperature K The temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. ERA5 Dew-point temperature K The temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. ERA5 ERA5 OUTPUT VARIABLES Name Units Description Heat wave days days Number of heat wave days. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Heat wave days days Number of heat wave days. Heat wave days days Number of heat wave days. 13 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mean-heat-transport-across-sections-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_WMHE_heattrp Mean Heat Transport across sections from Reanalysis DEFINITION Heat transport across lines are obtained by integrating the heat fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models’ daily output. The mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW). CONTEXT The ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth’s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth’s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. CMEMS KEY FINDINGS The mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00245 https://doi.org/10.48670/moi-00245 14 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/urban-atlas-street-tree-layer-2018-vector-europe-6-yearly https://land.copernicus.eu/local/urban-atlas/street-tree-layer-stl-2018 Urban Atlas Street Tree Layer 2018 (vector), Europe, 6-yearly, Feb. 2021 The Urban Atlas provides pan-European comparable land use and land cover data for Functional Urban Areas (FUA) across EEA38 countries (EU, EFTA, Western Balkan countries as well as Türkiye) and United Kingdom. The Street Tree Layer (STL) is a separate layer from the Urban Atlas 2018 LU/LC layer produced within the level 1 urban mask for each FUA. It includes contiguous rows or a patches of trees covering 500 m² or more and with a minimum width of 10 meter over "Artificial surfaces" (nomenclature class 1) inside FUA (i.e. rows of trees along the road network outside urban areas or forest adjacent to urban areas should not be included). Urban Atlas is a joint initiative of the European Commission Directorate-General for Regional and Urban Policy and the Directorate-General for Defence Industry and Space in the frame of the EU Copernicus programme, with the support of the European Space Agency and the European Environment Agency. NOTE: By the time of publishing this metadata not all FUAs were available for download through the Copernicus Land Monitoring Service website. The last FUAs were added in February 2021. 15 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-heat-and-cold-spells https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-heat-and-cold-spells sis-heat-and-cold-spells The dataset contains the number of hot and cold spell days using different European-wide and national/regional definitions developed within the C3S European Health service. These heat wave and cold spell days are available for different future time periods and use different climate change scenarios. A heat wave or cold spell is a prolonged period of extremely high or extremely low temperature for a particular region. However, there is a lack of rigorous definitions for heat waves and cold spells. This dataset combines multiple definitions and allows the user to compare European-wide definitions with national/regional definitions. First, the temperature statistics are calculated, either for the season winter and summer or for the whole year, based on a bias-adjusted EURO-CORDEX dataset. Then, the statistics are averaged for 30 years as a smoothed average from 1971 to 2100. This results in a timeseries covering the period from 1986 to 2085. Finally, the timeseries are averaged for the model ensemble and the standard deviation to this ensemble mean is provided. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid. Horizontal coverage European region (Extent: ~ 27N – 72N, ~22W – 45E) Horizontal resolution 0.1° x 0.1° Temporal coverage From 1986 to 2085 Temporal resolution Year (season average) File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency No updates expected. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid. Projection Regular latitude-longitude grid. Horizontal coverage European region (Extent: ~ 27N – 72N, ~22W – 45E) Horizontal coverage European region (Extent: ~ 27N – 72N, ~22W – 45E) Horizontal resolution 0.1° x 0.1° Horizontal resolution 0.1° x 0.1° Temporal coverage From 1986 to 2085 Temporal coverage From 1986 to 2085 Temporal resolution Year (season average) Temporal resolution Year (season average) File format NetCDF 4 File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency No updates expected. Update frequency No updates expected. MAIN VARIABLES Name Units Description Cold spell days days Number of cold days in a year using specific definitions. Heat wave days days Number of hot days in a year using specific definitions. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Cold spell days days Number of cold days in a year using specific definitions. Cold spell days days Number of cold days in a year using specific definitions. Heat wave days days Number of hot days in a year using specific definitions. Heat wave days days Number of hot days in a year using specific definitions. 16 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/urban-atlas-land-coverland-use-change-2012-2018-vector https://land.copernicus.eu/local/urban-atlas/urban-atlas-change-2012-2018 Urban Atlas Land Cover/Land Use Change 2012-2018 (vector), Europe, 6-yearly, Jan. 2021 The European Urban Atlas provides reliable, inter-comparable, high-resolution land use and land cover change data for 785 Functional Urban Areas (FUA) with more than 50,000 inhabitants between the reference years 2012 and 2018 in EEA38 countries (EU, EFTA, Western Balkan countries as well as Türkiye) and United Kingdom. The spatial data can be downloaded together with a map for each FUA covered and a report with the metadata for the respective area. The Urban Atlas Change layers have become available as of 2012. Urban Atlas is a joint initiative of the European Commission Directorate-General for Regional and Urban Policy and the Directorate-General for Defence Industry and Space in the frame of the EU Copernicus programme, with the support of the European Space Agency and the European Environment Agency. NOTE: By the time of publishing this metadata not all the FUAs were available through the Copernicus Land Service website. The last FUAs were added in January 2021. 17 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/urban-atlas-street-tree-layer-2012-vector-europe-6-yearly https://land.copernicus.eu/local/urban-atlas/street-tree-layer-stl/view Urban Atlas Street Tree Layer 2012 (vector), Europe, 6-yearly, May 2016 The Urban Atlas provides pan-European comparable land use and land cover data for Functional Urban Areas (FUA). The Street Tree Layer (STL) is a separate layer from the Urban Atlas 2012 LU/LC layer produced within the level 1 urban mask for each FUA. It includes contiguous rows or a patches of trees covering 500 m² or more and with a minimum width of 10 meter over "Artificial surfaces" (nomenclature class 1) inside FUA (i.e. rows of trees along the road network outside urban areas or forest adjacent to urban areas should not be included). Urban Atlas is a joint initiative of the European Commission Directorate-General for Regional and Urban Policy and the Directorate-General for Enterprise and Industry in the frame of the EU Copernicus programme, with the support of the European Space Agency and the European Environment Agency. 18 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-mean-temperature-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-mean-temperature-v2 ecde-app-mean-temperature-v2 Temperature is an essential climate variable making the Daily Mean Temperature a fundamental index for following climate variability and change. The “Mean Temperature Index” application provides pan-European information relevant to mean daily temperature in the present and in the future until the end of the century. It allows exploring Index changes from European down to sub-national administrative (NUTS0 to NUTS2) or transnational regions for two emission scenarios (RCP 4.5 and RCP8.5). The application interface is made of two parts, on the left a set of drop down menus with an interactive map and on the right a panel displaying plots corresponding to time series and spatial/temporal averages over the region selected on the interactive map. The user can interact with the application in two ways: by selecting visualisation options on the set of drop-down menus and by selecting a region on the interactive map and a time period on the time slider. The drop-down menu allows to: select the region, the time span, and the emission scenario. The interactive map allows to: zoom in and out, select a region by clicking on it to display the corresponding plots on the right panel, and switch between 30-year periods that cover past (1981-2010), near future (2011-2040), mid future (2041-2070) and far future (2071-2100) conditions. There are 4 plots in the right hand side panel that show trends and projections for the selected administrative region: historical anomaly time series, projected annual values, projected 30-year trend, and projected climatology. The atmospheric variable used to compute the indicator is the daily mean surface temperature. It is estimated using 3-hourly data from reanalysis (ERA5), for coherency with the climate projections data that are also 3-hourly. The climate projections dataset used in this application is a set of bias-corrected EURO-CORDEX projections available from the CDS. It is composed of 9 GCM-RCM simulations at 0.25° x 0.25° spatial resolution, 3-hourly temporal resolution for each of the two emission scenarios: RCP4.5 and RCP8.5. The dataset is the Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections. More technical details can be found in the dataset documentation. User-selectable parameters User-selectable parameters Region: the type of administrative region the user is interested in which can be “NUTS” region, “Transnational regions” or “Europe Zones” Time span: a time span which can be “Year”, “Season” or “Month” Season / Month: a specific season (winter, spring, summer or autumn) or month depending on the selected time span Scenario: a climate projection scenario: “RCP4.5” or “RCP8.5” Region: the type of administrative region the user is interested in which can be “NUTS” region, “Transnational regions” or “Europe Zones” Time span: a time span which can be “Year”, “Season” or “Month” Season / Month: a specific season (winter, spring, summer or autumn) or month depending on the selected time span Scenario: a climate projection scenario: “RCP4.5” or “RCP8.5” INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 19 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-mean-temperature https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-mean-temperature ecde-app-mean-temperature Temperature is an essential climate variable making the Daily Mean Temperature a fundamental index for following climate variability and change. The “Mean Temperature Index” application provides pan-European information relevant to mean daily temperature in the present and in the future until the end of the century. It allows exploring Index changes from European down to sub-national administrative (NUTS0 to NUTS2) or transnational regions for two emission scenarios (RCP 4.5 and RCP8.5). The application interface is made of two parts, on the left a set of drop down menus with an interactive map and on the right a panel displaying plots corresponding to time series and spatial/temporal averages over the region selected on the interactive map. The user can interact with the application in two ways: by selecting visualisation options on the set of drop-down menus and by selecting a region on the interactive map and a time period on the time slider. The drop-down menu allows to: select the region, the time span, and the emission scenario. The interactive map allows to: zoom in and out, select a region by clicking on it to display the corresponding plots on the right panel, and switch between 30-year periods that cover past (1981-2010), near future (2011-2040), mid future (2041-2070) and far future (2071-2100) conditions. There are 4 plots in the right hand side panel that show trends and projections for the selected administrative region: historical anomaly time series, projected annual values, projected 30-year trend, and projected climatology. The atmospheric variable used to compute the indicator is the daily mean surface temperature. It is estimated using 3-hourly data from reanalysis (ERA5), for coherency with the climate projections data that are also 3-hourly. The climate projections dataset used in this application is a set of bias-corrected EURO-CORDEX projections available from the CDS. It is composed of 9 GCM-RCM simulations at 0.25° x 0.25° spatial resolution, 3-hourly temporal resolution for each of the two emission scenarios: RCP4.5 and RCP8.5. The dataset is the Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections. More technical details can be found in the dataset documentation. User-selectable parameters User-selectable parameters Region: the type of administrative region the user is interested in which can be “NUTS” region, “Transnational regions” or “Europe Zones” Time span: a time span which can be “Year”, “Season” or “Month” Season / Month: a specific season (winter, spring, summer or autumn) or month depending on the selected time span Scenario: a climate projection scenario: “RCP4.5” or “RCP8.5” Region: the type of administrative region the user is interested in which can be “NUTS” region, “Transnational regions” or “Europe Zones” Time span: a time span which can be “Year”, “Season” or “Month” Season / Month: a specific season (winter, spring, summer or autumn) or month depending on the selected time span Scenario: a climate projection scenario: “RCP4.5” or “RCP8.5” INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 20 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/iberia-biscay-ireland-significant-wave-height-extreme http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_VAR_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs Iberia Biscay Ireland Significant Wave Height extreme from Observations Reprocessing DEFINITION The OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). CONTEXT Projections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). CMEMS KEY FINDINGS The mean 99th percentiles showed in the area present a wide range from 2-3m in the Canary Island with 0.1-0.3 m of standard deviation (std), 3.5m in the Gulf of Cadiz with 0.5m of std, 4-6m in the English Channel 0.5-0.6m of std, 4-7m in the Bay of Biscay with 0.4-0.9m of std to 8m in the West of the British Isles with 0.7m of std. Results for this year show slight negative anomalies in the Canary Island (-0.1/-0.17m), moderate negative anomaly in the Gulf of Cadiz (-0.7m) and general positive anomaly in the rest of the area, with moderate values in the Bay of Biscay (+0.16/+1.1) and the English Channel (-0.6/+0.8m) and an appreciable positive value in the West of the British Isles over the standard deviation (+1.5m). Severe storms developed in the Atlantic during 2020 reached the West of the British Isles and the Bay of Biscay, like Storm Brendan in January, Storm Dennis in February or Storms Ernesto and Bella in December. These storms produced waves with significant wave height over 9 m recorded by the buoys in the area. DOI (product):https://doi.org/10.48670/moi-00250 https://doi.org/10.48670/moi-00250 21 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-fire-burned-area https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-fire-burned-area satellite-fire-burned-area The Burned Area products provide global information of total burned area (BA) at pixel and grid scale. The BA is identified with the date of first detection of the burned signal in the case of the pixel product, and with the total BA per grid cell in the case of the grid product. The products were obtained through the analysis of reflectance changes from medium resolution sensors (Terra MODIS, Sentinel-3 OLCI), supported by the use of MODIS thermal information. The burned area products also include information related to the land cover that has been burned, which has been extracted from the Copernicus Climate Change Service (C3S) land cover dataset, thus assuring consistency between the datasets. The algorithms for BA retrieval were developed by the University of Alcala (Spain), and processed by Brockmann Consult GmbH (Germany). Different product versions are available. FireCCI v5.0cds and FireCCI v5.1cds were developed as part of the Fire ECV Climate Change Initiative Project (Fire CCI) and brokered to C3S, offering the first global burned area time series at 250m spatial resolution. FireCCI v5.1cds used a more mature algorithm than the previous version. This algorithm was adapted to Sentinel-3 OLCI data to create the C3S v1.0 burned area product, extending the BA database to the present. During July 2020, an error in some files in the version v5.1cds were identified, affecting the files of the grid product of January 2018, and the pixel and grid products of October, November and December 2019. These errors were fixed, and a new version, v5.1.1cds, was created for the whole time series, to replace version v5.1cds. The latter product has been deprecated, but it is temporally kept in the database for transparency and traceability reasons. Only version v5.1.1cds should be used. The BA products are useful for researchers studying climate change, as they provide crucial information on burned biomass, which can be translated to greenhouse gases emissions amongst other contaminants. Burned area is also useful for land cover change studies, fire management and risk analysis. DATA DESCRIPTION Data type Gridded Horizontal coverage Grid product: Global Pixel product: Continents Horizontal resolution Grid product: 0.25° latitude x 0.25° longitude Pixel product: 250m (v5.0cds and v5.1.1cds); 300m (v1.1) Vertical coverage Surface Vertical resolution Single level Temporal coverage From 2001 to 2016 for v5.0cds From 2001 to 2019 for v5.1.1cds From 2017 to present for v1.1 Temporal resolution Grid product: 15 days (v5.0cds); 1 month (v5.1.1cds and v1.1) Pixel product: Month File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 and ESA CCI Data Standards [DSWG 2015] Versions Versions 5.0cds and 5.1.1cds provide data from the European Space Agency Climate Change Initiative Version 1.1 is the first burned area product developed especifically for the Copernicus Climate Change Service. Version 5.1cds and 1.0 have been deprecated, and replaced by versions 5.1.1cds and 1.1, respectively. Versions v5.1cds and 1.0 are kept for traceability, transparency and reproducibility. All versions are produced with the same processing chain. Update frequency Yearly DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Grid product: Global Pixel product: Continents Horizontal coverage Grid product: Global Pixel product: Continents Grid product: Global Pixel product: Continents Horizontal resolution Grid product: 0.25° latitude x 0.25° longitude Pixel product: 250m (v5.0cds and v5.1.1cds); 300m (v1.1) Horizontal resolution Grid product: 0.25° latitude x 0.25° longitude Pixel product: 250m (v5.0cds and v5.1.1cds); 300m (v1.1) Grid product: 0.25° latitude x 0.25° longitude Pixel product: 250m (v5.0cds and v5.1.1cds); 300m (v1.1) Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage From 2001 to 2016 for v5.0cds From 2001 to 2019 for v5.1.1cds From 2017 to present for v1.1 Temporal coverage From 2001 to 2016 for v5.0cds From 2001 to 2019 for v5.1.1cds From 2017 to present for v1.1 From 2001 to 2016 for v5.0cds From 2001 to 2019 for v5.1.1cds From 2017 to present for v1.1 Temporal resolution Grid product: 15 days (v5.0cds); 1 month (v5.1.1cds and v1.1) Pixel product: Month Temporal resolution Grid product: 15 days (v5.0cds); 1 month (v5.1.1cds and v1.1) Pixel product: Month Grid product: 15 days (v5.0cds); 1 month (v5.1.1cds and v1.1) Pixel product: Month File format NetCDF4 File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 and ESA CCI Data Standards [DSWG 2015] Conventions Climate and Forecast (CF) Metadata Convention v1.6 and ESA CCI Data Standards [DSWG 2015] Versions Versions 5.0cds and 5.1.1cds provide data from the European Space Agency Climate Change Initiative Version 1.1 is the first burned area product developed especifically for the Copernicus Climate Change Service. Version 5.1cds and 1.0 have been deprecated, and replaced by versions 5.1.1cds and 1.1, respectively. Versions v5.1cds and 1.0 are kept for traceability, transparency and reproducibility. All versions are produced with the same processing chain. Versions Versions 5.0cds and 5.1.1cds provide data from the European Space Agency Climate Change Initiative Version 1.1 is the first burned area product developed especifically for the Copernicus Climate Change Service. Version 5.1cds and 1.0 have been deprecated, and replaced by versions 5.1.1cds and 1.1, respectively. Versions v5.1cds and 1.0 are kept for traceability, transparency and reproducibility. All versions are produced with the same processing chain. Versions 5.0cds and 5.1.1cds provide data from the European Space Agency Climate Change Initiative Version 1.1 is the first burned area product developed especifically for the Copernicus Climate Change Service. Version 5.1cds and 1.0 have been deprecated, and replaced by versions 5.1.1cds and 1.1, respectively. Versions v5.1cds and 1.0 are kept for traceability, transparency and reproducibility. All versions are produced with the same processing chain. Update frequency Yearly Update frequency Yearly MAIN VARIABLES Name Units Description Burned area (Grid product) m2 Total burned area within each pixel in the 15-days or monthly period. Burned area in vegetation class (Grid product) m2 Sum of burned area by land cover classes; land cover classes are from CCI Land Cover (for version 5.0cds) or C3S Land Cover (for the rest of the versions). Confidence level (Pixel product) % Probability of detecting a pixel as burned. Possible values: 0 when the pixel is not observed in the month, or it is not burnable; 1 to 100 probability values when the pixel was observed. The closer to 100, the higher the confidence that the pixel is actually burned. Flag of pixel detection (Pixel product) Dimensionless. Day in which the fire was first detected. Possible values: 0 if the pixel is not burned; 1 to 366 day of the first detection when the pixel is burned; -1 when the pixel is not observed in the month; -2 when pixel is not burnable: water bodies, bare areas, urban areas and permanent snow and ice. Fraction of burnable area (Grid product) Dimensionless The fraction of burnable area is the fraction of the cell that corresponds to vegetated land covers that could burn. The land cover classes are those from CCI Land Cover for version 5.0cds or C3S Land Cover for the rest of the versions. Fraction of observed area (Grid product) Dimensionless The fraction of the total burnable area in the cell that was observed during the time interval and was not marked as unsuitable/not observable. The latter refers to the area where it was not possible to obtain observational burned area information for the whole time interval because of lack of input data (non-existing images for that location and period), cloud cover, haze or pixels that fell below the quality thresholds of the algorithm. Land cover of burned pixels (Pixel product) Dimensionless Land cover of the burned pixel, extracted from the CCI LandCover v1.6.1 for version 5.0cds or C3S Land Cover for the rest of the versions. Possible values: 0 when the pixel is not burned in the month, either because it was observed and not classified as burned, or because it is non burnable or was not observed; 10 to 180: land cover code when the pixel is burned Number of patches (Grid product) Dimensionless Number of contiguous groups of burned pixels. Standard error (Grid product) m2 Error on the estimation of burned area in each grid cell, based on the aggregation of the confidence level of the pixel product. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Burned area (Grid product) m2 Total burned area within each pixel in the 15-days or monthly period. Burned area (Grid product) m2 Total burned area within each pixel in the 15-days or monthly period. Burned area in vegetation class (Grid product) m2 Sum of burned area by land cover classes; land cover classes are from CCI Land Cover (for version 5.0cds) or C3S Land Cover (for the rest of the versions). Burned area in vegetation class (Grid product) m2 Sum of burned area by land cover classes; land cover classes are from CCI Land Cover (for version 5.0cds) or C3S Land Cover (for the rest of the versions). Confidence level (Pixel product) % Probability of detecting a pixel as burned. Possible values: 0 when the pixel is not observed in the month, or it is not burnable; 1 to 100 probability values when the pixel was observed. The closer to 100, the higher the confidence that the pixel is actually burned. Confidence level (Pixel product) % Probability of detecting a pixel as burned. Possible values: 0 when the pixel is not observed in the month, or it is not burnable; 1 to 100 probability values when the pixel was observed. The closer to 100, the higher the confidence that the pixel is actually burned. Flag of pixel detection (Pixel product) Dimensionless. Day in which the fire was first detected. Possible values: 0 if the pixel is not burned; 1 to 366 day of the first detection when the pixel is burned; -1 when the pixel is not observed in the month; -2 when pixel is not burnable: water bodies, bare areas, urban areas and permanent snow and ice. Flag of pixel detection (Pixel product) Dimensionless. Day in which the fire was first detected. Possible values: 0 if the pixel is not burned; 1 to 366 day of the first detection when the pixel is burned; -1 when the pixel is not observed in the month; -2 when pixel is not burnable: water bodies, bare areas, urban areas and permanent snow and ice. Fraction of burnable area (Grid product) Dimensionless The fraction of burnable area is the fraction of the cell that corresponds to vegetated land covers that could burn. The land cover classes are those from CCI Land Cover for version 5.0cds or C3S Land Cover for the rest of the versions. Fraction of burnable area (Grid product) Dimensionless The fraction of burnable area is the fraction of the cell that corresponds to vegetated land covers that could burn. The land cover classes are those from CCI Land Cover for version 5.0cds or C3S Land Cover for the rest of the versions. Fraction of observed area (Grid product) Dimensionless The fraction of the total burnable area in the cell that was observed during the time interval and was not marked as unsuitable/not observable. The latter refers to the area where it was not possible to obtain observational burned area information for the whole time interval because of lack of input data (non-existing images for that location and period), cloud cover, haze or pixels that fell below the quality thresholds of the algorithm. Fraction of observed area (Grid product) Dimensionless The fraction of the total burnable area in the cell that was observed during the time interval and was not marked as unsuitable/not observable. The latter refers to the area where it was not possible to obtain observational burned area information for the whole time interval because of lack of input data (non-existing images for that location and period), cloud cover, haze or pixels that fell below the quality thresholds of the algorithm. Land cover of burned pixels (Pixel product) Dimensionless Land cover of the burned pixel, extracted from the CCI LandCover v1.6.1 for version 5.0cds or C3S Land Cover for the rest of the versions. Possible values: 0 when the pixel is not burned in the month, either because it was observed and not classified as burned, or because it is non burnable or was not observed; 10 to 180: land cover code when the pixel is burned Land cover of burned pixels (Pixel product) Dimensionless Land cover of the burned pixel, extracted from the CCI LandCover v1.6.1 for version 5.0cds or C3S Land Cover for the rest of the versions. Possible values: 0 when the pixel is not burned in the month, either because it was observed and not classified as burned, or because it is non burnable or was not observed; 10 to 180: land cover code when the pixel is burned Number of patches (Grid product) Dimensionless Number of contiguous groups of burned pixels. Number of patches (Grid product) Dimensionless Number of contiguous groups of burned pixels. Standard error (Grid product) m2 Error on the estimation of burned area in each grid cell, based on the aggregation of the confidence level of the pixel product. Standard error (Grid product) m2 Error on the estimation of burned area in each grid cell, based on the aggregation of the confidence level of the pixel product. 22 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-sea-surface-temperature-trend-map http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_TEMPSAL_sst_trend Global Ocean Sea Surface Temperature trend map from Observations Reprocessing DEFINITION Based on daily, global climate sea surface temperature (SST) analyses generated by the European Space Agency (ESA) SST Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) (Merchant et al., 2019; product SST-GLO-SST-L4-REP-OBSERVATIONS-010-024). Analysis of the data was based on the approach described in Mulet et al. (2018) and is described and discussed in Good et al. (2020). The processing steps applied were: 1. The daily analyses were averaged to create monthly means. 2. A climatology was calculated by averaging the monthly means over the period 1993 - 2014. 3. Monthly anomalies were calculated by differencing the monthly means and the climatology. 4. The time series for each grid cell was passed through the X11 seasonal adjustment procedure, which decomposes a time series into a residual seasonal component, a trend component and errors (e.g., Pezzulli et al., 2005). The trend component is a filtered version of the monthly time series. 5. The slope of the trend component was calculated using a robust method (Sen 1968). The method also calculates the 95% confidence range in the slope. CONTEXT Sea surface temperature (SST) is one of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS) as being needed for monitoring and characterising the state of the global climate system (GCOS 2010). It provides insight into the flow of heat into and out of the ocean, into modes of variability in the ocean and atmosphere, can be used to identify features in the ocean such as fronts and upwelling, and knowledge of SST is also required for applications such as ocean and weather prediction (Roquet et al., 2016). CMEMS KEY FINDINGS Warming trends occurred over most of the globe between 1993 and 2021. One of the exceptions is the North Atlantic, which has a region south of Greenland where a cooling trend is found. The cooling in this area has been previously noted as occurring on centennial time scales (IPCC, 2013; Caesar et al., 2018; Sevellee et al., 2017). DOI (product):https://doi.org/10.48670/moi-00243 https://doi.org/10.48670/moi-00243 23 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-tourism-climate-suitability-indicators https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-tourism-climate-suitability-indicators sis-tourism-climate-suitability-indicators This dataset provides a set of climate suitability indicators for tourism in Europe under future climate scenarios. These indicators have been tailored for two different kinds of tourism activities; urban and beach tourism. The climatic suitability indicators consist of two indices; the Holiday Climate Index (HCI) rates the climate resources based on activities associated with urban tourism, while the Climate Index for Tourism (CIT) rates the climate resources based on activities associated with beach tourism. Tailored indicators, such as these, allow a range of climatic variables to be quantified and ranked for particular tourist destinations and activities, serving as an important decision-making tool for both tourists and the tourism industry. The use of indicators is particularly valuable given the multifaceted nature of meteorological conditions and the complex manner in which they determine tourism activities. By integrating these indicators with climate projections, this dataset aims to provide an understanding of how the influence of a changing climate will impact on tourism activities in Europe under a range of future scenarios of greenhouse gas concentrations, referred to as Representative Concentration Pathway (RCP) climate scenarios. This dataset was produced using four different climate scenarios: the present climate (labelled 'historical'), and three Representative Concentration Pathway (RCP) scenarios consistent with an optimistic emission scenario where emissions start declining beyond 2020 (RCP2.6), a scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century (RCP8.5). The climate projections are derived from EURO-CORDEX ensemble model output. Also included in this dataset are historical simulations for the period 1970-2005 in order to provide a reference period for the future projections. Both these indices take into account different climate variables (daily precipitation, wind speed, total cloud cover, and effective temperature) to evaluate the climate in relation to tourism. To each of these climate variables, a score is assigned according to their weighting criteria. The scores are then aggregated, resulting in a total rating scale ranging from 0 to 100 for HCI, and from 0 to 7 for CIT (please refer to the documentation for further details). In general, the higher the value of the indicators, the more suitable for tourism activities the seasonal conditions are. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Europe (27°N to 72°N and 22°W to 45°E) Horizontal resolution 0.11° x 0.11° Vertical coverage Surface Vertical resolution Single level Temporal coverage From 1970 to 2100 Temporal resolution Daily, dekadal, monthly and seasonal File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Europe (27°N to 72°N and 22°W to 45°E) Horizontal coverage Europe (27°N to 72°N and 22°W to 45°E) Horizontal resolution 0.11° x 0.11° Horizontal resolution 0.11° x 0.11° Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage From 1970 to 2100 Temporal coverage From 1970 to 2100 Temporal resolution Daily, dekadal, monthly and seasonal Temporal resolution Daily, dekadal, monthly and seasonal File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Daily index Dimensionless A set of bioclimatic indicators designed to assess whether climate conditions are suitable for tourism activities. This dataset contains the Holiday Climate Index, formulated to evaluate conditions for typical urban tourism activities, and the Climate Index for Tourism, used to rate beach tourism activities. The index value is calculated on a daily timestep for the 5-year period selected. Number of fair days Count Number of days per month where the index value falls within the range assigned "fair" in the definition of the selected comfort index (Holiday Climate Index [50< HCI <70] or Climate Index for Tourism [CIT=4]). Number of good days Count Number of days per month where the index value falls within the range assigned "good" in the definition of the selected comfort index (Holiday Climate Index [HCI>70] or Climate Index for Tourism [CIT>4]). Number of unfavourable days Count Number of days per month where the index value falls within the range assigned "unfavourable" in the definition of the selected comfort index (Holiday Climate Index [HCI<50] or Climate Index for Tourism [CIT<4]). MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Daily index Dimensionless A set of bioclimatic indicators designed to assess whether climate conditions are suitable for tourism activities. This dataset contains the Holiday Climate Index, formulated to evaluate conditions for typical urban tourism activities, and the Climate Index for Tourism, used to rate beach tourism activities. The index value is calculated on a daily timestep for the 5-year period selected. Daily index Dimensionless A set of bioclimatic indicators designed to assess whether climate conditions are suitable for tourism activities. This dataset contains the Holiday Climate Index, formulated to evaluate conditions for typical urban tourism activities, and the Climate Index for Tourism, used to rate beach tourism activities. The index value is calculated on a daily timestep for the 5-year period selected. Number of fair days Count Number of days per month where the index value falls within the range assigned "fair" in the definition of the selected comfort index (Holiday Climate Index [50< HCI <70] or Climate Index for Tourism [CIT=4]). Number of fair days Count Number of days per month where the index value falls within the range assigned "fair" in the definition of the selected comfort index (Holiday Climate Index [50< HCI <70] or Climate Index for Tourism [CIT=4]). Number of good days Count Number of days per month where the index value falls within the range assigned "good" in the definition of the selected comfort index (Holiday Climate Index [HCI>70] or Climate Index for Tourism [CIT>4]). Number of good days Count Number of days per month where the index value falls within the range assigned "good" in the definition of the selected comfort index (Holiday Climate Index [HCI>70] or Climate Index for Tourism [CIT>4]). Number of unfavourable days Count Number of days per month where the index value falls within the range assigned "unfavourable" in the definition of the selected comfort index (Holiday Climate Index [HCI<50] or Climate Index for Tourism [CIT<4]). Number of unfavourable days Count Number of days per month where the index value falls within the range assigned "unfavourable" in the definition of the selected comfort index (Holiday Climate Index [HCI<50] or Climate Index for Tourism [CIT<4]). 24 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-extreme-indices-cmip6 https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-extreme-indices-cmip6 sis-extreme-indices-cmip6 The dataset provides climate extreme indices related to temperature and precipitation as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), as well as selected heat stress indicators (HSI). The indices are provided for historical and future climate projections (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) included in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and used in the 6th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). This dataset provides a comprehensive source of pre-calculated and consistent ETCCDI and heat stress indicators commonly used by the climate science and impact communities. The majority of models used in this catalogue entry are now available in the Climate Data Store though the indices offered in this entry additionally include ensemble members obtained from the Earth System Grid Federation. The indices are calculated from CMIP6 models that have the necessary daily resolved data for both historical and at least two of the future projections. In addition, four of the chosen models contained a large number of ensemble members in order to enable the estimation of the associated uncertainty in the spread of model outcomes when calculating the ETCCDI indices (CanESM5, EC-Earth3, MIROC6 and MPI-ESM1-2-LR). All the ETCCDI indices in this dataset are calculated using the climdex.pcic R package, which was developed, evaluated and approved by the ETCCDI. To facilitate the usage of heat stress indicators in combination with thresholds on absolute values, this dataset additionally provides bias-adjusted heat stress indicators. Bias adjustment is carried out using the ISIMIP3b bias-adjustment method and employs the WATCH Forcing Data methodology applied to ERA5 (WFDE5) dataset as reference. Providing both pre-calculated bias-adjusted data and data without bias adjustment is of great value for climate and impact studies since the calculation of these datasets also are computationally expensive. The WFDE5 dataset is also available in the Climate Data Store. The heat stress indicators combine near-surface air temperature, near-surface specific humidity, and surface air pressure to give indications of adverse effects of heat on human health. Other variables like wind or solar radiation are not considered, and the selected heat stress indicators thus represent indoor conditions or calm conditions in the shade. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution From 0.5° x 0.5° to 2.8125° x 2.8125° depending on the model Vertical coverage Surface Vertical resolution Single level Temporal coverage From 1850 to 2300 for the whole dataset. Shorter for most of the models and products. Temporal resolution Yearly, monthly, daily File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions Dataset version 1.0 (legacy), 2.0 Update frequency No expected updates DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution From 0.5° x 0.5° to 2.8125° x 2.8125° depending on the model Horizontal resolution From 0.5° x 0.5° to 2.8125° x 2.8125° depending on the model Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage From 1850 to 2300 for the whole dataset. Shorter for most of the models and products. Temporal coverage From 1850 to 2300 for the whole dataset. Shorter for most of the models and products. Temporal resolution Yearly, monthly, daily Temporal resolution Yearly, monthly, daily File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions Dataset version 1.0 (legacy), 2.0 Versions Dataset version 1.0 (legacy), 2.0 Update frequency No expected updates Update frequency No expected updates 25 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-health-temperature-exposure-projections https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-health-temperature-exposure-projections app-health-temperature-exposure-projections The application delivers the European temperature exposure statistics for both the historical period, 1976-2005, and projected 30 year periods 2031-2060 and 2071-2100 using different climate change scenarios. These statistics were derived from daily 2 m mean, minimum and maximum air temperatures using a bias-adjusted EURO-CORDEX climate projections dataset. Temperature statistics are typically used in epidemiology and public health when defining health risk estimates and when looking at current and future health impacts. User-selectable parameters User-selectable parameters Climate variable: minimum/mean/maximum 2-metre air temperature. Statistic: ensemble mean, or 10th, 50th, 90th percentile. Season: annual/summer/winter. Climate variable: minimum/mean/maximum 2-metre air temperature. Statistic: ensemble mean, or 10th, 50th, 90th percentile. Season: annual/summer/winter. Description of the graphical output Description of the graphical output The application presents temperature exposure statistics at a European scale for the historical period, 1976-2005, and projected 30 year periods 2031-2060 and 2071-2100. Users can select the time-period and RCP scenario (if applicable) to view using the checkboxes in the upper-right of the livemap (note: the uppermost selected layer is visible when multiple layers are checked). The livemap is interactive and the user may focus on a particular country or adminstrative region by selecting it. When a country or adminstrative region is selected, a window is opened providing a focused view on the region as well as displaying a time-series of the 30-year running mean, with upper and lower confidence levels, of the temperature statistic for that region. The focused view allows users to scroll through the different time periods and climate scenarios. The timeseries allows users to turn on-off the two RCP scenarios by clicking on the legend entries in the top left courner of the graph. Note adminstrative regions are currently only available for Belgium, Hungary, Italy and Lithuania. More details about the products are given in the Documentation section. INPUT VARIABLES Name Units Description Source Air temperature K 2-metre air temperature statitics derived from EURO-CORDEX projections. Temperature statistics INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Air temperature K 2-metre air temperature statitics derived from EURO-CORDEX projections. Temperature statistics Air temperature K 2-metre air temperature statitics derived from EURO-CORDEX projections. Temperature statistics Temperature statistics OUTPUT VARIABLES Name Units Description Air temperature K 2-metre air temperature. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Air temperature K 2-metre air temperature. Air temperature K 2-metre air temperature. 26 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-surface-radiation-budget https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-surface-radiation-budget satellite-surface-radiation-budget This dataset provides the Surface Radiation Budget (SRB) Essential Climate Variable (ECV). SRB represents the balance between the heating of the surface through incoming solar radiation and cooling through emission of infra-red radiation which is a fundamental component of the surface energy budget. Small changes in the surface radiation budget can lead to large climatological responses, which makes a permanent and precise monitoring indispensable. Accurate understanding of the surface radiation budget at regional and global scales can improve the understanding of the Earth’s climate system and supports research on current and anticipated climate change, as well as the evaluation of radiative processes in climate models. Better knowledge of the global and regional surface radiation budget is also required for a successful utilization of renewable energy sources, such as solar power plants. The ECV Surface Radiation Budget contains seven variables which can be separated into the solar (shortwave) and the thermal (longwave) surface radiation components. Both components include the incoming (downwelling), the outgoing (upwelling), and the net radiation fluxes. These variables were produced by two “product families”, based on the data from different sensors and algorithms cover the same seven variables. The two families follow somewhat different nomenclatures. To help the user to navigate the different names, a table relating the names used in the download form with the names used within the files and in other datasets distributed by the Climate Data Store is added at the end of this overview. The fact that the naming is different reflects the fact that each family was originated by different projects within different organisations at different times. However, the third organisation, the Copernicus Climate Change Service (C3S) is not associated with a different product family in this dataset. In fact it is associated with both product families to which it provides a continuation of their production chain. This means that the datasets contains three organisation but just two product families. The “CLARA product family” and “CCI product family” provide two complementary Thematic Climate Data Records (TCDRs), differing in temporal and horizontal resolution. These TCDR timeseries are intended to have sufficient length, consistency, and continuity to detect climate variability and change. They are frequently updated with Interim Climate Data Records (ICDRs) or simply extensions, generated using the same software and algorithms to cover more recent periods. Further details on algorithms, data description, and extensive validation results are given in the Documentation section. 27 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/iberia-biscay-ireland-significant-wave-height-extreme-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=IBI_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly Iberia Biscay Ireland Significant Wave Height extreme from Reanalysis DEFINITION The CMEMS IBI_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_WAV_005_006) and the Analysis product (IBI_ANALYSIS_FORECAST_WAV_005_005). Two parameters have been considered for this OMI: • Map of the 99th mean percentile: It is obtained from the Multi-Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1993-2019). • Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2020. This indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (Pérez Gómez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Álvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (Álvarez- Fanjul et al., 2019). CONTEXT The sea state and its related spatio-temporal variability affect dramatically maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (González-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2019). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions. The Iberia-Biscay-Ireland region, which covers the North-East Atlantic Ocean from Canary Islands to Ireland, is characterized by two different sea state wave climate regions: whereas the northern half, impacted by the North Atlantic subpolar front, is of one of the world’s greatest wave generating regions (Mørk et al., 2010; Folley, 2017), the southern half, located at subtropical latitudes, is by contrast influenced by persistent trade winds and thus by constant and moderate wave regimes. The North Atlantic Oscillation (NAO), which refers to changes in the atmospheric sea level pressure difference between the Azores and Iceland, is a significant driver of wave climate variability in the Northern Hemisphere. The influence of North Atlantic Oscillation on waves along the Atlantic coast of Europe is particularly strong in and has a major impact on northern latitudes wintertime (Martínez-Asensio et al. 2016; Bacon and Carter, 1991; Bouws et al., 1996; Bauer, 2001; Woolf et al., 2002; Gleeson et al., 2017). Swings in the North Atlantic Oscillation index produce changes in the storms track and subsequently in the wind speed and direction over the Atlantic that alter the wave regime. When North Atlantic Oscillation index is in its positive phase, storms usually track northeast of Europe and enhanced westerly winds induce higher than average waves in the northernmost Atlantic Ocean. Conversely, in the negative North Atlantic Oscillation phase, the track of the storms is more zonal and south than usual, with trade winds (mid latitude westerlies) being slower and producing higher than average waves in southern latitudes. Additionally, a variety of previous studies have unequivocally determined the relationship between the sea state variability in the IBI region and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Martínez-Asensio et al., 2016). In this context, long‐term statistical analysis of reanalyzed model data is mandatory not only to disentangle other driving agents of wave climate but also to attempt inferring any potential trend in the number and/or intensity of extreme wave events in coastal areas with subsequent socio-economic and environmental consequences. CMEMS KEY FINDINGS The climatic mean of 99th percentile (1993-2020) reveals a north-south gradient of Significant Wave Height with the highest values in northern latitudes (above 8m) and lowest values (2-3 m) detected southeastward of Canary Islands, in the seas between Canary Islands and the African Continental Shelf. This north-south pattern is the result of the two climatic conditions prevailing in the region. The anomaly of the 99th percentile in 2021 shows a homogeneous distribution of negative anomalies affecting the Eastern North Atlantic from Canary Islands at 27°N up to Ireland at 56°N. These anomalies are significative and surpass the climatic standard deviation of Significant Wave Height in the region. The indicator does not show any region with negative anomalies bigger than twice the climatic standard deviation. The indicator shows localized coastal regions where the maximum wave height surpasses twice the climatic standard deviation, i. e.: South of Canary islands, South coast of Iberian Peninsula, English Channel, and Irish Sea. DOI (product):https://doi.org/10.48670/moi-00249 https://doi.org/10.48670/moi-00249 28 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/urban-atlas-land-coverland-use-2018-vector-europe-6 https://land.copernicus.eu/local/urban-atlas/urban-atlas-2018?tab=download Urban Atlas Land Cover/Land Use 2018 (vector), Europe, 6-yearly, Jul. 2021 Urban Atlas 2018 provides reliable, inter-comparable, high-resolution land use and land cover data with integrated population estimates for 788 Functional Urban Areas (FUA) with more than 50,000 inhabitants for the 2018 reference year in EEA38 countries (EU, EFTA, Western Balkans countries, as well as Türkiye) and the United Kingdom. Urban Atlas is a joint initiative of the European Commission Directorate-General for Regional and Urban Policy and the Directorate-General for Defence Industry and Space in the frame of the EU Copernicus programme, with the support of the European Space Agency and the European Environment Agency. Per each of the FUAs, a ZIP is provided which includes: (1) the vector data in OGC GeoPackage SQLite format (ETRS89-LAEA, EPSG:3035); (2) PDF document with a high-resolution map of the area; (3) PDF document with the delivery report; (4) symbology files in .lyr, .qml and .sld formats; and (5) a xml document with metadata. 29 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/iberia-biscay-ireland-ocean-heat-content-anomaly-0-2000m http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_CLIMATE_OHC_IBI_area_averaged_anomalies Iberia Biscay Ireland Ocean Heat Content Anomaly (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing DEFINITION Ocean heat content (OHC) is defined here as the deviation from a reference period (1993-2020) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 2000 m depth. With a reference density of ρ0 = 1030 kgm-3 and a specific heat capacity of cp = 3980 J/kg°C (e.g. von Schuckmann et al., 2009) Averaged time series for ocean heat content and their error bars are calculated for the Iberia-Biscay-Ireland region (26°N, 56°N; 19°W, 5°E). This OMI is computed using IBI-MYP, GLO-MYP reanalysis and CORA, ARMOR data from observations which provide temperatures. Where the CMEMS product for each acronym is: * IBI-MYP: IBI_MULTIYEAR_PHY_005_002 (Reanalysis) * GLO-MYP: GLOBAL_REANALYSIS_PHY_001_031 (Reanalysis) * CORA: INSITU_GLO_TS_OA_REP_OBSERVATIONS_013_002_b (Observations) * ARMOR: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (Reprocessed observations) The figure comprises ensemble mean (blue line) and the ensemble spread (grey shaded). Details on the product are given in the corresponding PUM for this OMI as well as the CMEMS Ocean State Report: von Schuckmann et al., 2016; von Schuckmann et al., 2018. CONTEXT Change in OHC is a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and can impact marine ecosystems and human livelihoods (IPCC, 2019). Additionally, OHC is one of the six Global Climate Indicators recommended by the World Meterological Organisation (WMO, 2017). In the last decades, the upper North Atlantic Ocean experienced a reversal of climatic trends for temperature and salinity. While the period 1990-2004 is characterized by decadal-scale ocean warming, the period 2005-2014 shows a substantial cooling and freshening. Such variations are discussed to be linked to ocean internal dynamics, and air-sea interactions (Fox-Kemper et al., 2021; Collins et al., 2019; Robson et al 2016). Together with changes linked to the connectivity between the North Atlantic Ocean and the Mediterranean Sea (Masina et al., 2022), these variations affect the temporal evolution of regional ocean heat content in the IBI region. CMEMS KEY FINDINGS The ensemble mean OHC anomaly time series over the Iberia-Biscay-Ireland region are dominated by strong year-to-year variations, and an ocean warming trend of 0.49±0.4 W/m2 is barely significant. DOI (product):https://doi.org/10.48670/mds-00316 https://doi.org/10.48670/mds-00316 30 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/urban-atlas-land-coverland-use-change-2006-2012-vector https://land.copernicus.eu/local/urban-atlas/change-2006-2009/view Urban Atlas Land Cover/Land Use Change 2006-2012 (vector), Europe, 6-yearly, Sep. 2016 The Urban Atlas provides pan-European comparable land use and land cover data for Functional Urban Areas (FUA). The Urban Atlas Change layers have become available from 2012 and only for all FUAs that have been covered in both 2006 and 2012 reference years. Urban Atlas is a joint initiative of the European Commission Directorate-General for Regional and Urban Policy and the Directorate-General for Enterprise and Industry in the frame of the EU Copernicus programme, with the support of the European Space Agency and the European Environment Agency. 31 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-temperature-statistics https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-temperature-statistics sis-temperature-statistics This dataset contains temperature exposure statistics for Europe (e.g. percentiles) derived from the daily 2 metre mean, minimum and maximum air temperature for the entire year, winter (DJF: December-January-February) and summer (JJA: June-July-August). These statistics were derived within the C3S European Health service and are available for different future time periods and using different climate change scenarios. Temperature percentiles are typically used in epidemiology and public health when defining health risk estimates and when looking at current and future health impacts, and they allow to identify a common threshold and comparison between different cities/areas. The temperature statistics are calculated, either for the season winter and summer or for the whole year, based on a bias-adjusted EURO-CORDEX dataset. The statistics are averaged for 30 years as a smoothed average from 1971 to 2100. This results in a timeseries covering the period from 1986 to 2085. Finally, the timeseries are averaged for the model ensemble and the standard deviation to this ensemble mean is provided. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid. Horizontal coverage European region (approximately 27N – 72N, 22W – 45E) Horizontal resolution 0.1° x 0.1° Temporal coverage 1986 – 2085 Temporal resolution Season or year, that represents the 30-yr smoothed average around that particular season or year. File format NetCDF Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency No updates expected. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid. Projection Regular latitude-longitude grid. Horizontal coverage European region (approximately 27N – 72N, 22W – 45E) Horizontal coverage European region (approximately 27N – 72N, 22W – 45E) Horizontal resolution 0.1° x 0.1° Horizontal resolution 0.1° x 0.1° Temporal coverage 1986 – 2085 Temporal coverage 1986 – 2085 Temporal resolution Season or year, that represents the 30-yr smoothed average around that particular season or year. Temporal resolution Season or year, that represents the 30-yr smoothed average around that particular season or year. File format NetCDF File format NetCDF Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency No updates expected. Update frequency No updates expected. MAIN VARIABLES Name Units Description Average temperature K Daily average air temperature valid for a grid cell at the height of 2m above the surface, averaged over the year or season. Maximum temperature K Daily maximum air temperature valid for a grid cell at the height of 2m above the surface, averaged over the year or season. Minimum temperature K Daily minimum air temperature valid for a grid cell at the height of 2m above the surface, averaged over the year or season. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Average temperature K Daily average air temperature valid for a grid cell at the height of 2m above the surface, averaged over the year or season. Average temperature K Daily average air temperature valid for a grid cell at the height of 2m above the surface, averaged over the year or season. Maximum temperature K Daily maximum air temperature valid for a grid cell at the height of 2m above the surface, averaged over the year or season. Maximum temperature K Daily maximum air temperature valid for a grid cell at the height of 2m above the surface, averaged over the year or season. Minimum temperature K Daily minimum air temperature valid for a grid cell at the height of 2m above the surface, averaged over the year or season. Minimum temperature K Daily minimum air temperature valid for a grid cell at the height of 2m above the surface, averaged over the year or season. 32 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/land-surface-temperature-2021-present-raster-5-km-global https://land.copernicus.eu/global/access Land Surface Temperature 2021-present (raster 5 km), global, hourly - version 2 Land Surface Temperature (LST) is the radiative skin temperature over land. LST plays an important role in the physics of land surface as it is involved in the processes of energy and water exchange with the atmosphere. LST is useful for the scientific community, namely for those dealing with meteorological and climate models. Accurate values of LST are also of special interest in a wide range of areas related to land surface processes, including meteorology, hydrology, agrometeorology, climatology and environmental studies. 33 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/land-surface-temperature-2010-2021-raster-5-km-global https://land.copernicus.eu/global/access Land Surface Temperature 2010-2021 (raster 5 km), global, hourly - version 1 Land Surface Temperature (LST) is the radiative skin temperature over land. LST plays an important role in the physics of land surface as it is involved in the processes of energy and water exchange with the atmosphere. LST is useful for the scientific community, namely for those dealing with meteorological and climate models. Accurate values of LST are also of special interest in a wide range of areas related to land surface processes, including meteorology, hydrology, agrometeorology, climatology and environmental studies. 34 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-heat-content-0-300m-reanalysis-multi http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_OHC_area_averaged_anomalies_0_300 Global Ocean Heat Content (0-300m) from Reanalysis & Multi-Observations Reprocessing DEFINITION Estimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60°S-60°N aiming i) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change. ii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). Ocean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017). CONTEXT Knowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019). CMEMS KEY FINDINGS Since the year 2005, the near-surface (0-300m) near-global (60°S-60°N) ocean warms at a rate of 0.4 ± 0.1 W/m2. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00233 https://doi.org/10.48670/moi-00233 35 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/projections-cmip5-monthly-single-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip5-monthly-single-levels projections-cmip5-monthly-single-levels This catalogue entry provides monthly climate projections on single levels from a large number of experiments, models, members and time periods computed in the framework of fifth phase of the Coupled Model Intercomparison Project (CMIP5). The term "single levels" is used to express that the variables are computed at one vertical level which can be surface (or a level close to the surface) or a dedicated pressure level in the atmosphere. Multiple vertical levels are excluded from this catalogue entry. CMIP5 data are used extensively in the Intergovernmental Panel on Climate Change Assessment Reports (the latest one is IPCC AR5, which was published in 2014). The use of these data is mostly aimed at: addressing outstanding scientific questions that arose as part of the IPCC reporting process; improving the understanding of the climate system; providing estimates of future climate change and related uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different models are in simulating the recent past. addressing outstanding scientific questions that arose as part of the IPCC reporting process; improving the understanding of the climate system; providing estimates of future climate change and related uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different models are in simulating the recent past. The term "experiments" refers to the three main categories of CMIP5 simulations: Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2005. Ensemble of experiments from the Atmospheric Model Intercomparison Project (AMIP), which prescribes the oceanic variables for all models and during all period of the experiment. This configuration removes the added complexity of ocean-atmosphere feedbacks in the climate system. The period covered is typically 1950-2005. Ensemble of climate projection experiments following the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. The RCP scenarios provide different pathways of the future climate forcing. The period covered is typically 2006-2100, some extended RCP experimental data is available from 2100-2300. Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2005. Ensemble of experiments from the Atmospheric Model Intercomparison Project (AMIP), which prescribes the oceanic variables for all models and during all period of the experiment. This configuration removes the added complexity of ocean-atmosphere feedbacks in the climate system. The period covered is typically 1950-2005. Ensemble of climate projection experiments following the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. The RCP scenarios provide different pathways of the future climate forcing. The period covered is typically 2006-2100, some extended RCP experimental data is available from 2100-2300. In CMIP5, the same experiments were run using different GCMs. In addition, for each model, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. Note that CMIP5 GCM data can be also used as lateral boundary conditions for Regional Climate Models (RCMs). RCMs are also available in the CDS (see CORDEX datasets). The data are produced by the participating institutes of the CMIP5 project. The latest CMIP GCM experiments will form the CMIP6 dataset, which will be published in the CDS in a later stage. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution From 0.125° x 0.125° to 5° x 5° depending on the model Vertical resolution Variables are provided in one single level which may differ among variables. Temporal coverage 1850-2300 (shorter for some experiments) Temporal resolution Month File format NetCDF DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution From 0.125° x 0.125° to 5° x 5° depending on the model Horizontal resolution From 0.125° x 0.125° to 5° x 5° depending on the model Vertical resolution Variables are provided in one single level which may differ among variables. Vertical resolution Variables are provided in one single level which may differ among variables. Temporal coverage 1850-2300 (shorter for some experiments) Temporal coverage 1850-2300 (shorter for some experiments) Temporal resolution Month Temporal resolution Month File format NetCDF File format NetCDF MAIN VARIABLES Name Units Description 10m u component of wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity near the surface. 10m v component of wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity near the surface. 10m wind_speed m s-1 Magnitude of the two-dimensional horizontal air velocity near thesurface. 2m temperature K Temperature of the air near the surface. Eastward turbulent surface stress N s m-2 Eastward component of the horizontal drag exerted by the atmosphere on the surface through turbulent processes. Evaporation kg m-2 s-1 Evaporation rate. It includes conversion to vapor phase from both the liquid and solid phase, i.e., includes sublimation. Maximum 2m temperature in the last 24 hours K Daily maximum near-surface air temperature. Mean precipitation flux kg m-2 s-1 Amount of water per unit area and time. Mean sea level pressure Pa Time average of the air pressure at sea level. Minimum 2m temperature in the last 24 hours K Daily minimum near-surface air temperature. Near surface relative humidity % Amount of moisture in the air near the surface divided by the maximum amount of moisture that could exist in the air at a specific temperature and location. Near surface specific humidity Dimensionless Amount of moisture in the air near the surface divided by amount of air plus moist at that location. Northward turbulent surface stress N s m-2 Northward component of the horizontal drag exerted by the atmosphere on the surface through turbulent processes. Runoff kg m-2 s-1 Amount per unit area of surface and subsurface liquid water which drains from land. Sea ice fraction Dimensionless Area of the sea surface occupied by sea ice. Sea ice plus snow amount kg m-2 Mass per unit area of sea ice plus snow in the ocean portion of the grid cell averaged over the entire ocean portion, including the ice-free fraction. Reported as 0.0 in regions free of sea ice. Sea ice surface temperature K Temperature that exists at the interface of tea sea-ice and the overlying medium which may be air or snow. Sea ice thickness m Vertical extent of ocean sea ice. Sea surface height above geoid m Vertical distance between the actual sea surface and a surface of constant geopotential with which mean sea level would coincide if the ocean were at rest. Sea surface temperature K Temperature of sea water near the surface. Skin temperature K Temperature at the interface (not the bulk temperature of the medium above or below) between air and sea for open-sea regions. Snow depth over sea ice K Mean thickness of snow in the ocean portion of the grid cell (averaging over the entire ocean portion, including the snow-free ocean fraction). Reported as 0.0 in regions free of snow-covered sea ice. Snowfall kg m-2 s-1 Mass of water in the form of snow precipitating per unit area. Soil moisture content kg m-2 Vertical sum per unit area from the surface down to the bottom of the soil model of water in all phases contained in soil. Surface latent heat flux W m-2 Flux per unit area of heat between the surface and the air on account of evaporation including sublimation. Positive when directed upward (negative downward). Surface pressure Pa Pressure of air at the lower boundary of the atmopshere Surface sensible heat flux W m-2 Flux per unit area of heat between the surface and the air by motion of air only. Positive when directed upward (negative downward). Surface snow amount kg m-2 Snow amount on the ground, excluding that on the plant or vegetation canopy, per unit area. Surface solar radiation downwards W m-2 Radiative shortwave flux of energy downward at the surface. Surface thermal radiation downwards W m-2 Radiation inciding on the surface from the above per unit area. Surface upwelling longwave radiation W m-2 Longwave radiation from the surface per unit area. Surface upwelling shortwave radiation W m-2 Shortwave radiation from the surface per unit area. TOA incident solar radiation W m-2 Incident solar radiation at the top of atmosphere TOA outgoing clear-sky longwave radiation W m-2 Longwave radiation from the top of the atmosphere to space per unit area assuming clear-sky conditions TOA outgoing clear-sky shortwave radiation W m-2 Shortwave radiation from the top of the atmosphere to space per unit area assuming clear-sky conditions TOA outgoing longwave radiation W m-2 Longwave radiation from the top of the atmosphere to space per unit area. TOA outgoing shortwave radiation W m-2 Shortwave radiation from the top of the atmosphere to space per unit area. Total cloud cover Dimensionless Total refers to the whole atmosphere column, as seen from the surface or the top of the atmosphere. Cloud cover refers to fraction of horizontal area occupied by clouds. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 10m u component of wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity near the surface. 10m u component of wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity near the surface. 10m v component of wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity near the surface. 10m v component of wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity near the surface. 10m wind_speed m s-1 Magnitude of the two-dimensional horizontal air velocity near thesurface. 10m wind_speed m s-1 Magnitude of the two-dimensional horizontal air velocity near thesurface. 2m temperature K Temperature of the air near the surface. 2m temperature K Temperature of the air near the surface. Eastward turbulent surface stress N s m-2 Eastward component of the horizontal drag exerted by the atmosphere on the surface through turbulent processes. Eastward turbulent surface stress N s m-2 Eastward component of the horizontal drag exerted by the atmosphere on the surface through turbulent processes. Evaporation kg m-2 s-1 Evaporation rate. It includes conversion to vapor phase from both the liquid and solid phase, i.e., includes sublimation. Evaporation kg m-2 s-1 Evaporation rate. It includes conversion to vapor phase from both the liquid and solid phase, i.e., includes sublimation. Maximum 2m temperature in the last 24 hours K Daily maximum near-surface air temperature. Maximum 2m temperature in the last 24 hours K Daily maximum near-surface air temperature. Mean precipitation flux kg m-2 s-1 Amount of water per unit area and time. Mean precipitation flux kg m-2 s-1 Amount of water per unit area and time. Mean sea level pressure Pa Time average of the air pressure at sea level. Mean sea level pressure Pa Time average of the air pressure at sea level. Minimum 2m temperature in the last 24 hours K Daily minimum near-surface air temperature. Minimum 2m temperature in the last 24 hours K Daily minimum near-surface air temperature. Near surface relative humidity % Amount of moisture in the air near the surface divided by the maximum amount of moisture that could exist in the air at a specific temperature and location. Near surface relative humidity % Amount of moisture in the air near the surface divided by the maximum amount of moisture that could exist in the air at a specific temperature and location. Near surface specific humidity Dimensionless Amount of moisture in the air near the surface divided by amount of air plus moist at that location. Near surface specific humidity Dimensionless Amount of moisture in the air near the surface divided by amount of air plus moist at that location. Northward turbulent surface stress N s m-2 Northward component of the horizontal drag exerted by the atmosphere on the surface through turbulent processes. Northward turbulent surface stress N s m-2 Northward component of the horizontal drag exerted by the atmosphere on the surface through turbulent processes. Runoff kg m-2 s-1 Amount per unit area of surface and subsurface liquid water which drains from land. Runoff kg m-2 s-1 Amount per unit area of surface and subsurface liquid water which drains from land. Sea ice fraction Dimensionless Area of the sea surface occupied by sea ice. Sea ice fraction Dimensionless Area of the sea surface occupied by sea ice. Sea ice plus snow amount kg m-2 Mass per unit area of sea ice plus snow in the ocean portion of the grid cell averaged over the entire ocean portion, including the ice-free fraction. Reported as 0.0 in regions free of sea ice. Sea ice plus snow amount kg m-2 Mass per unit area of sea ice plus snow in the ocean portion of the grid cell averaged over the entire ocean portion, including the ice-free fraction. Reported as 0.0 in regions free of sea ice. Sea ice surface temperature K Temperature that exists at the interface of tea sea-ice and the overlying medium which may be air or snow. Sea ice surface temperature K Temperature that exists at the interface of tea sea-ice and the overlying medium which may be air or snow. Sea ice thickness m Vertical extent of ocean sea ice. Sea ice thickness m Vertical extent of ocean sea ice. Sea surface height above geoid m Vertical distance between the actual sea surface and a surface of constant geopotential with which mean sea level would coincide if the ocean were at rest. Sea surface height above geoid m Vertical distance between the actual sea surface and a surface of constant geopotential with which mean sea level would coincide if the ocean were at rest. Sea surface temperature K Temperature of sea water near the surface. Sea surface temperature K Temperature of sea water near the surface. Skin temperature K Temperature at the interface (not the bulk temperature of the medium above or below) between air and sea for open-sea regions. Skin temperature K Temperature at the interface (not the bulk temperature of the medium above or below) between air and sea for open-sea regions. Snow depth over sea ice K Mean thickness of snow in the ocean portion of the grid cell (averaging over the entire ocean portion, including the snow-free ocean fraction). Reported as 0.0 in regions free of snow-covered sea ice. Snow depth over sea ice K Mean thickness of snow in the ocean portion of the grid cell (averaging over the entire ocean portion, including the snow-free ocean fraction). Reported as 0.0 in regions free of snow-covered sea ice. Snowfall kg m-2 s-1 Mass of water in the form of snow precipitating per unit area. Snowfall kg m-2 s-1 Mass of water in the form of snow precipitating per unit area. Soil moisture content kg m-2 Vertical sum per unit area from the surface down to the bottom of the soil model of water in all phases contained in soil. Soil moisture content kg m-2 Vertical sum per unit area from the surface down to the bottom of the soil model of water in all phases contained in soil. Surface latent heat flux W m-2 Flux per unit area of heat between the surface and the air on account of evaporation including sublimation. Positive when directed upward (negative downward). Surface latent heat flux W m-2 Flux per unit area of heat between the surface and the air on account of evaporation including sublimation. Positive when directed upward (negative downward). Surface pressure Pa Pressure of air at the lower boundary of the atmopshere Surface pressure Pa Pressure of air at the lower boundary of the atmopshere Surface sensible heat flux W m-2 Flux per unit area of heat between the surface and the air by motion of air only. Positive when directed upward (negative downward). Surface sensible heat flux W m-2 Flux per unit area of heat between the surface and the air by motion of air only. Positive when directed upward (negative downward). Surface snow amount kg m-2 Snow amount on the ground, excluding that on the plant or vegetation canopy, per unit area. Surface snow amount kg m-2 Snow amount on the ground, excluding that on the plant or vegetation canopy, per unit area. Surface solar radiation downwards W m-2 Radiative shortwave flux of energy downward at the surface. Surface solar radiation downwards W m-2 Radiative shortwave flux of energy downward at the surface. Surface thermal radiation downwards W m-2 Radiation inciding on the surface from the above per unit area. Surface thermal radiation downwards W m-2 Radiation inciding on the surface from the above per unit area. Surface upwelling longwave radiation W m-2 Longwave radiation from the surface per unit area. Surface upwelling longwave radiation W m-2 Longwave radiation from the surface per unit area. Surface upwelling shortwave radiation W m-2 Shortwave radiation from the surface per unit area. Surface upwelling shortwave radiation W m-2 Shortwave radiation from the surface per unit area. TOA incident solar radiation W m-2 Incident solar radiation at the top of atmosphere TOA incident solar radiation W m-2 Incident solar radiation at the top of atmosphere TOA outgoing clear-sky longwave radiation W m-2 Longwave radiation from the top of the atmosphere to space per unit area assuming clear-sky conditions TOA outgoing clear-sky longwave radiation W m-2 Longwave radiation from the top of the atmosphere to space per unit area assuming clear-sky conditions TOA outgoing clear-sky shortwave radiation W m-2 Shortwave radiation from the top of the atmosphere to space per unit area assuming clear-sky conditions TOA outgoing clear-sky shortwave radiation W m-2 Shortwave radiation from the top of the atmosphere to space per unit area assuming clear-sky conditions TOA outgoing longwave radiation W m-2 Longwave radiation from the top of the atmosphere to space per unit area. TOA outgoing longwave radiation W m-2 Longwave radiation from the top of the atmosphere to space per unit area. TOA outgoing shortwave radiation W m-2 Shortwave radiation from the top of the atmosphere to space per unit area. TOA outgoing shortwave radiation W m-2 Shortwave radiation from the top of the atmosphere to space per unit area. Total cloud cover Dimensionless Total refers to the whole atmosphere column, as seen from the surface or the top of the atmosphere. Cloud cover refers to fraction of horizontal area occupied by clouds. Total cloud cover Dimensionless Total refers to the whole atmosphere column, as seen from the surface or the top of the atmosphere. Cloud cover refers to fraction of horizontal area occupied by clouds. 36 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-europe-air-quality-forecasts-precaching https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-forecasts-for-precaching cams-europe-air-quality-forecasts-for-precaching This dataset is a hidden mirror of cams-europe-air-quality-forecasts. It uses a different adaptor method that doesn't apply the constraints, which is necessary in order to use the adaptor (via the CDS API) to retrieve, and therefore pre-cache, as-yet unpublished fields. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Horizontal coverage Europe (west boundary=25.0° W, east=45.0° E, south=30.0° N, north=70.0°) Horizontal resolution 0.1°x0.1° (10 km x 10 km) Vertical coverage Surface, 50m, 100m, 250m, 500m, 750m, 1000m, 2000m, 3000m, 5000m Temporal coverage three-year rolling archive Temporal resolution 1-hourly File format GRIB, NetCDF Update frequency daily DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Europe (west boundary=25.0° W, east=45.0° E, south=30.0° N, north=70.0°) Horizontal coverage Europe (west boundary=25.0° W, east=45.0° E, south=30.0° N, north=70.0°) Horizontal resolution 0.1°x0.1° (10 km x 10 km) Horizontal resolution 0.1°x0.1° (10 km x 10 km) Vertical coverage Surface, 50m, 100m, 250m, 500m, 750m, 1000m, 2000m, 3000m, 5000m Vertical coverage Surface, 50m, 100m, 250m, 500m, 750m, 1000m, 2000m, 3000m, 5000m Temporal coverage three-year rolling archive Temporal coverage three-year rolling archive Temporal resolution 1-hourly Temporal resolution 1-hourly File format GRIB, NetCDF File format GRIB, NetCDF Update frequency daily Update frequency daily 37 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-climate-monitoring-volcanoes https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-climate-monitoring-volcanoes app-climate-monitoring-volcanoes This application provides visualisations of historical climate statistics averaged over spatial and temporal domains, and volcanic eruptions. Major explosive volcanic eruptions can cause short-term effects on the Earth climate, contributing to natural climate variability on global and regional scales. During such explosive volcanic eruptions, large quantities of ash and aerosols are released into the atmosphere which can potentially reach the stratosphere. Once in the stratosphere, fast-moving winds can quickly spread the ash cloud around the world, giving a local eruption a global impact. The effect of the volcanic eruptions on the climate variability depends on the amount of sulphur dioxide contained in the cloud dust, how high the dust cloud reaches in the atmosphere and the geographical position of the eruption. Major volcanic eruptions may cause cooling of the troposphere and, if the dust cloud reaches the stratosphere, they cause heating of the stratosphere and may impact stratospheric ozone. These effects are mainly caused by sulphur dioxide that is quickly converted into sulphate aerosols. These aerosols, alongside the fine volcanic ash, absorb incoming solar radiation, resulting in stratospheric heating, and scatter it back out into space providing a partial barrier to incoming solar radiation, thus resulting in tropospheric cooling. During the eruption of Mt. Pinatubo in June 1991, a large amount of sulphur dioxide was emitted into the stratosphere, with the enhancement of the stratospheric aerosol burden for 2 to 4 years after the eruption. The aerosol layer perturbed the Earth's radiative balance, resulting in a global mean radiative forcing up to −3 W m−2, a global surface cooling of ∼ 0.4 - 0.5 K, and a temperature increase of ∼ 2.5 - 3.5 K in the tropical lower stratosphere. The observed temperature perturbations were then connected to many feedbacks in the Earth system, including dynamical and chemical effects on stratospheric ozone. This application does not provide a detailed analysis of the effects of the volcanic eruptions on the Earth's climate, but offers a quick look at climate statistics and highlights the behaviour of these statistics in response to natural events such as volcanic eruptions. The signal in the climate variable statistics does not automatically imply a connection to a selected volcanic eruption, but is indicative of when the eruption occured. The response of the climate statistics to a certain volcanic eruption can overlap with the response to another source of climate variability. See documentation for more information. Users have a choice of several climate Variables and Regions/Volcanoes. Spatial aggregation and volcanic eruptions are presented on the livemap as selectable layers. The user can select a specific region or a specific volcanic eruption by clicking on the map. By selecting a region users can study signals in the time series climate data and compare with the timeline of the major volcanic eruptions. By selecting a volcanic eruption users will be presented with a map that will help to analyse the possible effects of the eruption on the data 6, 12 and 24 months after the eruption. This application is driven by ERA5 - the fifth generation ECMWF atmospheric reanalysis of the global climate, and CAMS EAC4 (ECMWF Atmospheric Composition Reanalysis 4) - the fourth generation ECMWF global reanalysis of atmospheric composition. ERA5 currently describes the global history of the atmosphere for the period from 1979 till present time, using a combination of forecast models and data assimilation systems to "reanalyse" past observations. The CAMS EAC4 is only available from 2003 onwards. User-selectable parameters User-selectable parameters Variable: monthly mean climate fields. Some variables are available from several datasets. Variables from different datasets have undergone a unit conversion to make them homogeneous across different datasets. Regions/Volcanoes: switch between a map of geographical zones or volcanic eruptions. Options are: Geographical zones, Whole globe, Volcanoes. Geographical zones (Arctic, Northern mid-latitudes, Equatorial region, Southern mid-latitudes, Antarctic) for spatial averaging are displayed as a selectable layer on the map. Volcanic eruptions comprise of events of category 4 and above which can affect climate data, displayed as a selectable layer on the map. Variable: monthly mean climate fields. Some variables are available from several datasets. Variables from different datasets have undergone a unit conversion to make them homogeneous across different datasets. Regions/Volcanoes: switch between a map of geographical zones or volcanic eruptions. Options are: Geographical zones, Whole globe, Volcanoes. Geographical zones (Arctic, Northern mid-latitudes, Equatorial region, Southern mid-latitudes, Antarctic) for spatial averaging are displayed as a selectable layer on the map. Volcanic eruptions comprise of events of category 4 and above which can affect climate data, displayed as a selectable layer on the map. Output Output Time series anomalies for a range of statistics: 4-months rolling mean, 12-months rolling mean, winter mean, spring mean, summer mean, autumn mean, yearly mean, decadal mean. Statistics are calculated from monthly mean anomalies taking into account weighting by days in a month where applicable. Anomalies are calculated with respect to the data availability period with only full years included. Gridded Global anomalies averaged over 1, 6, 12 and 24 months from the start of the selected volcanic eruption. Also given are gridded anomalies averaged over the time period, equil to the duration of the volcanic eruption in months, from the start of the selected volcanic eruption, where possible. Time series anomalies for a range of statistics: 4-months rolling mean, 12-months rolling mean, winter mean, spring mean, summer mean, autumn mean, yearly mean, decadal mean. Statistics are calculated from monthly mean anomalies taking into account weighting by days in a month where applicable. Anomalies are calculated with respect to the data availability period with only full years included. Time series anomalies for a range of statistics: 4-months rolling mean, 12-months rolling mean, winter mean, spring mean, summer mean, autumn mean, yearly mean, decadal mean. Statistics are calculated from monthly mean anomalies taking into account weighting by days in a month where applicable. Anomalies are calculated with respect to the data availability period with only full years included. Gridded Global anomalies averaged over 1, 6, 12 and 24 months from the start of the selected volcanic eruption. Also given are gridded anomalies averaged over the time period, equil to the duration of the volcanic eruption in months, from the start of the selected volcanic eruption, where possible. Gridded Global anomalies averaged over 1, 6, 12 and 24 months from the start of the selected volcanic eruption. Also given are gridded anomalies averaged over the time period, equil to the duration of the volcanic eruption in months, from the start of the selected volcanic eruption, where possible. Description of the graphical output Description of the graphical output The application consists of one of 2 charts depending on the selected feature on the map: geographical region - time series anomalies with overplotted volcanic eruptions; volcanic eruption - carousel map of global anomalies averaged over 1, 6, 12 and 24 months from the start of the eruption. Also shown are maps of global anomalies averaged over the time period, equil to the duration of the volcanic eruption in months, from the start of the selected volcanic eruption, where possible. geographical region - time series anomalies with overplotted volcanic eruptions; geographical region - time series anomalies with overplotted volcanic eruptions; volcanic eruption - carousel map of global anomalies averaged over 1, 6, 12 and 24 months from the start of the eruption. Also shown are maps of global anomalies averaged over the time period, equil to the duration of the volcanic eruption in months, from the start of the selected volcanic eruption, where possible. volcanic eruption - carousel map of global anomalies averaged over 1, 6, 12 and 24 months from the start of the eruption. Also shown are maps of global anomalies averaged over the time period, equil to the duration of the volcanic eruption in months, from the start of the selected volcanic eruption, where possible. INPUT VARIABLES Name Units Description Source Stratospheric temperature at 100 hPa oC This parameter is the temperature of the atmosphere at 100 hPa. ERA5 Stratospheric temperature at 50 hPa oC This parameter is the temperature of the atmosphere at 50 hPa. ERA5 Surface air temperature oC This variable is the temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. ERA5 Total aerosol optical depth at 550nm ~ Aerosol optical depth is a measure of the extinction of the solar beam by dust and haze. In other words, particles in the atmosphere (dust, smoke, pollution) can block sunlight by absorbing or by scattering light. AOD tells us how much direct sunlight is prevented from reaching the ground by these aerosol particles. CAMS EAC4 Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can also be referred to as total ozone, or vertically integrated ozone. The values are dominated by ozone within the stratosphere. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. In the IFS, the units for total ozone are kilograms per square metre, but before 12/06/2001 dobson units were used. Dobson units (DU) are still used extensively for total column ozone. 1 DU = 2.1415E-5 kg m-2. This variable is composed of ERA5 data for 1979-2002, CAMS data for 2003-2019, ERA5 data for 2020-present. ERA5, CAMS EAC4 Volcanic eruptions Dimensionless Volcanic eruptions. Only volcanic eruptions with the volcanic explosivity index (VEI) of 4 and above are included. Such eruptions release a tephra volume of at least 0.1 km3 (0.024 cu mi) and above with substantial effects on the surrounding area. An increase of 1 VEI index indicates an eruption that is 10 times as powerful. Each volcanic eruption is presented in the format NAME (start date, end date), where start and end dates are given as YYYY-MM-DD INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Stratospheric temperature at 100 hPa oC This parameter is the temperature of the atmosphere at 100 hPa. ERA5 Stratospheric temperature at 100 hPa oC This parameter is the temperature of the atmosphere at 100 hPa. ERA5 ERA5 Stratospheric temperature at 50 hPa oC This parameter is the temperature of the atmosphere at 50 hPa. ERA5 Stratospheric temperature at 50 hPa oC This parameter is the temperature of the atmosphere at 50 hPa. ERA5 ERA5 Surface air temperature oC This variable is the temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. ERA5 Surface air temperature oC This variable is the temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. ERA5 ERA5 Total aerosol optical depth at 550nm ~ Aerosol optical depth is a measure of the extinction of the solar beam by dust and haze. In other words, particles in the atmosphere (dust, smoke, pollution) can block sunlight by absorbing or by scattering light. AOD tells us how much direct sunlight is prevented from reaching the ground by these aerosol particles. CAMS EAC4 Total aerosol optical depth at 550nm ~ Aerosol optical depth is a measure of the extinction of the solar beam by dust and haze. In other words, particles in the atmosphere (dust, smoke, pollution) can block sunlight by absorbing or by scattering light. AOD tells us how much direct sunlight is prevented from reaching the ground by these aerosol particles. CAMS EAC4 CAMS EAC4 Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can also be referred to as total ozone, or vertically integrated ozone. The values are dominated by ozone within the stratosphere. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. In the IFS, the units for total ozone are kilograms per square metre, but before 12/06/2001 dobson units were used. Dobson units (DU) are still used extensively for total column ozone. 1 DU = 2.1415E-5 kg m-2. This variable is composed of ERA5 data for 1979-2002, CAMS data for 2003-2019, ERA5 data for 2020-present. ERA5, CAMS EAC4 Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can also be referred to as total ozone, or vertically integrated ozone. The values are dominated by ozone within the stratosphere. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. In the IFS, the units for total ozone are kilograms per square metre, but before 12/06/2001 dobson units were used. Dobson units (DU) are still used extensively for total column ozone. 1 DU = 2.1415E-5 kg m-2. This variable is composed of ERA5 data for 1979-2002, CAMS data for 2003-2019, ERA5 data for 2020-present. ERA5, CAMS EAC4 ERA5 CAMS EAC4 Volcanic eruptions Dimensionless Volcanic eruptions. Only volcanic eruptions with the volcanic explosivity index (VEI) of 4 and above are included. Such eruptions release a tephra volume of at least 0.1 km3 (0.024 cu mi) and above with substantial effects on the surrounding area. An increase of 1 VEI index indicates an eruption that is 10 times as powerful. Each volcanic eruption is presented in the format NAME (start date, end date), where start and end dates are given as YYYY-MM-DD Volcanic eruptions Dimensionless Volcanic eruptions. Only volcanic eruptions with the volcanic explosivity index (VEI) of 4 and above are included. Such eruptions release a tephra volume of at least 0.1 km3 (0.024 cu mi) and above with substantial effects on the surrounding area. An increase of 1 VEI index indicates an eruption that is 10 times as powerful. Each volcanic eruption is presented in the format NAME (start date, end date), where start and end dates are given as YYYY-MM-DD OUTPUT VARIABLES Name Units Description Area averaged anomalies Varies Anomalies with respect to a selected climate reference interval, averaged over a selected geographical domain. Climatological period used in this application equals to the data availability period minus 1 year. Global anomalies Varies Global anomalies with respect to a selected climate reference interval, averaged over a time period 6, 12, 18, 24 months from the start of the eruption and over the duration of the volcanic eruption in months. Climatological period used in this application equals to the data availability period minus 1 year. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Area averaged anomalies Varies Anomalies with respect to a selected climate reference interval, averaged over a selected geographical domain. Climatological period used in this application equals to the data availability period minus 1 year. Area averaged anomalies Varies Anomalies with respect to a selected climate reference interval, averaged over a selected geographical domain. Climatological period used in this application equals to the data availability period minus 1 year. Global anomalies Varies Global anomalies with respect to a selected climate reference interval, averaged over a time period 6, 12, 18, 24 months from the start of the eruption and over the duration of the volcanic eruption in months. Climatological period used in this application equals to the data availability period minus 1 year. Global anomalies Varies Global anomalies with respect to a selected climate reference interval, averaged over a time period 6, 12, 18, 24 months from the start of the eruption and over the duration of the volcanic eruption in months. Climatological period used in this application equals to the data availability period minus 1 year. 38 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/iberia-biscay-ireland-coastal-upwelling-index-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=IBI_OMI_CURRENTS_cui Iberia Biscay Ireland Coastal Upwelling Index from Reanalysis DEFINITION The Coastal Upwelling Index (CUI) is computed along the African and the Iberian Peninsula coasts. For each latitudinal point from 27°N to 42°N the Coastal Upwelling Index is defined as the temperature difference between the maximum and minimum temperature in a range of distance from the coast up to 3.5º westwards. 〖CUI〗_lat=max⁡(T_lat )-min⁡(T_lat) A high Coastal Upwelling Index indicates intense upwelling conditions. The index is computed from the following Copernicus Marine products: IBI-MYP: IBI_MULTIYEAR_PHY_005_002 (1993-2019) IBI-NRT: IBI_ANALYSISFORECAST_PHYS_005_001 (2020 onwards) CONTEXT Coastal upwelling process occurs along coastlines as the result of deflection of the oceanic water away from the shore. Such deflection is produced by Ekman transport induced by persistent winds parallel to the coastline (Sverdrup, 1938). When this transported water is forced, the mass balance is maintained by pumping of ascending intermediate water. This water is typically denser, cooler and richer in nutrients. The Iberia-Biscay-Ireland domain contains two well-documented Eastern Boundary Upwelling Ecosystems, they are hosted under the same system known as Canary Current Upwelling System (Fraga, 1981; Hempel, 1982). This system is one of the major coastal upwelling regions of the world and it is produced by the eastern closure of the Subtropical Gyre. The North West African (NWA) coast presents an intense upwelling region that extends from Morocco to south of Senegal, likewise the western coast of the Iberian Peninsula (IP) shows a seasonal upwelling behavior. These two upwelling domains are separated by the presence of the Gulf of Cadiz, where the coastline does not allow the formation of upwelling conditions from 34ºN up to 37ºN. The Copernicus Marine Service Coastal Upwelling Index is defined following the steps of several previous upwelling indices described in literature. More details and full scientific evaluation can be found in the dedicated section in the first issue of the Copernicus Marine Service Ocean State report (Sotillo et al., 2016). CMEMS KEY FINDINGS The NWA coast (latitudes below 34ºN) shows a quite constantlow variability of the periodicity and the intensity of the upwelling, few periods of upwelling intensifications are found in years 1993-1995, and 2003-2004. In the IP coast (latitudes higher than 37ºN) the interannual variability is more remarkable showing years with high upwelling activity (1994, 2004, and 2015-2017) followed by periods of lower activity (1996-1998, 2003, 2011, and 2013). According to the results of the IBI-NRT system, 2020 was a year with weak upwelling in the IP latitudes. While in the NWA coast the upwelling activity was more intense than the average. The analysis of trends in the period 1993-2019 shows significant positive trend of the upwelling index in the IP latitudes. This trend implies an increase of temperature differences between the coastal and offshore waters of approximately 0.02 °C/Year. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00248 https://doi.org/10.48670/moi-00248 39 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-heating-cooling-degree-days https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-heating-cooling-degree-days app-heating-cooling-degree-days This application provides pan-European information relevant to indoor heating and cooling demand in the past, near, mid, and far future. The measures used to quantify the energy demand are heating (HDD) and cooling (CDD) degree days which measure how far the outdoor temperature is from a given indoor comfort temperature. The comfort temperature thresholds considered in this application are 15.5 °C in winter and 22.0 °C in summer. The indicator integrates the difference between the threshold temperature and the daily average, minimum and maximum outdoor temperature. A high HDD number means that the outdoor temperature is well below the winter comfort temperature and that a high amount of energy would subsequently be required to heat up buildings. A high CDD number means that the outdoor temperature is well above the summer comfort temperature and that a high amount of energy would subsequently be required to cool down buildings. The essential atmospheric variables used to calculate HDDs and CDDs are the daily maximum, average and minimum temperatures. ERA5 reanalysis was used to cover the past and present, while nine bias-corrected EURO-CORDEX regional climate models were used for climate projections considering both RCP4.5 and RCP8.5 (Representative Concentration Pathways). This application is highly relevant to the European adaptation community and has been developed to provide an insight and quantify the projected changes in heating and cooling demand due to different climate change scenarios. This application explores the past and future evolution of HDD and CDD across Europe at national (NUTS0), regional (NUTS1) and provincial (NUTS2) level (Nomenclature des Unités Territoriales Statistiques). The H/CDD indicator can be explored annually, seasonally and monthly, and for the RCP4.5 and RCP8.5 future scenarios. The interactive map displays the average H/CDD for an individual year in the past (1979 to present) or a representative 30-year period (1981–2010). For the future projections one can also choose between representative 30-year periods that illustrate the near, mid, and far future. The graphs on the right-hand-side show H/CDD indicators from reanalysis and the past and projected future periods from the climate models. User-selectable parameters User-selectable parameters Year: displays on the interactive map a specific year ranging from 1979 to present. The H/CDD for the specific year is calculated using ERA5 reanalysis data and is not affected by the selected RCP scenario. Time aggregation: displays on the interactive map and graphs a time aggregation that can be Annual, Season or Month. Season/Month: displays on the interactive map and graphs a specific season (Winter, Spring, Summer or Autumn) or month depending on the selected time aggregation. Scenario: displays on the interactive map and graphs a climate projection scenario; RCP4.5 or RCP8.5. HDD or CDD: displays on the interactive map and graphs Heating Degree Days (HDD) or Cooling Degree Days (CDD). Statistic: displays on the interactive map and graphs the Sum providing the cumulated HDD or CDD value over the time aggregation period or Daily average returning the daily average of HDD or CDD over the selected time aggregation period. Reference period: a radio button menu on the interactive map which switches the map between the selected year in the past, a reference period in the past (1981–2010); or the representative 30-year periods that illustrate near- (2011-2040), medium- (2041-2070), far future (2071-2100). Year: displays on the interactive map a specific year ranging from 1979 to present. The H/CDD for the specific year is calculated using ERA5 reanalysis data and is not affected by the selected RCP scenario. Time aggregation: displays on the interactive map and graphs a time aggregation that can be Annual, Season or Month. Annual Season Month Season/Month: displays on the interactive map and graphs a specific season (Winter, Spring, Summer or Autumn) or month depending on the selected time aggregation. Winter Spring Summer Autumn Scenario: displays on the interactive map and graphs a climate projection scenario; RCP4.5 or RCP8.5. RCP4.5 RCP8.5 HDD or CDD: displays on the interactive map and graphs Heating Degree Days (HDD) or Cooling Degree Days (CDD). HDD CDD Statistic: displays on the interactive map and graphs the Sum providing the cumulated HDD or CDD value over the time aggregation period or Daily average returning the daily average of HDD or CDD over the selected time aggregation period. Sum Daily average Reference period: a radio button menu on the interactive map which switches the map between the selected year in the past, a reference period in the past (1981–2010); or the representative 30-year periods that illustrate near- (2011-2040), medium- (2041-2070), far future (2071-2100). INPUT VARIABLES Name Units Description Source 2m temperature K The ambient temperature of air at 2m above the surface of land, sea or inland waters. 3 hourly data was used to calculate daily mean, maximum and minimum temperature. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. ERA5 single levels Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient temperature of air at 2m above the surface of land, sea or inland waters. 3 hourly data was used to calculate daily mean, maximum and minimum temperature. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. ERA5 single levels Bias-corrected CORDEX 2m temperature K The ambient temperature of air at 2m above the surface of land, sea or inland waters. 3 hourly data was used to calculate daily mean, maximum and minimum temperature. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. ERA5 single levels Bias-corrected CORDEX ERA5 single levels ERA5 single levels Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Cooling degree days °C Number of degrees that a day's average temperature is above 22 °C, a threshold for outdoor temperature used to estimate the need for cooling indoors. Heating degree days °C Number of degrees that a day's average temperature is below 15.5 °C, a threshold for outdoor temperature used to estimate the need for heating indoors. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Cooling degree days °C Number of degrees that a day's average temperature is above 22 °C, a threshold for outdoor temperature used to estimate the need for cooling indoors. Cooling degree days °C Number of degrees that a day's average temperature is above 22 °C, a threshold for outdoor temperature used to estimate the need for cooling indoors. Heating degree days °C Number of degrees that a day's average temperature is below 15.5 °C, a threshold for outdoor temperature used to estimate the need for heating indoors. Heating degree days °C Number of degrees that a day's average temperature is below 15.5 °C, a threshold for outdoor temperature used to estimate the need for heating indoors. 40 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-significant-wave-height-extreme http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_VAR_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs Mediterranean Sea Significant Wave Height extreme from Observations Reprocessing DEFINITION The OMI_EXTREME_WAVE_MEDSEA_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). CONTEXT Projections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). CMEMS KEY FINDINGS The mean 99th percentiles showed in the area present a range from 1.5-3.5 in the Gibraltar Strait and Alboran Sea with 0.25-0.6 of standard deviation (std), 2-4m in the East coast of the Iberian Peninsula and Balearic Islands with 0.2-0.4m of std, 3m in the Ligurian Sea with 0.2m of std to 3-5m in the Gulf of Lyon with 0.2-0.5m of std. Results for this year show slight either negative or positive anomalies in the whole area from -0.3m to +0.3m, all of them around the std range. DOI (product):https://doi.org/10.48670/moi-00263 https://doi.org/10.48670/moi-00263 41 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-ocean-heat-content-anomaly-0-300m-time-series http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies Black Sea Ocean Heat Content Anomaly (0-300m) time series and trend from Reanalysis & Multi-Observations Reprocessing DEFINITION Ocean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 300 m depth: OHC=∫_(z_1)^(z_2)ρ_0 c_p (T_m-T_clim )dz [1] with a reference density = 1020 kg m-3 and a specific heat capacity of cp = 4181.3 J kg-1 °C-1 (e.g. von Schuckmann et al., 2009; Lima et al., 2020); T_m corresponds to the monthly average temperature and T_clim is the climatological temperature of the corresponding month that varies according to each individual product. Time series of monthly mean values area averaged ocean heat content is provided for the Black Sea (40.86°N, 46.8°N; 27.32°E, 41.96°E) and is evaluated in areas where the topography is deeper than 300m. The Azov and Marmara Seas are not considered. CONTEXT Knowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the oceans shape our perspectives for the future. The quality evaluation of OMI_CLIMATE_OHC_BLKSEA_area_averaged_anomalies is based on the “multi-product” approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean’s experience (Masina et al., 2017). Three global products and one regional (Black Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are: The Black Sea Reanalysis at 1/36-degree horizontal resolution (BLKSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004, Lima et al., 2021) A global reanalysis at 1/12 degree horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031). Two observation-based products: CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) and ARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Details on the products are delivered in the PUM and QUID of this OMI. https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004 CMEMS KEY FINDINGS Since 2005 the Black Sea experienced a continuous increase in ocean heat content (0-300 m), and record OHC values are noticed in recent years. The Black Sea is warming at a rate of 1.00±0.09 W m-2, which is higher than the global average warming rate Previous studies also detected a warming in the Black Sea (Akpinar et al., 2017; Stanev et al. 2019; Lima et al. 2020). Stanev et al. (2019) found a warming trend in the cold intermediate layer (CIL) of ~0.05 oC year-1 in recent years. The increase in ocean heat content weakens the CIL, whereas its decreasing favors the CIL restoration (Akpinar et al., 2017). The years 2012 and 2017 exhibited a more evident warming interruption that induced a replenishment of the CIL (Lima et al. 2021). DOI (product):  https://doi.org/10.48670/moi-00306 https://doi.org/10.48670/moi-00306 42 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-surface-temperature-extreme-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_VAR_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs Baltic Sea Surface Temperature extreme from Observations Reprocessing DEFINITION The OMI_VAR_EXTREME_SST_BALTIC_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). CONTEXT Sea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years. An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial. The Baltic Sea has showed in the last two decades a warming trend across the whole basin. This trend is significantly higher when considering only the summer season, which would affect the high extremes (e.g. Høyer and Karagali, 2016). CMEMS KEY FINDINGS The mean 99th percentiles showed in the area go from 18.7ºC in Slipshavn to 21.2ºC around the Zealand Region, and the standard deviation ranges between 1ºC and 2ºC. Results for this year show a slight positive anomaly in the central Baltic Sea below +0.5ºC, a moderate positive anomaly in Bomholm and Arkona Basins, reaching +2.8ºC in Slipshavn, and a negative anomaly in the Southwest of Zealand Region (Bay of Mecklenburg, Kiel Basin and South of Great Belt) between -1.5ºC and -2.5ºC. DOI (product):https://doi.org/10.48670/moi-00204 https://doi.org/10.48670/moi-00204 43 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-heat-content-0-700m-reanalysis-multi http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_OHC_area_averaged_anomalies_0_700 Global Ocean Heat Content (0-700m) from Reanalysis & Multi-Observations Reprocessing DEFINITION Estimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60°S-60°N aiming i) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change. ii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). Ocean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017). CONTEXT Knowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019). CMEMS KEY FINDINGS Since the year 2005, the upper (0-700m) near-global (60°S-60°N) ocean warms at a rate of 0.6 ± 0.1 W/m2. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00234 https://doi.org/10.48670/moi-00234 44 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-heat-content-0-2000m-time-series-and-trend http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_OHC_area_averaged_anomalies_0_2000 Global Ocean Heat Content (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing DEFINITION Estimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60°S-60°N aiming i) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change. ii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). Ocean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017). CONTEXT Knowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019). CMEMS KEY FINDINGS Since the year 2005, the upper (0-2000m) near-global (60°S-60°N) ocean warms at a rate of 1.0 ± 0.1 W/m2. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00235 https://doi.org/10.48670/moi-00235 45 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/projections-cmip5-daily-single-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip5-daily-single-levels projections-cmip5-daily-single-levels This catalogue entry provides daily climate projections on single levels from a large number of experiments, models, members and time periods computed in the framework of the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The term "single levels" is used to express that the variables are computed at one vertical level which can be surface (or a level close to the surface) or a dedicated pressure level in the atmosphere. Multiple vertical levels are excluded from this catalogue entry. CMIP5 data are used extensively in the Intergovernmental Panel on Climate Change Assessment Reports (the latest one is IPCC AR5, which was published in 2014). The use of these data is mostly aimed at: addressing outstanding scientific questions that arose as part of the IPCC reporting process; improving the understanding of the climate system; providing estimates of future climate change and related uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different models are in simulating the recent past. addressing outstanding scientific questions that arose as part of the IPCC reporting process; improving the understanding of the climate system; providing estimates of future climate change and related uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different models are in simulating the recent past. The term "experiments" refers to the three main categories of CMIP5 simulations: Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2005.; Ensemble of experiments from the Atmospheric Model Intercomparison Project (AMIP), which prescribes the oceanic variables for all models and during all period of the experiment. This configuration removes the added complexity of ocean-atmosphere feedbacks in the climate system. The period covered is typically 1950-2005. Ensemble of climate projection experiments following the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. The RCP scenarios provide different pathways of the future climate forcing. The period covered is typically, 2006-2100 some extended RCP experimental data is available from 2100-2300. Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2005.; Ensemble of experiments from the Atmospheric Model Intercomparison Project (AMIP), which prescribes the oceanic variables for all models and during all period of the experiment. This configuration removes the added complexity of ocean-atmosphere feedbacks in the climate system. The period covered is typically 1950-2005. Ensemble of climate projection experiments following the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. The RCP scenarios provide different pathways of the future climate forcing. The period covered is typically, 2006-2100 some extended RCP experimental data is available from 2100-2300. In CMIP5, the same experiments were run using different GCMs. In addition, for each model, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. Note that CMIP5 GCM data can be also used as lateral boundary conditions for Regional Climate Models (RCMs). RCMs are also available in the CDS (see CORDEX datasets). The data are produced by the participating institutes of the CMIP5 project. The latest CMIP GCM experiments will form the CMIP6 dataset, which will be published in the CDS in a later stage. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution From 0.125° x 0.125° to 5° x 5° depending on the model Vertical coverage Variables are provided in one single level, which may differ among variables Temporal coverage From 1850 to 2300 (shorter for some experiments) Temporal resolution Day File format NetCDF DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution From 0.125° x 0.125° to 5° x 5° depending on the model Horizontal resolution From 0.125° x 0.125° to 5° x 5° depending on the model Vertical coverage Variables are provided in one single level, which may differ among variables Vertical coverage Variables are provided in one single level, which may differ among variables Temporal coverage From 1850 to 2300 (shorter for some experiments) Temporal coverage From 1850 to 2300 (shorter for some experiments) Temporal resolution Day Temporal resolution Day File format NetCDF File format NetCDF MAIN VARIABLES Name Units Description 10m wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. 2m temperature K Temperature of the air near the surface. Daily near surface relative humidity % Amount of moisture in the air near the surface divided by the maximum amount of moisture that could exist in the air at a specific temperature and location. Maximum 2m temperature in the last 24 hours K Daily maximum near-surface air temperature. Mean precipitation flux kg m-2 s-1 Amount of water per unit area and time. Mean sea level pressure Pa Time average of the air pressure at sea level. Minimum 2m temperature in the last 24 hours K Daily minimum near-surface air temperature. Near surface specific humidity Dimensionless Amount of moisture in the air near the surface divided by amount of air plus moist at that location. Snowfall kg m-2 s-1 Mass of water in the form of snow precipitating per unit area. Surface solar radiation downwards W m-2 Radiative shortwave flux of energy downward at the surface. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 10m wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. 10m wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. 2m temperature K Temperature of the air near the surface. 2m temperature K Temperature of the air near the surface. Daily near surface relative humidity % Amount of moisture in the air near the surface divided by the maximum amount of moisture that could exist in the air at a specific temperature and location. Daily near surface relative humidity % Amount of moisture in the air near the surface divided by the maximum amount of moisture that could exist in the air at a specific temperature and location. Maximum 2m temperature in the last 24 hours K Daily maximum near-surface air temperature. Maximum 2m temperature in the last 24 hours K Daily maximum near-surface air temperature. Mean precipitation flux kg m-2 s-1 Amount of water per unit area and time. Mean precipitation flux kg m-2 s-1 Amount of water per unit area and time. Mean sea level pressure Pa Time average of the air pressure at sea level. Mean sea level pressure Pa Time average of the air pressure at sea level. Minimum 2m temperature in the last 24 hours K Daily minimum near-surface air temperature. Minimum 2m temperature in the last 24 hours K Daily minimum near-surface air temperature. Near surface specific humidity Dimensionless Amount of moisture in the air near the surface divided by amount of air plus moist at that location. Near surface specific humidity Dimensionless Amount of moisture in the air near the surface divided by amount of air plus moist at that location. Snowfall kg m-2 s-1 Mass of water in the form of snow precipitating per unit area. Snowfall kg m-2 s-1 Mass of water in the form of snow precipitating per unit area. Surface solar radiation downwards W m-2 Radiative shortwave flux of energy downward at the surface. Surface solar radiation downwards W m-2 Radiative shortwave flux of energy downward at the surface. 46 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mean-volume-transport-across-sections-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_WMHE_voltrp Mean Volume Transport across sections from Reanalysis DEFINITION Volume transport across lines are obtained by integrating the volume fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models’ daily output. The mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in Sverdrup (Sv). CONTEXT The ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth’s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth’s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. CMEMS KEY FINDINGS The mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. At Drake Passage, the multi-product approach (product no. 2.4.1) is larger than the value (130 Sv) of Lumpkin and Speer (2007), but smaller than the new observational based results of Colin de Verdière and Ollitrault, (2016) (175 Sv) and Donohue (2017) (173.3 Sv). Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00247 https://doi.org/10.48670/moi-00247 47 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-west-shelf-significant-wave-height-extreme http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_VAR_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs North West Shelf Significant Wave Height extreme from Observations Reprocessing DEFINITION The OMI_EXTREME_WAVE_NORTHWESTSHELF_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). CONTEXT Projections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). CMEMS KEY FINDINGS The mean 99th percentiles showed in the area present a wide range from 2.5 meters in the English Channel with 0.3m of standard deviation (std), 3-5m around Helgoland Bight with 0.3-0.5m of std, 4 meters in the Skagerrak Strait with 0.5m of std, 6m in the central North Sea with 0.3m of std to 8-9 meters in the North of the British Isles with 0.5-0.75m of std. Results for this year show a general trend of positive anomalies with slight or moderate values around the range of the std in all the area except in the North of the British Isles where the positive anomaly is appreciable, reaching +1m. Severe storms developed in the Atlantic during 2020 reached the British Isles, like Storm Brendan in January, Storm Dennis in February or Storm Bella in December. These storms produced waves with significant wave height over 10 m recorded by the buoys in the area. DOI (product):https://doi.org/10.48670/moi-00270 https://doi.org/10.48670/moi-00270 48 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-west-shelf-sea-surface-temperature-extreme-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=NORTHWESTSHELF_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly North West Shelf Sea Surface Temperature extreme from Reanalysis DEFINITION The CMEMS NORTHWESTSHELF_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The North-West Shelf Multi Year Product (NWSHELF_MULTIYEAR_PHY_004_009) and the Analysis product (NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013). Two parameters are included on this OMI: * Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2019). * Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile. This indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019). CONTEXT This domain comprises the North West European continental shelf where depths do not exceed 200m and deeper Atlantic waters to the North and West. For these deeper waters, the North-South temperature gradient dominates (Liu and Tanhua, 2021). Temperature over the continental shelf is affected also by the various local currents in this region and by the shallow depth of the water (Elliott et al., 1990). Atmospheric heat waves can warm the whole water column, especially in the southern North Sea, much of which is no more than 30m deep (Holt et al., 2012). Warm summertime water observed in the Norwegian trench is outflow heading North from the Baltic Sea and from the North Sea itself. CMEMS KEY FINDINGS The 99th percentile SST product can be considered to represent approximately the warmest 4 days for the sea surface in Summer. Maximum anomalies for 2020 are up to 4oC warmer than the 1993-2019 average in the western approaches, Celtic and Irish Seas, English Channel and the southern North Sea. For the atmosphere, Summer 2020 was exceptionally warm and sunny in southern UK (Kendon et al., 2021), with heatwaves in June and August. Further north in the UK, the atmosphere was closer to long-term average temperatures. Overall, the 99th percentile SST anomalies show a similar pattern, with the exceptional warm anomalies in the south of the domain. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product)https://doi.org/10.48670/moi-00273 https://doi.org/10.48670/moi-00273 49 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-ghg-reanalysis-egg4 https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-ghg-reanalysis-egg4 cams-global-ghg-reanalysis-egg4 This dataset is part of the ECMWF Atmospheric Composition Reanalysis focusing on long-lived greenhouse gases: carbon dioxide (CO2) and methane (CH4). The emissions and natural fluxes at the surface are crucial for the evolution of the long-lived greenhouse gases in the atmosphere. In this dataset the CO2 fluxes from terrestrial vegetation are modelled in order to simulate the variability across a wide range of scales from diurnal to inter-annual. The CH4 chemical loss is represented by a climatological loss rate and the emissions at the surface are taken from a range of datasets. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. The assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available. The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with. The observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. For this reason, EAC4 is only available from 2003 onwards. The analysis procedure assimilates data in a window of 12 hours using the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window. These data are available in 3-hourly resolution, worldwide. Monthly means can be accessed at: https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-ghg-reanalysis-egg4-monthly https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-ghg-reanalysis-egg4-monthly More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 0.75°x0.75° Vertical coverage Surface, total column, model levels and pressure levels Vertical resolution 60 model levels. Pressure levels: 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa Temporal coverage 2003 to 2020 Temporal resolution 3-hourly File format GRIB, NetCDF Versions Only one version Update frequency Twice a year with 6 month delay DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.75°x0.75° Horizontal resolution 0.75°x0.75° Vertical coverage Surface, total column, model levels and pressure levels Vertical coverage Surface, total column, model levels and pressure levels Vertical resolution 60 model levels. Pressure levels: 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa Vertical resolution 60 model levels. Pressure levels: 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa Temporal coverage 2003 to 2020 Temporal coverage 2003 to 2020 Temporal resolution 3-hourly Temporal resolution 3-hourly File format GRIB, NetCDF File format GRIB, NetCDF Versions Only one version Versions Only one version Update frequency Twice a year with 6 month delay Update frequency Twice a year with 6 month delay MAIN VARIABLES Name Units 10m u-component of wind m s-1 10m v-component of wind m s-1 2m dewpoint temperature K 2m temperature K Accumulated carbon dioxide ecosystem respiration kg m-2 Accumulated carbon dioxide gross primary production kg m-2 Accumulated carbon dioxide net ecosystem exchange kg m-2 Anthropogenic emissions of carbon dioxide kg m-2 s-1 Boundary layer height m CH4 column-mean molar fraction ppb CO2 column-mean molar fraction ppm Carbon dioxide kg kg-1 Convective available potential energy J kg-1 Convective inhibition J kg-1 Convective precipitation m Downward UV radiation at the surface J m-2 Evaporation m of water equivalent Flux of carbon dioxide ecosystem respiration kg m-2 s-1 Flux of carbon dioxide gross primary production kg m-2 s-1 Flux of carbon dioxide net ecosystem exchange kg m-2 s-1 Forecast albedo (0 - 1) Fraction of cloud cover (0 - 1) GPP coefficient from biogenic flux adjustment system dimensionless Geopotential m2 s-2 High cloud cover (0 - 1) Land-sea mask (0 - 1) Large-scale precipitation m Logarithm of surface pressure ~ Low cloud cover (0 - 1) Mean sea level pressure Pa Medium cloud cover (0 - 1) Methane kg kg-1 Methane loss rate due to radical hydroxyl (OH) s-1 Methane surface fluxes kg m-2 s-1 Ocean flux of carbon dioxide kg m-2 s-1 Photosynthetically active radiation at the surface J m-2 Potential evaporation m Potential vorticity K m2 kg-1 s-1 Precipitation type dimensionless Rec coefficient from biogenic flux adjustment system dimensionless Relative humidity % Sea surface temperature K Sea-ice cover (0 - 1) Skin reservoir content m of water equivalent Skin temperature K Snow albedo (0 - 1) Snow depth m of water equivalent Specific cloud ice water content kg kg-1 Specific cloud liquid water content kg kg-1 Specific humidity kg kg-1 Specific rain water content kg kg-1 Specific snow water content kg kg-1 Sunshine duration s Surface Geopotential m2 s-2 Surface latent heat flux J m-2 Surface net solar radiation J m-2 Surface net solar radiation, clear sky J m-2 Surface net thermal radiation J m-2 Surface net thermal radiation, clear sky J m-2 Surface sensible heat flux J m-2 Surface solar radiation downward, clear sky J m-2 Surface solar radiation downwards J m-2 Surface thermal radiation downward, clear sky J m-2 Surface thermal radiation downwards J m-2 TOA incident solar radiation J m-2 Temperature K Top net solar radiation J m-2 Top net solar radiation, clear sky J m-2 Top net thermal radiation J m-2 Top net thermal radiation, clear sky J m-2 Total cloud cover (0 - 1) Total column cloud ice water kg m-2 Total column cloud liquid water kg m-2 Total column water kg m-2 Total column water vapour kg m-2 Total precipitation m U-component of wind m s-1 V-component of wind m s-1 Vertical velocity Pa s-1 Visibility m Wildfire flux of carbon dioxide kg m-2 s-1 Wildfire flux of methane kg m-2 s-1 MAIN VARIABLES MAIN VARIABLES Name Units Name Units 10m u-component of wind m s-1 10m u-component of wind m s-1 10m v-component of wind m s-1 10m v-component of wind m s-1 2m dewpoint temperature K 2m dewpoint temperature K 2m temperature K 2m temperature K Accumulated carbon dioxide ecosystem respiration kg m-2 Accumulated carbon dioxide ecosystem respiration kg m-2 Accumulated carbon dioxide gross primary production kg m-2 Accumulated carbon dioxide gross primary production kg m-2 Accumulated carbon dioxide net ecosystem exchange kg m-2 Accumulated carbon dioxide net ecosystem exchange kg m-2 Anthropogenic emissions of carbon dioxide kg m-2 s-1 Anthropogenic emissions of carbon dioxide kg m-2 s-1 Boundary layer height m Boundary layer height m CH4 column-mean molar fraction ppb CH4 column-mean molar fraction ppb CO2 column-mean molar fraction ppm CO2 column-mean molar fraction ppm Carbon dioxide kg kg-1 Carbon dioxide kg kg-1 Convective available potential energy J kg-1 Convective available potential energy J kg-1 Convective inhibition J kg-1 Convective inhibition J kg-1 Convective precipitation m Convective precipitation m Downward UV radiation at the surface J m-2 Downward UV radiation at the surface J m-2 Evaporation m of water equivalent Evaporation m of water equivalent Flux of carbon dioxide ecosystem respiration kg m-2 s-1 Flux of carbon dioxide ecosystem respiration kg m-2 s-1 Flux of carbon dioxide gross primary production kg m-2 s-1 Flux of carbon dioxide gross primary production kg m-2 s-1 Flux of carbon dioxide net ecosystem exchange kg m-2 s-1 Flux of carbon dioxide net ecosystem exchange kg m-2 s-1 Forecast albedo (0 - 1) Forecast albedo (0 - 1) Fraction of cloud cover (0 - 1) Fraction of cloud cover (0 - 1) GPP coefficient from biogenic flux adjustment system dimensionless GPP coefficient from biogenic flux adjustment system dimensionless Geopotential m2 s-2 Geopotential m2 s-2 High cloud cover (0 - 1) High cloud cover (0 - 1) Land-sea mask (0 - 1) Land-sea mask (0 - 1) Large-scale precipitation m Large-scale precipitation m Logarithm of surface pressure ~ Logarithm of surface pressure ~ Low cloud cover (0 - 1) Low cloud cover (0 - 1) Mean sea level pressure Pa Mean sea level pressure Pa Medium cloud cover (0 - 1) Medium cloud cover (0 - 1) Methane kg kg-1 Methane kg kg-1 Methane loss rate due to radical hydroxyl (OH) s-1 Methane loss rate due to radical hydroxyl (OH) s-1 Methane surface fluxes kg m-2 s-1 Methane surface fluxes kg m-2 s-1 Ocean flux of carbon dioxide kg m-2 s-1 Ocean flux of carbon dioxide kg m-2 s-1 Photosynthetically active radiation at the surface J m-2 Photosynthetically active radiation at the surface J m-2 Potential evaporation m Potential evaporation m Potential vorticity K m2 kg-1 s-1 Potential vorticity K m2 kg-1 s-1 Precipitation type dimensionless Precipitation type dimensionless Rec coefficient from biogenic flux adjustment system dimensionless Rec coefficient from biogenic flux adjustment system dimensionless Relative humidity % Relative humidity % Sea surface temperature K Sea surface temperature K Sea-ice cover (0 - 1) Sea-ice cover (0 - 1) Skin reservoir content m of water equivalent Skin reservoir content m of water equivalent Skin temperature K Skin temperature K Snow albedo (0 - 1) Snow albedo (0 - 1) Snow depth m of water equivalent Snow depth m of water equivalent Specific cloud ice water content kg kg-1 Specific cloud ice water content kg kg-1 Specific cloud liquid water content kg kg-1 Specific cloud liquid water content kg kg-1 Specific humidity kg kg-1 Specific humidity kg kg-1 Specific rain water content kg kg-1 Specific rain water content kg kg-1 Specific snow water content kg kg-1 Specific snow water content kg kg-1 Sunshine duration s Sunshine duration s Surface Geopotential m2 s-2 Surface Geopotential m2 s-2 Surface latent heat flux J m-2 Surface latent heat flux J m-2 Surface net solar radiation J m-2 Surface net solar radiation J m-2 Surface net solar radiation, clear sky J m-2 Surface net solar radiation, clear sky J m-2 Surface net thermal radiation J m-2 Surface net thermal radiation J m-2 Surface net thermal radiation, clear sky J m-2 Surface net thermal radiation, clear sky J m-2 Surface sensible heat flux J m-2 Surface sensible heat flux J m-2 Surface solar radiation downward, clear sky J m-2 Surface solar radiation downward, clear sky J m-2 Surface solar radiation downwards J m-2 Surface solar radiation downwards J m-2 Surface thermal radiation downward, clear sky J m-2 Surface thermal radiation downward, clear sky J m-2 Surface thermal radiation downwards J m-2 Surface thermal radiation downwards J m-2 TOA incident solar radiation J m-2 TOA incident solar radiation J m-2 Temperature K Temperature K Top net solar radiation J m-2 Top net solar radiation J m-2 Top net solar radiation, clear sky J m-2 Top net solar radiation, clear sky J m-2 Top net thermal radiation J m-2 Top net thermal radiation J m-2 Top net thermal radiation, clear sky J m-2 Top net thermal radiation, clear sky J m-2 Total cloud cover (0 - 1) Total cloud cover (0 - 1) Total column cloud ice water kg m-2 Total column cloud ice water kg m-2 Total column cloud liquid water kg m-2 Total column cloud liquid water kg m-2 Total column water kg m-2 Total column water kg m-2 Total column water vapour kg m-2 Total column water vapour kg m-2 Total precipitation m Total precipitation m U-component of wind m s-1 U-component of wind m s-1 V-component of wind m s-1 V-component of wind m s-1 Vertical velocity Pa s-1 Vertical velocity Pa s-1 Visibility m Visibility m Wildfire flux of carbon dioxide kg m-2 s-1 Wildfire flux of carbon dioxide kg m-2 s-1 Wildfire flux of methane kg m-2 s-1 Wildfire flux of methane kg m-2 s-1 50 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-european-risk-flood-indicators https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-risk-flood-indicators sis-european-risk-flood-indicators The dataset presents indicators to evaluate the spatial distribution of flood risk in terms of hazards and direct damages. It is provided as a high-resolution product focused on 20 European cities that were identified as vulnerable to urban pluvial flooding by experts and practitioners from government agencies and Civil Protection. The dataset combines a high-resolution, probabilistic, description of extreme precipitation, exposure datasets and damage/vulnerability models into a comprehensive pluvial flood risk assessment for cities across Europe for the current climate. It allows city stakeholders to exploit flood risk analysis over the city. The dataset is derived from data available on the Climate Data Store and the Copernicus Land Monitoring Service (CLMS). The former includes ERA5 reanalysis data, dynamically downscaled to 2km x 2km grid with the regional climate model COSMO-CLM and accounting for urban parameterization in order to reach the spatial and temporal resolution suitable for pluvial flood analysis at a city scale. This downscaled product is used for deriving hourly precipitation input at prescribed recurrence intervals that, in combination with supporting digital elevation models (DEM) from the CLMS, is used to feed hazard and damage models. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage Europe Horizontal resolution EU-DEM: 25m x 25m Lidar-derived: 1m x 1m & 2m x 2m (depending on city) Vertical coverage Surface Vertical resolution Single level Temporal coverage 1989-2018 Temporal resolution 30-years File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions 1.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Europe Horizontal coverage Europe Horizontal resolution EU-DEM: 25m x 25m Lidar-derived: 1m x 1m & 2m x 2m (depending on city) Horizontal resolution EU-DEM: 25m x 25m Lidar-derived: 1m x 1m & 2m x 2m (depending on city) EU-DEM: 25m x 25m Lidar-derived: 1m x 1m & 2m x 2m (depending on city) Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage 1989-2018 Temporal coverage 1989-2018 Temporal resolution 30-years Temporal resolution 30-years File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions 1.0 Versions 1.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Expected damage €/m2 Direct damage on existing assets due to pluvial flooding induced by hourly precipitation events for fixed return periods computed over 30-years (1989-2018) for the 20 selected European cities. The computed return periods include: 5, 10, 25, 50, 100-years. Urban depression Dimensionless Topographic depressions which are defined as areas without an outlet and often referred to as sinks or pits. Water depth m Pluvial flood water depth induced by hourly precipitation events for fixed return periods computed over 30-years (1989-2018) for the 20 selected European cities. The computed return periods include: 5, 10, 25, 50, 100-years. Water mask Dimensionless Mask of flooded areas. The values are between 0 (not affected) and 1 (affected) and are function of hourly precipitation events for fixed return periods computed over 30-years (1989-2018) for the 20 selected European cities. The computed return periods include: 5, 10, 25, 50, 100-years. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Expected damage €/m2 Direct damage on existing assets due to pluvial flooding induced by hourly precipitation events for fixed return periods computed over 30-years (1989-2018) for the 20 selected European cities. The computed return periods include: 5, 10, 25, 50, 100-years. Expected damage €/m2 Direct damage on existing assets due to pluvial flooding induced by hourly precipitation events for fixed return periods computed over 30-years (1989-2018) for the 20 selected European cities. The computed return periods include: 5, 10, 25, 50, 100-years. Urban depression Dimensionless Topographic depressions which are defined as areas without an outlet and often referred to as sinks or pits. Urban depression Dimensionless Topographic depressions which are defined as areas without an outlet and often referred to as sinks or pits. Water depth m Pluvial flood water depth induced by hourly precipitation events for fixed return periods computed over 30-years (1989-2018) for the 20 selected European cities. The computed return periods include: 5, 10, 25, 50, 100-years. Water depth m Pluvial flood water depth induced by hourly precipitation events for fixed return periods computed over 30-years (1989-2018) for the 20 selected European cities. The computed return periods include: 5, 10, 25, 50, 100-years. Water mask Dimensionless Mask of flooded areas. The values are between 0 (not affected) and 1 (affected) and are function of hourly precipitation events for fixed return periods computed over 30-years (1989-2018) for the 20 selected European cities. The computed return periods include: 5, 10, 25, 50, 100-years. Water mask Dimensionless Mask of flooded areas. The values are between 0 (not affected) and 1 (affected) and are function of hourly precipitation events for fixed return periods computed over 30-years (1989-2018) for the 20 selected European cities. The computed return periods include: 5, 10, 25, 50, 100-years. 51 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-west-shelf-sea-surface-temperature-extreme http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_VAR_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs North West Shelf Sea Surface Temperature extreme from Observations Reprocessing DEFINITION The OMI_VAR_EXTREME_SST_NORTHWESTSHELF_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). CONTEXT Sea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years. An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial. The North-West Self area comprises part of the North Atlantic, where this refreshing trend has been observed, and the North Sea, where a warming trend has been taking place in the last three decades (e.g. Høyer and Karagali, 2016). CMEMS KEY FINDINGS The mean 99th percentiles showed in the area present a range from 14ºC in the Northwest of the British Isles, 15.6ºC in the North of the North Sea (Heimdal Station), 18ºC in the English Channel to 20-21ºC around Denmark (Helgoland Bight, Skagerrak and Kattegat Seas). The standard deviation ranges from 0.5ºC in the English Channel and 0.8/0.9ºC in the Northwest of the British Isles and Heimdal Station to 1.0/1.7ºC in the buoys around Denmark. Results for this year show either positive (+1.3ºC in Helgoland Bight) or negative (-0.6ºC in the North West of the British Isles) anomalies around their corresponding standard deviation in all the area, except in Aarhus station in the North East of Zealand Island where the negative anomaly reaches -2.0ºC in concordance with the negative anomalies found in the Zealand Region in the Baltic OMI. DOI (product):https://doi.org/10.48670/moi-00274 https://doi.org/10.48670/moi-00274 52 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/projections-cordex-domains-single-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels projections-cordex-domains-single-levels This catalogue entry provides Regional Climate Model (RCM) data on single levels from a number of experiments, models, domains, resolutions, ensemble members, time frequencies and periods computed over several regional domains all over the World in the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX). The term "single levels" is used to express that the variables are 2D-matrices computed on one vertical level which can be surface (or a level close to the surface) or a dedicated pressure level in the atmosphere. Multiple vertical levels are excluded from this catalogue entry. High-resolution Regional Climate Models (RCMs) can provide climate change information on regional and local scales in relatively fine detail, which cannot be obtained from coarse scale Global Climate Models (GCMs). This is manifested in better description of small-scale regional climate characteristics and also in more accurate representation of extreme events. Consequently, outputs of such RCMs are indispensable in supporting regional and local climate impact studies and adaptation decisions. RCMs are not independent from the GCMs, since the GCMs provide lateral and lower boundary conditions to the regional models. In that sense RCMs can be viewed as magnifying glasses of the GCMs. The CORDEX experiments consist of RCM simulations representing different future socio-economic scenarios (forcings), different combinations of GCMs and RCMs and different ensemble members of the same GCM-RCM combinations. This experiment design through the ensemble members allows for studies addressing questions related to the key uncertainties in future climate change. These uncertainties come from differences in the scenarios of future socio-economic development, the imperfection of regional and global models used and the internal (natural) variability of the climate system. This experiment design allows for studies addressing questions related to the key uncertainties in future climate change: what will future climate forcing be? what will be the response of the climate system to changes in forcing? what is the uncertainty related to natural variability of the climate system? what will future climate forcing be? what will be the response of the climate system to changes in forcing? what is the uncertainty related to natural variability of the climate system? The term "experiment" in the CDS form refers to three main categories: Evaluation: CORDEX experiment driven by ECMWF ERA-Interim reanalysis for a past period. These experiments can be used to evaluate the quality of the RCMs using perfect boundary conditions as provided by a reanalysis system. The period covered is typically 1980-2010; Historical: CORDEX experiment which covers a period for which modern climate observations exist. Boundary conditions are provided by GCMs. These experiments, that follow the observed changes in climate forcing, show how the RCMs perform for the past climate when forced by GCMs and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1950-2005; Scenario: Ensemble of CORDEX climate projection experiments using RCP (Representative Concentration Pathways) forcing scenarios. These scenarios are the RCP 2.6, 4.5 and 8.5 scenarios providing different pathways of the future climate forcing. Boundary conditions are provided by GCMs. The period covered is typically 2006-2100. Evaluation: CORDEX experiment driven by ECMWF ERA-Interim reanalysis for a past period. These experiments can be used to evaluate the quality of the RCMs using perfect boundary conditions as provided by a reanalysis system. The period covered is typically 1980-2010; Evaluation Historical: CORDEX experiment which covers a period for which modern climate observations exist. Boundary conditions are provided by GCMs. These experiments, that follow the observed changes in climate forcing, show how the RCMs perform for the past climate when forced by GCMs and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1950-2005; Historical Scenario: Ensemble of CORDEX climate projection experiments using RCP (Representative Concentration Pathways) forcing scenarios. These scenarios are the RCP 2.6, 4.5 and 8.5 scenarios providing different pathways of the future climate forcing. Boundary conditions are provided by GCMs. The period covered is typically 2006-2100. Scenario In CORDEX, the same experiments were done using different RCMs (labelled as “Regional Climate Model” in the CDS form). In addition, for each RCM, there is a variety of GCMs, which can be used as lateral boundary conditions. The GCMs used are coming from the CMIP5 (5th phase of the Coupled Model Intercomparison Project) archive. These GCM boundary conditions are labelled as “Global Climate Model” in the form and are also available in the CDS. Additionally, the uncertainty related to internal variability of the climate system is sampled by running several simulations with the same RCM-GCM combination. On the forms, these are indexed as separate ensemble members (the naming convention for ensemble members is available in the documentation). For each GCM, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. More details behind these sequential ensemble numbers is available in the detailed documentation. The data are produced by the institutes and modelling centres participating in the different CORDEX domains with partial support from different international and national contributions including support from COPERNICUS for some of the EURO-CORDEX runs. The data can be used for commercial purposes (unrestricted use) with the exception of the simulations from the following RCMs: BOUN-RegCM4-3 model (for Central Asia and Middle East and North Africa domains) and RU-CORE-RegCM4-3 model (for South-East Asia domain). Precise terms of use are provided in the CORDEX licence. DATA DESCRIPTION Data type Gridded Horizontal coverage 14 CORDEX domains Horizontal resolution African domain: 0.22°x0.22° and 0.44°x0.44° Antarctica domain: 0.44°x0.44° Arctic domain: 0.22°x0.22° and 0.44°x0.44° Australasian domain: 0.22°x0.22° and 0.44°x0.44° Central American domain: 0.22°x0.22° and 0.44°x0.44° Central Asian domain: 0.22°x0.22° and 0.44°x0.44° East Asian domain: 0.22°x0.22° and 0.44°x0.44° European domain: 0.11°x0.11° Mediterranean domain: 0.11°x0.11° and 0.44°x0.44° Middle East and North African domain: 0.22°x0.22° and 0.44°x0.44° North American domain: 0.22°x0.22° and 0.44°x0.44° South American domain: 0.20° x 0.20°, 0.22° x 0.22° and 0.44° x 0.44° South Asian domain: 0.22°x0.22° and 0.44°x0.44° South-East Asian domain: 0.22°x0.22° Vertical resolution Variables are provided at a specific single level for each variable. That level may differ among the variables. Temporal coverage From 1989 to 2008 for evaluation experiments From 1951 to 2005 for historical experiments From 2006 to 2100 for RCP experiments Temporal resolution 3h, 6h, daily, monthly and seasonal File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions Latest version of the data is provided. Update frequency Quarterly DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage 14 CORDEX domains Horizontal coverage 14 CORDEX domains Horizontal resolution African domain: 0.22°x0.22° and 0.44°x0.44° Antarctica domain: 0.44°x0.44° Arctic domain: 0.22°x0.22° and 0.44°x0.44° Australasian domain: 0.22°x0.22° and 0.44°x0.44° Central American domain: 0.22°x0.22° and 0.44°x0.44° Central Asian domain: 0.22°x0.22° and 0.44°x0.44° East Asian domain: 0.22°x0.22° and 0.44°x0.44° European domain: 0.11°x0.11° Mediterranean domain: 0.11°x0.11° and 0.44°x0.44° Middle East and North African domain: 0.22°x0.22° and 0.44°x0.44° North American domain: 0.22°x0.22° and 0.44°x0.44° South American domain: 0.20° x 0.20°, 0.22° x 0.22° and 0.44° x 0.44° South Asian domain: 0.22°x0.22° and 0.44°x0.44° South-East Asian domain: 0.22°x0.22° Horizontal resolution African domain: 0.22°x0.22° and 0.44°x0.44° Antarctica domain: 0.44°x0.44° Arctic domain: 0.22°x0.22° and 0.44°x0.44° Australasian domain: 0.22°x0.22° and 0.44°x0.44° Central American domain: 0.22°x0.22° and 0.44°x0.44° Central Asian domain: 0.22°x0.22° and 0.44°x0.44° East Asian domain: 0.22°x0.22° and 0.44°x0.44° European domain: 0.11°x0.11° Mediterranean domain: 0.11°x0.11° and 0.44°x0.44° Middle East and North African domain: 0.22°x0.22° and 0.44°x0.44° North American domain: 0.22°x0.22° and 0.44°x0.44° South American domain: 0.20° x 0.20°, 0.22° x 0.22° and 0.44° x 0.44° South Asian domain: 0.22°x0.22° and 0.44°x0.44° South-East Asian domain: 0.22°x0.22° African domain: 0.22°x0.22° and 0.44°x0.44° Antarctica domain: 0.44°x0.44° Arctic domain: 0.22°x0.22° and 0.44°x0.44° Australasian domain: 0.22°x0.22° and 0.44°x0.44° Central American domain: 0.22°x0.22° and 0.44°x0.44° Central Asian domain: 0.22°x0.22° and 0.44°x0.44° East Asian domain: 0.22°x0.22° and 0.44°x0.44° European domain: 0.11°x0.11° Mediterranean domain: 0.11°x0.11° and 0.44°x0.44° Middle East and North African domain: 0.22°x0.22° and 0.44°x0.44° North American domain: 0.22°x0.22° and 0.44°x0.44° South American domain: 0.20° x 0.20°, 0.22° x 0.22° and 0.44° x 0.44° South Asian domain: 0.22°x0.22° and 0.44°x0.44° South-East Asian domain: 0.22°x0.22° Vertical resolution Variables are provided at a specific single level for each variable. That level may differ among the variables. Vertical resolution Variables are provided at a specific single level for each variable. That level may differ among the variables. Temporal coverage From 1989 to 2008 for evaluation experiments From 1951 to 2005 for historical experiments From 2006 to 2100 for RCP experiments Temporal coverage From 1989 to 2008 for evaluation experiments From 1951 to 2005 for historical experiments From 2006 to 2100 for RCP experiments From 1989 to 2008 for evaluation experiments From 1951 to 2005 for historical experiments From 2006 to 2100 for RCP experiments Temporal resolution 3h, 6h, daily, monthly and seasonal Temporal resolution 3h, 6h, daily, monthly and seasonal File format NetCDF4 File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions Latest version of the data is provided. Versions Latest version of the data is provided. Update frequency Quarterly Update frequency Quarterly MAIN VARIABLES Name Units Description 10m Wind Speed m s-1 The magnitude of the two-dimensional horizontal air velocity. The data represents the mean over the aggregation period at 10m above the surface. 10m u-component of the wind m s-1 The magnitude of the eastward component of the wind at 10m above the surface. 10m v-component of the wind m s-1 The magnitude of the northward component of the wind at 10m above the surface. 200hPa u-component of the wind m s-1 The magnitude of the eastward component of the wind at 10m 200hPa. 200hPa v-component of the wind m s-1 The magnitude of the northward component of the wind at 10m 200hPa. 2m relative humidity % Relative humidity is the percentage ratio of the water vapour mass to the water vapour mass at the saturation point given the temperature at that location. The data represents the mean over the aggregation period at 2m above the surface. 2m specific humidity Dimensionless Amount of moisture in the air at 2m above the surface divided by the amount of air plus moisture at that location. 2m temperature K Ambient air temperature. The data represents the mean over the aggregation period at to 2m above the surface. 500hPa geopotential height m Gravitational potential energy per unit mass normalised by the standard gravity at 500hPa at the same latitude. 850hPa U-component of the wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity at 850hPa. 850hPa V-component of the wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity at 850hPa. Land area fraction % The fraction (in percentage) of grid cell occupied by land surface. The data is time-independent. Maximum 2m temperature in the last 24 hours K Maximum temperature of the air near the surface. The data represents the daily maximum at 2m above the surface. Mean evaporation flux kg m-2 s-1 Mass of surface and sub-surface liquid water per unit area ant time, which evaporates from land. The data includes conversion to vapour phase from both the liquid and solid phase, i.e., includes sublimation, and represents the mean over the aggregation period. Mean precipitation flux kg m-2 s-1 Deposition of water to the Earth s surface in the form of rain, snow, ice or hail. The precipitation flux is the mass of water per unit area and time. The data represents the mean over the aggregation period. Mean sea level pressure Pa The air pressure at sea level. In regions where the Earth s surface is above sea level the surface pressure is used to compute the air pressure that would exist at sea level directly below given a constant air temperature from the surface to the sea level point. The data represents the mean over the aggregation period. Minimum 2m temperature in the last 24 hours K Minimum temperature of the air near the surface. The data represents the daily minimum at 2m above the surface. Orography m The height above the geoid (being 0.0 over the ocean). The data is time-independent. Surface pressure Pa Pressure of air at the lower boundary of the atmosphere. Surface solar radiation downwards W m-2 The downward shortwave radiative flux of energy per unit area. The data represents the mean over the aggregation period at the surface. Surface thermal radiation downward W m-2 Radiative longwave flux of energy incinding on the surface from the above per unit area. Surface upwelling shortwave radiation W m-2 Short wave radiative flux of energy from the surface per unit area. Total cloud cover Dimensionless Total refers to the whole atmosphere column, as seen from the surface or the top of the atmosphere. Cloud cover refers to fraction of horizontal area occupied by clouds. Total run-off flux kg m-2 s-1 The mass of surface and sub-surface liquid water per unit area and time, which drains from land. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 10m Wind Speed m s-1 The magnitude of the two-dimensional horizontal air velocity. The data represents the mean over the aggregation period at 10m above the surface. 10m Wind Speed m s-1 The magnitude of the two-dimensional horizontal air velocity. The data represents the mean over the aggregation period at 10m above the surface. 10m u-component of the wind m s-1 The magnitude of the eastward component of the wind at 10m above the surface. 10m u-component of the wind m s-1 The magnitude of the eastward component of the wind at 10m above the surface. 10m v-component of the wind m s-1 The magnitude of the northward component of the wind at 10m above the surface. 10m v-component of the wind m s-1 The magnitude of the northward component of the wind at 10m above the surface. 200hPa u-component of the wind m s-1 The magnitude of the eastward component of the wind at 10m 200hPa. 200hPa u-component of the wind m s-1 The magnitude of the eastward component of the wind at 10m 200hPa. 200hPa v-component of the wind m s-1 The magnitude of the northward component of the wind at 10m 200hPa. 200hPa v-component of the wind m s-1 The magnitude of the northward component of the wind at 10m 200hPa. 2m relative humidity % Relative humidity is the percentage ratio of the water vapour mass to the water vapour mass at the saturation point given the temperature at that location. The data represents the mean over the aggregation period at 2m above the surface. 2m relative humidity % Relative humidity is the percentage ratio of the water vapour mass to the water vapour mass at the saturation point given the temperature at that location. The data represents the mean over the aggregation period at 2m above the surface. 2m specific humidity Dimensionless Amount of moisture in the air at 2m above the surface divided by the amount of air plus moisture at that location. 2m specific humidity Dimensionless Amount of moisture in the air at 2m above the surface divided by the amount of air plus moisture at that location. 2m temperature K Ambient air temperature. The data represents the mean over the aggregation period at to 2m above the surface. 2m temperature K Ambient air temperature. The data represents the mean over the aggregation period at to 2m above the surface. 500hPa geopotential height m Gravitational potential energy per unit mass normalised by the standard gravity at 500hPa at the same latitude. 500hPa geopotential height m Gravitational potential energy per unit mass normalised by the standard gravity at 500hPa at the same latitude. 850hPa U-component of the wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity at 850hPa. 850hPa U-component of the wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity at 850hPa. 850hPa V-component of the wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity at 850hPa. 850hPa V-component of the wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity at 850hPa. Land area fraction % The fraction (in percentage) of grid cell occupied by land surface. The data is time-independent. Land area fraction % The fraction (in percentage) of grid cell occupied by land surface. The data is time-independent. Maximum 2m temperature in the last 24 hours K Maximum temperature of the air near the surface. The data represents the daily maximum at 2m above the surface. Maximum 2m temperature in the last 24 hours K Maximum temperature of the air near the surface. The data represents the daily maximum at 2m above the surface. Mean evaporation flux kg m-2 s-1 Mass of surface and sub-surface liquid water per unit area ant time, which evaporates from land. The data includes conversion to vapour phase from both the liquid and solid phase, i.e., includes sublimation, and represents the mean over the aggregation period. Mean evaporation flux kg m-2 s-1 Mass of surface and sub-surface liquid water per unit area ant time, which evaporates from land. The data includes conversion to vapour phase from both the liquid and solid phase, i.e., includes sublimation, and represents the mean over the aggregation period. Mean precipitation flux kg m-2 s-1 Deposition of water to the Earth s surface in the form of rain, snow, ice or hail. The precipitation flux is the mass of water per unit area and time. The data represents the mean over the aggregation period. Mean precipitation flux kg m-2 s-1 Deposition of water to the Earth s surface in the form of rain, snow, ice or hail. The precipitation flux is the mass of water per unit area and time. The data represents the mean over the aggregation period. Mean sea level pressure Pa The air pressure at sea level. In regions where the Earth s surface is above sea level the surface pressure is used to compute the air pressure that would exist at sea level directly below given a constant air temperature from the surface to the sea level point. The data represents the mean over the aggregation period. Mean sea level pressure Pa The air pressure at sea level. In regions where the Earth s surface is above sea level the surface pressure is used to compute the air pressure that would exist at sea level directly below given a constant air temperature from the surface to the sea level point. The data represents the mean over the aggregation period. Minimum 2m temperature in the last 24 hours K Minimum temperature of the air near the surface. The data represents the daily minimum at 2m above the surface. Minimum 2m temperature in the last 24 hours K Minimum temperature of the air near the surface. The data represents the daily minimum at 2m above the surface. Orography m The height above the geoid (being 0.0 over the ocean). The data is time-independent. Orography m The height above the geoid (being 0.0 over the ocean). The data is time-independent. Surface pressure Pa Pressure of air at the lower boundary of the atmosphere. Surface pressure Pa Pressure of air at the lower boundary of the atmosphere. Surface solar radiation downwards W m-2 The downward shortwave radiative flux of energy per unit area. The data represents the mean over the aggregation period at the surface. Surface solar radiation downwards W m-2 The downward shortwave radiative flux of energy per unit area. The data represents the mean over the aggregation period at the surface. Surface thermal radiation downward W m-2 Radiative longwave flux of energy incinding on the surface from the above per unit area. Surface thermal radiation downward W m-2 Radiative longwave flux of energy incinding on the surface from the above per unit area. Surface upwelling shortwave radiation W m-2 Short wave radiative flux of energy from the surface per unit area. Surface upwelling shortwave radiation W m-2 Short wave radiative flux of energy from the surface per unit area. Total cloud cover Dimensionless Total refers to the whole atmosphere column, as seen from the surface or the top of the atmosphere. Cloud cover refers to fraction of horizontal area occupied by clouds. Total cloud cover Dimensionless Total refers to the whole atmosphere column, as seen from the surface or the top of the atmosphere. Cloud cover refers to fraction of horizontal area occupied by clouds. Total run-off flux kg m-2 s-1 The mass of surface and sub-surface liquid water per unit area and time, which drains from land. Total run-off flux kg m-2 s-1 The mass of surface and sub-surface liquid water per unit area and time, which drains from land. 53 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-subsurface-temperature-trend-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTIC_OMI_TEMPSAL_Ttz_trend Baltic Sea Subsurface Temperature trend from Reanalysis DEFINITION The subsurface temperature trends have been derived from regional reanalysis results for the Baltic Sea (product references BALTICSEA_REANALYSIS_PHY_003_011). Horizontal averaging has been conducted over the Baltic Sea domain (13 °E - 31 °E and 53 °N - 66 °N; excluding the Skagerrak strait). The temperature trend has been obtained through a linear fit for each time series of horizontally averaged annual temperature and at each depth level. CONTEXT The Baltic Sea is a semi-enclosed sea in North-Eastern Europe. The temperature of the upper mixed layer of the Baltic Sea is characterised by a strong seasonal cycle driven by the annual course of solar radiation (Leppäranta and Myrberg, 2008). The maximum water temperatures in the upper layer are reached in July and August and the minimum during February, when the Baltic Sea becomes partially frozen (CMEMS OMI Baltic Sea Sea Ice Extent, CMEMS OMI Baltic Sea Sea Ice Volume). Seasonal thermocline, developing in the depth range of 10-30 m in spring, reaches its maximum strength in summer and is eroded in autumn. During autumn and winter the Baltic Sea is thermally mixed down to the permanent halocline in the depth range of 60-80 metres (Matthäus, 1984). The 20–50 m thick cold intermediate layer forms below the upper mixed layer in March and is observed until October within the 15-65 m depth range (Chubarenko and Stepanova, 2018; Liblik and Lips, 2011). The deep layers of the Baltic Sea are disconnected from the ventilated upper ocean layers, and temperature variations are predominantly driven by mixing processes and horizontal advection. A warming trend of the sea surface waters is positively correlated with the increasing trend of diffuse attenuation of light (Kd490) and satellite-detected chlorophyll concentration (Kahru et al., 2016). Temperature increase in the water column could accelerate oxygen consumption during organic matter oxidation (Savchuk, 2018). CMEMS KEY FINDINGS The subsurface temperature over the 1993-2021 period shows warming trends of about 0.05 °C/year at all depths. The largest warming trend of 0.06 °C/year is recorded at the 20 m depth, which corresponds to seasonal thermocline. Similar positive trend is at the depth of 60-70 metres, which corresponds to the depth of the upper part of the permanent halocline. A positive trend in the sea surface waters has been detected since the 1990s (BACCII Author Team, 2015) as well as a decreasing trend of the start day of the spring phytoplankton bloom (Raudsepp et al., 2019; Kahru et al., 2016). From the measurements Savchuk (2018) has calculated the temperature trend of 0.04◦oC/year since 1979 on average in the deep layers (>60m) of the Baltic Proper. The temperature trend in the upper layer of 60-m has the widest confidence interval, which indicates the largest interannual variability in that layer. DOI (product):https://doi.org/10.48670/moi-00208 https://doi.org/10.48670/moi-00208 54 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-heat-content-trend-map-reanalysis-multi http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_OHC_trend Global Ocean Heat Content trend map from Reanalysis & Multi-Observations Reprocessing DEFINITION Estimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60°S-60°N aiming i) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change. ii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). Ocean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017). CONTEXT Knowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019). CMEMS KEY FINDINGS Regional trends for the period 2005-2019 from the Copernicus Marine Service multi-ensemble approach show warming at rates ranging from the global mean average up to more than 8 W/m2 in some specific regions (e.g. northern hemisphere western boundary current regimes). There are specific regions where a negative trend is observed above noise at rates up to about -5 W/m2 such as in the subpolar North Atlantic, or the western tropical Pacific. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020). Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00236 https://doi.org/10.48670/moi-00236 55 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/iberia-biscay-ireland-sea-surface-temperature-extreme-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=IBI_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly Iberia Biscay Ireland Sea Surface Temperature extreme from Reanalysis DEFINITION The CMEMS IBI_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_PHY_005_002) and the Analysis product (IBI_ANALYSISFORECAST_PHY_005_001). Two parameters have been considered for this OMI: • Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2019). • Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile. This indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019). CONTEXT The Sea Surface Temperature is one of the essential ocean variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. While the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013) in the North Atlantic, anomalous cold conditions have also been reported since 2014 (Mulet et al., 2018; Dubois et al., 2018). The IBI area is a complex dynamic region with a remarkable variety of ocean physical processes and scales involved. The Sea Surface Temperature field in the region is strongly dependent on latitude, with higher values towards the South (Locarnini et al. 2013). This latitudinal gradient is supported by the presence of the eastern part of the North Atlantic subtropical gyre that transports cool water from the northern latitudes towards the equator. Additionally, the Iberia-Biscay-Ireland region is under the influence of the Sea Level Pressure dipole established between the Icelandic low and the Bermuda high. Therefore, the interannual and interdecadal variability of the surface temperature field may be influenced by the North Atlantic Oscillation pattern (Czaja and Frankignoul, 2002; Flatau et al., 2003). Also relevant in the region are the upwelling processes taking place in the coastal margins. The most referenced one is the eastern boundary coastal upwelling system off the African and western Iberian coast (Sotillo et al., 2016), although other smaller upwelling systems have also been described in the northern coast of the Iberian Peninsula (Alvarez et al., 2011), the south-western Irish coast (Edwars et al., 1996) and the European Continental Slope (Dickson, 1980). CMEMS KEY FINDINGS In the IBI region, the 99th mean percentile for 1993-2019 shows a north-south pattern driven by the climatological distribution of temperatures in the North Atlantic. In the coastal regions of Africa and the Iberian Peninsula, the mean values are influenced by the upwelling processes (Sotillo et al., 2016). These results are consistent with the ones presented in Álvarez Fanjul et al;, (2019) for the period 1993-2016. The anomaly of the 99th percentile in 2021 is mainly positive with values that surpass the climatic standard deviation. However, the anomaly values range from -3°C to 5°C, consequently, results show localised regions where positive and negative anomalies exceeded twice the climatic standard deviation. The higher positive anomalies are spread on the open ocean mainly affecting latitudes from 30°N up to 45°N and from 50°N up to 56°N. The indicator shows negative anomalies higher than twice the mean variability (up to -3°C) in the upwelling regions near the north-west African coast (from 30°N to 33°N). DOI (product):https://doi.org/10.48670/moi-00254 https://doi.org/10.48670/moi-00254 56 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/nino-34-temporal-evolution-vertical-profile-temperature http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_CLIMVAR_enso_Tzt_anomaly Nino 3.4 Temporal Evolution of Vertical Profile of Temperature from Reanalysis "''DEFINITION NINO34 sub surface temperature anomaly (°C) is defined as the difference between the subsurface temperature averaged over the 170°W-120°W 5°S,-5°N area and the climatological reference value over same area. Spatial averaging was weighted by surface area. Monthly mean values are given here. The reference period is 1993-2014. CONTEXT El Nino Southern Oscillation (ENSO) is one of the most important sources of climatic variability resulting from a strong coupling between ocean and atmosphere in the central tropical Pacific and affecting surrounding populations. Globally, it impacts ecosystems, precipitation, and freshwater resources (Glantz, 2001). ENSO is mainly characterized by two anomalous states that last from several months to more than a year and recur irregularly on a typical time scale of 2-7 years. The warm phase El Niño is broadly characterized by a weakening of the easterly trade winds at interannual timescales associated with surface and subsurface processes leading to a surface warming in the eastern Pacific. Opposite changes are observed during the cold phase La Niña (review in Wang et al., 2017). Nino 3.4 sub-surface Temperature Anomaly is a good indicator of the state of the Central tropical Pacific el Nino conditions and enable to monitor the evolution the ENSO phase. CMEMS KEY FINDINGS Over the 1993-2017 period, there were several episodes of strong positive ENSO (el nino) phases in particular during the 1997/1998 winter and the 2015/2016 winter, where NINO3.4 indicator reached positive values larger than 2°C (and remained above 0.5°C during more than 6 months). Several La Nina events were also observed like during the 1998/1999 winter and during the 2010/2011 winter. The NINO34 subsurface indicator is a good index to monitor the state of ENSO phase and a useful tool to help seasonal forecasting of atmospheric conditions. Note: The key findings will be updated annually in November, in line with OMI evolutions. '""REFERENCES DOI (product):https://doi.org/10.48670/moi-00220 https://doi.org/10.48670/moi-00220 57 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-zonal-mean-subsurface-temperature-cumulative http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_TEMPSAL_Tyz_trend Global Ocean Zonal Mean Subsurface Temperature cumulative trend from Multi-Observations Reprocessing DEFINITION The linear change of zonal mean subsurface temperature over the period 1993-2019 at each grid point (in depth and latitude) is evaluated to obtain a global mean depth-latitude plot of subsurface temperature trend, expressed in °C. The linear change is computed using the slope of the linear regression at each grid point scaled by the number of time steps (27 years, 1993-2019). A multi-product approach is used, meaning that the linear change is first computed for 5 different zonal mean temperature estimates. The average linear change is then computed, as well as the standard deviation between the five linear change computations. The evaluation method relies in the study of the consistency in between the 5 different estimates, which provides a qualitative estimate of the robustness of the indicator. See Mulet et al. (2018) for more details. CONTEXT Large-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions, while the deeper ocean temperature in the main thermocline and below varies due to many dynamical forcing mechanisms (Bindoff et al., 2019). Together with ocean acidification and deoxygenation (IPCC, 2019), ocean warming can lead to dramatic changes in ecosystem assemblages, biodiversity, population extinctions, coral bleaching and infectious disease, change in behavior (including reproduction), as well as redistribution of habitat (e.g. Gattuso et al., 2015, Molinos et al., 2016, Ramirez et al., 2017). Ocean warming also intensifies tropical cyclones (Hoegh-Guldberg et al., 2018; Trenberth et al., 2018; Sun et al., 2017). CMEMS KEY FINDINGS The results show an overall ocean warming of the upper global ocean over the period 1993-2019, particularly in the upper 300m depth. In some areas, this warming signal reaches down to about 800m depth such as for example in the Southern Ocean south of 40°S. In other areas, the signal-to-noise ratio in the deeper ocean layers is less than two, i.e. the different products used for the ensemble mean show weak agreement. However, interannual-to-decadal fluctuations are superposed on the warming signal, and can interfere with the warming trend. For example, in the subpolar North Atlantic decadal variations such as the so called ‘cold event’ prevail (Dubois et al., 2018; Gourrion et al., 2018), and the cumulative trend over a quarter of a decade does not exceed twice the noise level below about 100m depth. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00244 https://doi.org/10.48670/moi-00244 58 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-health-temperature-exposure-current-climate https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-health-temperature-exposure-current-climate app-health-temperature-exposure-current-climate The application presents European temperature statistics for the period 1979-2019. These statistics were derived from ERA5 hourly near-surface air temperatures. Temperature statistics are typically used in epidemiology and public health when defining health risk estimates and when looking at current and future health impacts. The interactive livemap displays the selected climate variable for the selected season and year. Users can view the annual/seasonal mean or 10th/50th/90th percentile using the selection buttons in the upper right of the map window. Clicking on a highlighted country or adminstrative region will open a window with a focused view on that region. The focused view contains: 1. A series of map plots for the selected year. One map plot each for the annual mean and 10th/50th/90th percentiles 1. 2. A time series which displays the mean (with +/- standard deviation) and the mean of the 10th, 50th and 90th percentiles for the entire region. Users can select which statistics to view by clicking on them in the legend. 2. (Note: adminstrative regions are currently only available for Belgium, Hungary, Italy and Lithuania.) User-selectable parameters User-selectable parameters Daily temperature statistic: Mean/minimum/maximum 2-metre air temperature Season: Annual/summer/winter Year: 1979-2019 Daily temperature statistic: Mean/minimum/maximum 2-metre air temperature Daily temperature statistic Season: Annual/summer/winter Season Year: 1979-2019 Year More details about the products are given in the Documentation section. INPUT VARIABLES Name Units Description Source Near-surface air temperature K ERA5 hourly temperature of air at 2m above the surface. ERA5 INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Near-surface air temperature K ERA5 hourly temperature of air at 2m above the surface. ERA5 Near-surface air temperature K ERA5 hourly temperature of air at 2m above the surface. ERA5 ERA5 59 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-density-2018-raster-10-m-europe-3-yearly https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/imperviousness-density-2018 Imperviousness Density 2018 (raster 10 m), Europe, 3-yearly, Aug. 2020 The High Resolution Layer on Imperviousness Density 2018 is a thematic product showing the sealing density in the range from 0-100% for the period 2018 (including data from 2017-2019) for the EEA-38 area and the United Kingdom. The production of the high resolution imperviousness layers is coordinated by EEA in the frame of the EU Copernicus programme. The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. Data is provided as 10 meter rasters (fully conformant with the EEA reference grid) in 100 x 100 km tiles grouped according to the EEA38 countries and the United Kingdom. 60 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/nino-34-sea-surface-temperature-time-series-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_CLIMVAR_enso_sst_area_averaged_anomalies Nino 3.4 Sea Surface Temperature time series from Reanalysis DEFINITION NINO34 sea surface temperature anomaly (°C) is defined as the difference between the sea surface temperature averaged over the 170°W-120°W 5°S,-5°N area and the climatological reference value over same area. Spatial averaging was weighted by surface area. Monthly mean values are given here. The reference period is 1993-2014. El Nino or La Nina events are defined when the NINO3.4 SST anomalies exceed +/- 0.5°C during a period of six month. CONTEXT El Nino Southern Oscillation (ENSO) is one of the most important source of climatic variability resulting from a strong coupling between ocean and atmosphere in the central tropical Pacific and affecting surrounding populations. Globally, it impacts ecosystems, precipitation, and freshwater resources (Glantz, 2001). ENSO is mainly characterized by two anomalous states that last from several months to more than a year and recur irregularly on a typical time scale of 2-7 years. The warm phase El Niño is broadly characterized by a weakening of the easterly trade winds at interannual timescales associated with surface and subsurface processes leading to a surface warming in the eastern Pacific. Opposite changes are observed during the cold phase La Niña (review in Wang et al., 2017). Nino 3.4 Sea surface Temperature Anomaly is a good indicator of the state of the Central tropical Pacific El Nino conditions and enable to monitor the evolution the ENSO phase. CMEMS KEY FINDINGS Over the 1993-2019 period, there were several episodes of strong positive ENSO phases in particular in 1998 and 2016, where NINO3.4 indicator reached positive values larger than 2°C (and remained above 0.5°C during more than 6 months). Several La Nina events were also observed like in 2000 and 2008. The NINO34 indicator is a good index to monitor the state of ENSO phase and a useful tool to help seasonal forecasting of meteorological conditions. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00219 https://doi.org/10.48670/moi-00219 61 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-surface-temperature-extreme-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MEDSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly Mediterranean Sea Surface Temperature extreme from Reanalysis DEFINITION The CMEMS MEDSEA_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_PHY_006_004) and the Analysis product (MEDSEA_ANALYSISFORECAST_PHY_006_013). Two parameters have been considered for this OMI: * Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1987-2019). * Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Near Real Time product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile. This indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019). CONTEXT The Sea Surface Temperature is one of the Essential Ocean Variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. In recent decades (from mid ‘80s) the Mediterranean Sea showed a trend of increasing temperatures (Ducrocq et al., 2016), which has been observed also by means of the CMEMS SST_MED_SST_L4_REP_OBSERVATIONS_010_021 satellite product and reported in the following CMEMS OMI: MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies and MEDSEA_OMI_TEMPSAL_sst_trend. The Mediterranean Sea is a semi-enclosed sea characterized by an annual average surface temperature which varies horizontally from ~14°C in the Northwestern part of the basin to ~23°C in the Southeastern areas. Large-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions. The Mediterranean Sea annual 99th percentile presents a significant interannual and multidecadal variability with a significant increase starting from the 80’s as shown in Marbà et al. (2015) which is also in good agreement with the multidecadal change of the mean SST reported in Mariotti et al. (2012). Moreover the spatial variability of the SST 99th percentile shows large differences at regional scale (Darmariaki et al., 2019; Pastor et al. 2018). CMEMS KEY FINDINGS The Mediterranean mean Sea Surface Temperature 99th percentile evaluated in the period 1987-2019 (upper panel) presents highest values (~ 28-30 °C) in the eastern Mediterranean-Levantine basin and along the Tunisian coasts especially in the area of the Gulf of Gabes, while the lowest (~ 23–25 °C) are found in the Gulf of Lyon (a deep water formation area), in the Alboran Sea (affected by incoming Atlantic waters) and the eastern part of the Aegean Sea (an upwelling region). These results are in agreement with previous findings in Darmariaki et al. (2019) and Pastor et al. (2018) and are consistent with the ones presented in CMEMS OSR3 (Alvarez Fanjul et al., 2019) for the period 1993-2016. The 2020 Sea Surface Temperature 99th percentile anomaly map (bottom panel) shows a general positive pattern up to +3°C in the North-West Mediterranean area while colder anomalies are visible in the Gulf of Lion and North Aegean Sea . This Ocean Monitoring Indicator confirms the continuous warming of the SST and in particular it shows that the year 2020 is characterized by an overall increase of the extreme Sea Surface Temperature values in almost the whole domain with respect to the reference period. This finding can be probably affected by the different dataset used to evaluate this anomaly map: the 2020 Sea Surface Temperature 99th percentile derived from the Near Real Time Analysis product compared to the mean (1987-2019) Sea Surface Temperature 99th percentile evaluated from the Reanalysis product which, among the others, is characterized by different atmospheric forcing). Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00266 https://doi.org/10.48670/moi-00266 62 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-surface-temperature-cumulative-trend http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MEDSEA_OMI_TEMPSAL_sst_trend Mediterranean Sea Surface Temperature cumulative trend map from Observations Reprocessing DEFINITION The medsea_omi_tempsal_sst_trend product includes the cumulative/net Sea Surface Temperature (SST) trend for the Mediterranean Sea over the period 1993-2021, i.e. the rate of change (°C/year) multiplied by the number years in the time series (29 years). This OMI is derived from the CMEMS Reprocessed Mediterranean L4 SST product (SST_MED_SST_L4_REP_OBSERVATIONS_010_021, see also the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-MEDSEA-SST.pdf), which provided the SSTs used to compute the SST trend over the Mediterranean Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05° grid resolution SST maps over the Mediterranean Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005; Pisano et al., 2020), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens’s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018). http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-MEDSEA-SST.pdf CONTEXT Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterize the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). The Mediterranean Sea is a climate change hotspot (Giorgi F., 2006). Indeed, Mediterranean SST has experienced a continuous warming trend since the beginning of 1980s (e.g., Pisano et al., 2020; Pastor et al., 2020). Specifically, since the beginning of the 21st century (from 2000 onward), the Mediterranean Sea featured the highest SSTs and this warming trend is expected to continue throughout the 21st century (Kirtman et al., 2013). CMEMS KEY FINDINGS The spatial pattern of the Mediterranean SST trend shows a general warming tendency, ranging from 0.002 °C/year to 0.063 °C/year. Overall, a higher SST trend intensity characterizes the Eastern and Central Mediterranean basin with respect to the Western basin. In particular, the Balearic Sea, Tyrrhenian and Adriatic Seas, as well as the northern Ionian and Aegean-Levantine Seas show the highest SST trends (from 0.04 °C/year to 0.05 °C/year on average). Trend patterns of warmer intensity characterize some of main sub-basin Mediterranean features, such as the Pelops Anticyclone, the Cretan gyre and the Rhodes Gyre. On the contrary, less intense values characterize the southern Mediterranean Sea (toward the African coast), where the trend attains around 0.025 °C/year. The SST warming rate spatial change, mostly showing an eastward increase pattern (see, e.g., Pisano et al., 2020, and references therein), i.e. the Levantine basin getting warm faster than the Western, appears now to have tilted more along a North-South direction. DOI (product):https://doi.org/10.48670/moi-00269 https://doi.org/10.48670/moi-00269 63 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-level-extreme-observations-reprocessing http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_VAR_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs Baltic Sea Level extreme from Observations Reprocessing DEFINITION The OMI_VAR_EXTREME_SL_BALTIC_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset baltic_omi_sl_extreme_var_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). CONTEXT Sea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990’s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves. CMEMS KEY FINDINGS Up to 51 stations fulfill the completeness index criteria in this region, a significant improvement with respect to 2019 (only 28 stations), due to new data providers and reprocessed timeseries availability in the new product INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053. The spatial variation of the mean 99th percentiles follow the tidal range pattern, reaching its highest values in the northern end of the Gulf of Bothnia (e.g.: 0.81 m above mean sea level in Kemi) and the inner part of the Gulf of Finland (e.g.: 0.82 m above mean sea level in St. Petersburg). Smaller tides and therefore 99th percentiles are found along the southeastern coast of Sweden, between Stockholm and Gotland Island (e.g.: 0.42 m above mean sea level in Visby). Annual percentiles standard deviation ranges between 3-5 cm in the South (e.g.: 4 cm in Slipshavn) to 10-13 cm in the Gulf of Finland (e.g.: 13 cm in St. Petersburg). Positive anomalies of 2020 99th percentile are observed for most of the basin (up to 11 cm in St. Petersburg), except at the southern Danish stations, which show negative anomalies reaching -6 cm in Hesnaes and Rodby. This result contrasts with the remarkably negative anomaly observed in 2019. DOI (product):https://doi.org/10.48670/moi-00203 https://doi.org/10.48670/moi-00203 64 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-significant-wave-height-extreme-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MEDSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly Mediterranean Sea Significant Wave Height extreme from Reanalysis DEFINITION The CMEMS MEDSEA_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (MEDSEA_MULTIYEAR_WAV_006_012) and the Analysis product (MEDSEA_ANALYSIS_FORECAST_WAV_006_017). Two parameters have been considered for this OMI: * Map of the 99th mean percentile: It is obtained from the Multy Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1993-2019). * Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2020. This indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (Pérez Gómez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Álvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (Álvarez- Fanjul et al., 2019). CONTEXT The sea state and its related spatio-temporal variability affect maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (González-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2014). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions. The Mediterranean Sea is an almost enclosed basin where the complexity of its orographic characteristics deeply influences the atmospheric circulation at local scale, giving rise to strong regional wind regimes (Drobinski et al. 2018). Therefore, since waves are primarily driven by winds, high waves are present over most of the Mediterranean Sea and tend to reach the highest values where strong wind and long fetch (i.e. the horizontal distance over which wave-generating winds blow) are simultaneously present (Lionello et al. 2006). Specifically, as seen in figure and in agreement with other studies (e.g. Sartini et al. 2017), the highest values (5 – 6 m in figure, top) extend from the Gulf of Lion to the southwestern Sardinia through the Balearic Sea and are sustained southwards approaching the Algerian coast. They result from northerly winds dominant in the western Mediterranean Sea (Mistral or Tramontana), that become stronger due to orographic effects (Menendez et al. 2014), and act over a large area. In the Ionian Sea, the northerly Mistral wind is still the main cause of high waves (4-5 m in figure, top). In the Aegean and Levantine Seas, high waves (4-5 m in figure, top) are caused by the northerly Bora winds, prevalent in winter, and the northerly Etesian winds, prevalent in summer (Lionello et al. 2006; Chronis et al. 2011; Menendez et al. 2014). In general, northerly winds are responsible for most high waves in the Mediterranean (e.g. Chronis et al. 2011; Menendez et al. 2014). In agreement with figure (top), studies on the eastern Mediterranean and the Hellenic Seas have found that the typical wave height range in the Aegean Sea is similar to the one observed in the Ionian Sea despite the shorter fetches characterizing the former basin (Zacharioudaki et al. 2015). This is because of the numerous islands in the Aegean Sea which cause wind funneling and enhance the occurrence of extreme winds and thus of extreme waves (Kotroni et al. 2001). Special mention should be made of the high waves, sustained throughout the year, observed east and west of the island of Crete, i.e. around the exiting points of the northerly airflow in the Aegean Sea (Zacharioudaki et al. 2015). This airflow is characterized by consistently high magnitudes that are sustained during all seasons in contrast to other airflows in the Mediterranean Sea that exhibit a more pronounced seasonality (Chronis et al. 2011). CMEMS KEY FINDINGS In 2020 (bottom panel), higher-than-average values of the 99th percentile of Significant Wave Height are seen over most of the northern Mediterranean Sea, in the eastern Alboran Sea, and along stretches of the African coast (Tunisia, Libya and Egypt). In many cases they exceed the climatic standard deviation. Regions where the climatic standard deviation is exceeded twice are the European and African coast of the eastern Alboran Sea, a considerable part of the eastern Spanish coast, the Ligurian Sea and part of the east coast of France as well as areas of the southern Adriatic. These anomalies correspond to the maximum positive anomalies computed in the Mediterranean Sea for year 2020 with values that reach up to 1.1 m. Spatially constrained maxima are also found at other coastal stretches (e.g. Algeri, southeast Sardinia). Part of the positive anomalies found along the French and Spanish coast, including the coast of the Balearic Islands, can be associated with the wind storm “Gloria” (19/1 – 24/1) during which exceptional eastern winds originated in the Ligurian Sea and propagated westwards. The storm, which was of a particularly high intensity and long duration, caused record breaking wave heights in the region, and, in return, great damage to the coast (Amores et al., 2020; de Alfonso et al., 2021). Other storms that could have contributed to the positive anomalies observed in the western Mediterranean Sea include: storm Karine (25/2 – 5/4), which caused high waves from the eastern coast of Spain to the Balearic Islands (Copernicus, Climate Change Service, 2020); storm Bernardo (7/11 – 18/11) which also affected the Balearic islands and the Algerian coast and; storm Hervé (2/2 – 8/2) during which the highest wind gust was recorded at north Corsica (Wikiwand, 2021). In the eastern Mediterranean Sea, the medicane Ianos (14/9 – 21/9) may have contributed to the positive anomalies shown in the central Ionian Sea since this area coincides with the area of peak wave height values during the medicane (Copernicus, 2020a and Copernicus, 2020b). Otherwise, higher-than-average values in the figure are the result of severe, yet not unusual, wind events, which occurred during the year. Negative anomalies occur over most of the southern Mediterranean Sea, east of the Alboran Sea. The maximum negative anomalies reach about -1 m and are located in the southeastern Ionian Sea and west of the south part of mainland Greece as well as in coastal locations of the north and east Aegean They appear to be quite unusual since they are greater than two times the climatic standard deviation in the region. They could imply less severe southerly wind activity during 2020 (Drobinski et al., 2018). Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00262 https://doi.org/10.48670/moi-00262 65 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-health-urban-climate https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-health-urban-climate app-health-urban-climate This application presents visualisations of urban temperature, humidity and wind speed statistics over the ten year period of 2008-2017; derived from the dataset: 'Climate variables for cities in Europe from 2008 to 2017', which is underpinned by the UrbClim model. Urban regions pose a challenge to our use of climate and meteorological data. These regions are where humans spend the majority of the lives, yet they are not well represented by weather forecasting and climate reanalysis models. This application presents the output from the Urbclim model which interpolates ERA5 output to scales more applicable to urban environments. It is important for a wide range of urban applications such as health, civic planning and tourism. The one hundred most populated cities in Europe are presented as clickable pins on a interactive map. Clicking on the pins opens a child application window that presents a map and time-series of the selected variable, season and seasonal statistic. User-selectable parameters User-selectable parameters Season: Summer (June, July, August) Winter (December, January, February) Annual Variable: Minimum temperature (daily) Mean temperature (daily) Maximum temperature (daily) Specific humidity Relative humidity Surface wind speed Annual/seasonal statistic (statistic to apply in the temporal dimension): Mean 10th/25th/50th/75th/90th Percentiles Season: Summer (June, July, August) Winter (December, January, February) Annual Summer (June, July, August) Winter (December, January, February) Annual Summer (June, July, August) Winter (December, January, February) Annual Variable: Minimum temperature (daily) Mean temperature (daily) Maximum temperature (daily) Specific humidity Relative humidity Surface wind speed Minimum temperature (daily) Mean temperature (daily) Maximum temperature (daily) Specific humidity Relative humidity Surface wind speed Minimum temperature (daily) Mean temperature (daily) Maximum temperature (daily) Specific humidity Relative humidity Surface wind speed Annual/seasonal statistic (statistic to apply in the temporal dimension): Mean 10th/25th/50th/75th/90th Percentiles Mean 10th/25th/50th/75th/90th Percentiles Mean 10th/25th/50th/75th/90th Percentiles MAIN VARIABLES Name Units Description Source Air temperature K Air temperature valid for a grid cell at the height of 2m above the surface. UrbClim Land-sea mask Dimensionless The land cover classes from CORINE that represent land areas are masked with value 1 and land cover classes that represent water surfaces are masked as NaN. UrbClim Relative humidity % Relation between actual humidity and saturation humidity at 2m height. Values are in the interval [0,100]: 0% means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. UrbClim Specific humidity kg kg-1 Mass of water vapour in a unit mass of moist air at 2m height. UrbClim Wind speed m s-1 Wind speed valid for a grid cell at the height of 2m above the surface. It is computed from both the zonal (u) and the meridional (v) wind components by sqrt(u2 + v2 ). UrbClim MAIN VARIABLES MAIN VARIABLES Name Units Description Source Name Units Description Source Air temperature K Air temperature valid for a grid cell at the height of 2m above the surface. UrbClim Air temperature K Air temperature valid for a grid cell at the height of 2m above the surface. UrbClim UrbClim Land-sea mask Dimensionless The land cover classes from CORINE that represent land areas are masked with value 1 and land cover classes that represent water surfaces are masked as NaN. UrbClim Land-sea mask Dimensionless The land cover classes from CORINE that represent land areas are masked with value 1 and land cover classes that represent water surfaces are masked as NaN. UrbClim UrbClim Relative humidity % Relation between actual humidity and saturation humidity at 2m height. Values are in the interval [0,100]: 0% means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. UrbClim Relative humidity % Relation between actual humidity and saturation humidity at 2m height. Values are in the interval [0,100]: 0% means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. UrbClim UrbClim Specific humidity kg kg-1 Mass of water vapour in a unit mass of moist air at 2m height. UrbClim Specific humidity kg kg-1 Mass of water vapour in a unit mass of moist air at 2m height. UrbClim UrbClim Wind speed m s-1 Wind speed valid for a grid cell at the height of 2m above the surface. It is computed from both the zonal (u) and the meridional (v) wind components by sqrt(u2 + v2 ). UrbClim Wind speed m s-1 Wind speed valid for a grid cell at the height of 2m above the surface. It is computed from both the zonal (u) and the meridional (v) wind components by sqrt(u2 + v2 ). UrbClim UrbClim 66 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/projections-cmip6 https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip6 projections-cmip6 This catalogue entry provides daily and monthly global climate projections data from a large number of experiments, models and time periods computed in the framework of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). CMIP6 data underpins the Intergovernmental Panel on Climate Change 6th Assessment Report. The use of these data is mostly aimed at: addressing outstanding scientific questions that arose as part of the IPCC reporting process; improving the understanding of the climate system; providing estimates of future climate change and related uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different models are in simulating the recent past. addressing outstanding scientific questions that arose as part of the IPCC reporting process; improving the understanding of the climate system; providing estimates of future climate change and related uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different models are in simulating the recent past. The term "experiments" refers to the three main categories of CMIP6 simulations: Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2014. Climate projection experiments following the combined pathways of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP). The SSP scenarios provide different pathways of the future climate forcing. The period covered is typically 2015-2100. Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2014. Climate projection experiments following the combined pathways of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP). The SSP scenarios provide different pathways of the future climate forcing. The period covered is typically 2015-2100. This catalogue entry provides both two- and three-dimensional data, along with an option to apply spatial and/or temporal subsetting to data requests. This is a new feature of the global climate projection dataset, which relies on compute processes run simultaneously in the ESGF nodes, where the data are originally located. The data are produced by the participating institutes of the CMIP6 project. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid, ocean grid Horizontal coverage Global Horizontal resolution Varies between models Vertical coverage Single levels, pressure levels (1 - 1000 hPa) Temporal coverage From 1850 to 2014 for historical experiments From 2015 to 2100 for SSP experiments Temporal resolution Monthly, daily, fixed (no temporal resolution) File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention CF-1.7 CMIP-6.2 Versions Latest version of the data is provided DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid, ocean grid Projection Regular latitude-longitude grid, ocean grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution Varies between models Horizontal resolution Varies between models Vertical coverage Single levels, pressure levels (1 - 1000 hPa) Vertical coverage Single levels, pressure levels (1 - 1000 hPa) Temporal coverage From 1850 to 2014 for historical experiments From 2015 to 2100 for SSP experiments Temporal coverage From 1850 to 2014 for historical experiments From 2015 to 2100 for SSP experiments From 1850 to 2014 for historical experiments From 2015 to 2100 for SSP experiments Temporal resolution Monthly, daily, fixed (no temporal resolution) Temporal resolution Monthly, daily, fixed (no temporal resolution) File format NetCDF4 File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention CF-1.7 CMIP-6.2 Conventions Climate and Forecast (CF) Metadata Convention CF-1.7 CMIP-6.2 Versions Latest version of the data is provided Versions Latest version of the data is provided MAIN VARIABLES Name Units Description Air temperature K Temperature in the atmosphere. It has units of Kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere. Capacity of soil to store water (field capacity) kg m-2 The total water holding capacity of all the soil in the grid cell divided by the land area of the grid cell. Daily maximum near-surface air temperature K Daily maximum temperature of air at 2m above the surface of land, sea or inland waters. Daily minimum near-surface air temperature K Daily minimum temperature of air at 2m above the surface of land, sea or inland waters. Eastward near-surface wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity 10m above the surface. Eastward wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity. Evaporation including sublimation and transpiration kg m-2 s-1 The transfer of latent heat (resulting from water phase changes, such as evaporation, condensation, sublimation and transpiration) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. It indicates a vector component which is positive when directed downward (negative upward). Grid-cell area for ocean variables m2 The area of the grid cell in the ocean. The data is time-independent. Land ice area percentage % Fraction of grid cell occupied by "permanent" ice (e.g. glaciers). The data is time-independent. Moisture in upper portion of soil column kg m-2 Vertical sum per unit area from the surface down to the bottom of the soil model of water in all phases contained in soil. Near-Surface air temperature K Temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Near-surface relative humidity % Amount of moisture in the air near the surface divided by the maximum amount of moisture that could exist in the air at a specific temperature and location. Near-surface specific humidity Dimensionless Amount of moisture in the air near the surface divided by amount of air plus moisture at that location. Near-surface wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Northward near-surface wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity 10m above the surface. Percentage of the grid cell occupied by land including lakes % The percentage of land or lake surface in a grid cell. The data is time-independent. Precipitation kg m-2 s-1 The sum of liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable represents amount of water per unit area and time. Relative humidity % Amount of moisture in the air divided by the maximum amount of moisture that could exist in the air at a specific temperature and location. Sea area percentage % The percentage of sea surface in a grid cell. The data is time-independent. Sea ice thickness m Vertical extent of ocean sea ice. Sea level pressure Pa The pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at sea level. It is calculated over all surfaces - land, sea and inland water. Sea surface height above geoid m Vertical distance between the actual sea surface and a surface of constant geopotential with which mean sea level would coincide if the ocean were at rest. Sea surface salinity PSU Salt concentration close to the ocean's surface. Sea surface temperature K Temperature of sea water near the surface. Sea-ice area percentage on ocean grid % Area of the sea surface occupied by sea ice. Snow depth m Mean thickness of snow. Snowfall flux kg m-2 s-1 Mass of water in the form of snow precipitating per unit area. Specific humidity Dimensionless Amount of moisture in the air divided by amount of air plus moisture at that location. Surface air pressure Pa The pressure (force per unit area) of the air at the lower boundary of the atmosphere. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface. It is calculated over all surfaces - land, sea and inland water. Surface altitude m The height above the geoid (being 0.0 over the sea). The data is time-independent. Surface downward eastward wind stress Pa Eastward component of the horizontal drag exerted by the atmosphere on the surface through turbulent processes. Surface downward northward wind stress Pa Northward component of the horizontal drag exerted by the atmosphere on the surface through turbulent processes. Surface downwelling longwave radiation W m-2 Radiative longwave flux of energy downward at the surface. Surface downwelling shortwave radiation W m-2 Radiative shortwave flux of energy downward at the surface. Surface snow amount kg m-2 Snow amount on the ground, excluding that on the plant or vegetation canopy, per unit area. Surface temperature K Temperature at the interface (not the bulk temperature of the medium above or below) between air and sea for open-sea regions. Surface temperature of sea ice K Temperature that exists at the interface of the sea-ice and the overlying medium which may be air or snow. Surface upward latent heat flux W m-2 Flux per unit area of heat between the surface and the air on account of evaporation including sublimation. Positive when directed upward (negative downward). Surface upward sensible heat flux W m-2 Flux per unit area of heat between the surface and the air by motion of air only. Positive when directed upward (negative downward). Surface upwelling longwave radiation W m-2 Radiative longwave flux of energy from the surface per unit area. Surface upwelling shortwave radiation W m-2 Radiative longwave flux of energy from the surface per unit area. TOA incident shortwave radiation W m-2 Incoming solar radiation received from the Sun, at the top of the atmosphere. TOA outgoing longwave radiation W m-2 Longwave radiation from the top of the atmosphere to space per unit area. TOA outgoing shortwave radiation W m-2 Shortwave radiation from the top of the atmosphere to space per unit area. Total cloud cover percentage Dimensionless Fraction of horizontal area occupied by clouds as seen from the surface to the top of the atmosphere in the whole atmosphere column. Total runoff kg m-2 s-1 Amount per unit area of surface and subsurface liquid water which drains from land. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Air temperature K Temperature in the atmosphere. It has units of Kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere. Air temperature K Temperature in the atmosphere. It has units of Kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere. Capacity of soil to store water (field capacity) kg m-2 The total water holding capacity of all the soil in the grid cell divided by the land area of the grid cell. Capacity of soil to store water (field capacity) kg m-2 The total water holding capacity of all the soil in the grid cell divided by the land area of the grid cell. Daily maximum near-surface air temperature K Daily maximum temperature of air at 2m above the surface of land, sea or inland waters. Daily maximum near-surface air temperature K Daily maximum temperature of air at 2m above the surface of land, sea or inland waters. Daily minimum near-surface air temperature K Daily minimum temperature of air at 2m above the surface of land, sea or inland waters. Daily minimum near-surface air temperature K Daily minimum temperature of air at 2m above the surface of land, sea or inland waters. Eastward near-surface wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity 10m above the surface. Eastward near-surface wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity 10m above the surface. Eastward wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity. Eastward wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity. Evaporation including sublimation and transpiration kg m-2 s-1 The transfer of latent heat (resulting from water phase changes, such as evaporation, condensation, sublimation and transpiration) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. It indicates a vector component which is positive when directed downward (negative upward). Evaporation including sublimation and transpiration kg m-2 s-1 The transfer of latent heat (resulting from water phase changes, such as evaporation, condensation, sublimation and transpiration) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. It indicates a vector component which is positive when directed downward (negative upward). Grid-cell area for ocean variables m2 The area of the grid cell in the ocean. The data is time-independent. Grid-cell area for ocean variables m2 The area of the grid cell in the ocean. The data is time-independent. Land ice area percentage % Fraction of grid cell occupied by "permanent" ice (e.g. glaciers). The data is time-independent. Land ice area percentage % Fraction of grid cell occupied by "permanent" ice (e.g. glaciers). The data is time-independent. Moisture in upper portion of soil column kg m-2 Vertical sum per unit area from the surface down to the bottom of the soil model of water in all phases contained in soil. Moisture in upper portion of soil column kg m-2 Vertical sum per unit area from the surface down to the bottom of the soil model of water in all phases contained in soil. Near-Surface air temperature K Temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Near-Surface air temperature K Temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Near-surface relative humidity % Amount of moisture in the air near the surface divided by the maximum amount of moisture that could exist in the air at a specific temperature and location. Near-surface relative humidity % Amount of moisture in the air near the surface divided by the maximum amount of moisture that could exist in the air at a specific temperature and location. Near-surface specific humidity Dimensionless Amount of moisture in the air near the surface divided by amount of air plus moisture at that location. Near-surface specific humidity Dimensionless Amount of moisture in the air near the surface divided by amount of air plus moisture at that location. Near-surface wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Near-surface wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Northward near-surface wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity 10m above the surface. Northward near-surface wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity 10m above the surface. Percentage of the grid cell occupied by land including lakes % The percentage of land or lake surface in a grid cell. The data is time-independent. Percentage of the grid cell occupied by land including lakes % The percentage of land or lake surface in a grid cell. The data is time-independent. Precipitation kg m-2 s-1 The sum of liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable represents amount of water per unit area and time. Precipitation kg m-2 s-1 The sum of liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable represents amount of water per unit area and time. Relative humidity % Amount of moisture in the air divided by the maximum amount of moisture that could exist in the air at a specific temperature and location. Relative humidity % Amount of moisture in the air divided by the maximum amount of moisture that could exist in the air at a specific temperature and location. Sea area percentage % The percentage of sea surface in a grid cell. The data is time-independent. Sea area percentage % The percentage of sea surface in a grid cell. The data is time-independent. Sea ice thickness m Vertical extent of ocean sea ice. Sea ice thickness m Vertical extent of ocean sea ice. Sea level pressure Pa The pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at sea level. It is calculated over all surfaces - land, sea and inland water. Sea level pressure Pa The pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at sea level. It is calculated over all surfaces - land, sea and inland water. Sea surface height above geoid m Vertical distance between the actual sea surface and a surface of constant geopotential with which mean sea level would coincide if the ocean were at rest. Sea surface height above geoid m Vertical distance between the actual sea surface and a surface of constant geopotential with which mean sea level would coincide if the ocean were at rest. Sea surface salinity PSU Salt concentration close to the ocean's surface. Sea surface salinity PSU Salt concentration close to the ocean's surface. Sea surface temperature K Temperature of sea water near the surface. Sea surface temperature K Temperature of sea water near the surface. Sea-ice area percentage on ocean grid % Area of the sea surface occupied by sea ice. Sea-ice area percentage on ocean grid % Area of the sea surface occupied by sea ice. Snow depth m Mean thickness of snow. Snow depth m Mean thickness of snow. Snowfall flux kg m-2 s-1 Mass of water in the form of snow precipitating per unit area. Snowfall flux kg m-2 s-1 Mass of water in the form of snow precipitating per unit area. Specific humidity Dimensionless Amount of moisture in the air divided by amount of air plus moisture at that location. Specific humidity Dimensionless Amount of moisture in the air divided by amount of air plus moisture at that location. Surface air pressure Pa The pressure (force per unit area) of the air at the lower boundary of the atmosphere. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface. It is calculated over all surfaces - land, sea and inland water. Surface air pressure Pa The pressure (force per unit area) of the air at the lower boundary of the atmosphere. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface. It is calculated over all surfaces - land, sea and inland water. Surface altitude m The height above the geoid (being 0.0 over the sea). The data is time-independent. Surface altitude m The height above the geoid (being 0.0 over the sea). The data is time-independent. Surface downward eastward wind stress Pa Eastward component of the horizontal drag exerted by the atmosphere on the surface through turbulent processes. Surface downward eastward wind stress Pa Eastward component of the horizontal drag exerted by the atmosphere on the surface through turbulent processes. Surface downward northward wind stress Pa Northward component of the horizontal drag exerted by the atmosphere on the surface through turbulent processes. Surface downward northward wind stress Pa Northward component of the horizontal drag exerted by the atmosphere on the surface through turbulent processes. Surface downwelling longwave radiation W m-2 Radiative longwave flux of energy downward at the surface. Surface downwelling longwave radiation W m-2 Radiative longwave flux of energy downward at the surface. Surface downwelling shortwave radiation W m-2 Radiative shortwave flux of energy downward at the surface. Surface downwelling shortwave radiation W m-2 Radiative shortwave flux of energy downward at the surface. Surface snow amount kg m-2 Snow amount on the ground, excluding that on the plant or vegetation canopy, per unit area. Surface snow amount kg m-2 Snow amount on the ground, excluding that on the plant or vegetation canopy, per unit area. Surface temperature K Temperature at the interface (not the bulk temperature of the medium above or below) between air and sea for open-sea regions. Surface temperature K Temperature at the interface (not the bulk temperature of the medium above or below) between air and sea for open-sea regions. Surface temperature of sea ice K Temperature that exists at the interface of the sea-ice and the overlying medium which may be air or snow. Surface temperature of sea ice K Temperature that exists at the interface of the sea-ice and the overlying medium which may be air or snow. Surface upward latent heat flux W m-2 Flux per unit area of heat between the surface and the air on account of evaporation including sublimation. Positive when directed upward (negative downward). Surface upward latent heat flux W m-2 Flux per unit area of heat between the surface and the air on account of evaporation including sublimation. Positive when directed upward (negative downward). Surface upward sensible heat flux W m-2 Flux per unit area of heat between the surface and the air by motion of air only. Positive when directed upward (negative downward). Surface upward sensible heat flux W m-2 Flux per unit area of heat between the surface and the air by motion of air only. Positive when directed upward (negative downward). Surface upwelling longwave radiation W m-2 Radiative longwave flux of energy from the surface per unit area. Surface upwelling longwave radiation W m-2 Radiative longwave flux of energy from the surface per unit area. Surface upwelling shortwave radiation W m-2 Radiative longwave flux of energy from the surface per unit area. Surface upwelling shortwave radiation W m-2 Radiative longwave flux of energy from the surface per unit area. TOA incident shortwave radiation W m-2 Incoming solar radiation received from the Sun, at the top of the atmosphere. TOA incident shortwave radiation W m-2 Incoming solar radiation received from the Sun, at the top of the atmosphere. TOA outgoing longwave radiation W m-2 Longwave radiation from the top of the atmosphere to space per unit area. TOA outgoing longwave radiation W m-2 Longwave radiation from the top of the atmosphere to space per unit area. TOA outgoing shortwave radiation W m-2 Shortwave radiation from the top of the atmosphere to space per unit area. TOA outgoing shortwave radiation W m-2 Shortwave radiation from the top of the atmosphere to space per unit area. Total cloud cover percentage Dimensionless Fraction of horizontal area occupied by clouds as seen from the surface to the top of the atmosphere in the whole atmosphere column. Total cloud cover percentage Dimensionless Fraction of horizontal area occupied by clouds as seen from the surface to the top of the atmosphere in the whole atmosphere column. Total runoff kg m-2 s-1 Amount per unit area of surface and subsurface liquid water which drains from land. Total runoff kg m-2 s-1 Amount per unit area of surface and subsurface liquid water which drains from land. 67 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-sea-surface-temperature-time-series-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_TEMPSAL_sst_area_averaged_anomalies Global Ocean Sea Surface Temperature time series and trend from Observations Reprocessing DEFINITION Based on daily, global climate sea surface temperature (SST) analyses generated by the European Space Agency (ESA) SST Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) (Merchant et al., 2019; product SST-GLO-SST-L4-REP-OBSERVATIONS-010-024). Analysis of the data was based on the approach described in Mulet et al. (2018) and is described and discussed in Good et al. (2020). The processing steps applied were: 1. The daily analyses were averaged to create monthly means. 2. A climatology was calculated by averaging the monthly means over the period 1993 - 2014. 3. Monthly anomalies were calculated by differencing the monthly means and the climatology. 4. An area averaged time series was calculated by averaging the monthly fields over the globe, with each grid cell weighted according to its area. 5. The time series was passed through the X11 seasonal adjustment procedure, which decomposes the time series into a residual seasonal component, a trend component and errors (e.g., Pezzulli et al., 2005). The trend component is a filtered version of the monthly time series. 6. The slope of the trend component was calculated using a robust method (Sen 1968). The method also calculates the 95% confidence range in the slope. CONTEXT Sea surface temperature (SST) is one of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS) as being needed for monitoring and characterising the state of the global climate system (GCOS 2010). It provides insight into the flow of heat into and out of the ocean, into modes of variability in the ocean and atmosphere, can be used to identify features in the ocean such as fronts and upwelling, and knowledge of SST is also required for applications such as ocean and weather prediction (Roquet et al., 2016). CMEMS KEY FINDINGS Over the period 1993 to 2021, the global average linear trend was 0.015 ± 0.001°C / year (95% confidence interval). 2021 is nominally the sixth warmest year in the time series. Aside from this trend, variations in the time series can be seen which are associated with changes between El Niño and La Niña conditions. For example, peaks in the time series coincide with the strong El Niño events that occurred in 1997/1998 and 2015/2016 (Gasparin et al., 2018). DOI (product):https://doi.org/10.48670/moi-00242 https://doi.org/10.48670/moi-00242 68 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-sea-ice-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011 Arctic Ocean Sea Ice Analysis and Forecast Short Description: The Arctic Sea Ice Analysis and Forecast system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, including the Canadian Archipelago, the Baffin and Hudson Bays. neXtSIM is forced with surface atmosphere forcings from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ5, the ARC MFC PHY NRT system (002_001a). neXtSIM runs daily, assimilating OSI-SAF sea ice concentrations (both SSMI and AMSR2) from the SI TAC combined with manual ice charts and providing 7-day forecasts. The output variables are the ice concentrations, ice thickness, ice drift velocity, snow depths, sea ice type, ridge area fractoin and albedo, provided at hourly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products. DOI (product) : https://doi.org/10.48670/moi-00004 https://doi.org/10.48670/moi-00004 69 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-sea-and-ice-surface-temperature-reprocessed http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_ARC_PHY_CLIMATE_L3_MY_011_021 Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED Short description: Arctic Sea and Ice surface temperature Detailed description: Arctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone. DOI (product) :https://doi.org/10.48670/moi-00315 https://doi.org/10.48670/moi-00315 70 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-fisheries-ocean-fronts https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-fisheries-ocean-fronts sis-fisheries-ocean-fronts This dataset provides monthly projections of changes in the spatial and temporal distribution of ocean fronts relative to a recent climatology. Ocean fronts are the interfaces between water masses of differing temperature, density or biological properties, which may be observed at the surface as a sharp change in temperature or colour. Fronts are recognised to enhance productivity and promote the aggregation of commercial pelagic fish species. Many front locations depend upon bathymetry, though future currents and weather have the potential to affect the strength and persistence of these features. Hence this dataset will contribute to studies of the potential for climate change to modify productive fishing zones and related marine conservation measures. Fronts are automatically detected on daily fields of sea-surface temperature (SST) and chlorophyll-a from ocean circulation models coupled to marine biogeochemical models and satellite observations within the ESA Climate Change Initiative (CCI) SST and Ocean Colour datasets. Daily fronts are combined into monthly metrics of front strength, persistence and distance. The variables are provided as monthly maps indicating changes in front strength, persistence and location, relative to a baseline of climatological monthly maps from observations or historical model runs. The primary data source for this dataset are: NEMO-ERSEM: NEMO ocean model run on the Atlantic Margin Model (7 km) domain for the NW European Shelf coupled with the marine biogeochemical ERSEM model. POLCOMS-ERSEM: POLCOMS ocean model output for the NW European Shelf and Mediterranean Sea coupled with the marine biogeochemical ERSEM model. ESA-CCI: ESA Climate Change Initiative satellite data. NEMO-ERSEM: NEMO ocean model run on the Atlantic Margin Model (7 km) domain for the NW European Shelf coupled with the marine biogeochemical ERSEM model. NEMO-ERSEM: NEMO ocean model run on the Atlantic Margin Model (7 km) domain for the NW European Shelf coupled with the marine biogeochemical ERSEM model. POLCOMS-ERSEM: POLCOMS ocean model output for the NW European Shelf and Mediterranean Sea coupled with the marine biogeochemical ERSEM model. POLCOMS-ERSEM: POLCOMS ocean model output for the NW European Shelf and Mediterranean Sea coupled with the marine biogeochemical ERSEM model. ESA-CCI: ESA Climate Change Initiative satellite data. ESA-CCI: ESA Climate Change Initiative satellite data. The hydrodynamic biogeochemical models were each driven by a global climate model generated for the Coupled Model Inter-comparison Project Phase 5 (CMIP5) at the open ocean boundaries, in combination with downscaled atmospheric data generated using the Swedish Meteorological and Hydrological Institute (SMHI) Rossby Centre Regional Atmospheric Model (RCA4). This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) Horizontal resolution POLCOMS-ERSEM: 0.1° x 0.1° (approx. 11km) NEMO-ERSEM: 0.06° x 0.06° (approx. 7km) Vertical coverage Surface Vertical resolution Variables are provided on a single level and may differ among variables Temporal coverage POLCOMS-ERSEM: 2006 up to 2100 NEMO-ERSEM: 2006 up to 2050 ESA Climate Change Initiative: 1991 to 2010 (satellite sea surface temperature) ESA Climate Change Initiative: 1997 to 2016 (satellite chlorophyll-a) Temporal resolution Monthly File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.4 DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) Horizontal coverage POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) Horizontal resolution POLCOMS-ERSEM: 0.1° x 0.1° (approx. 11km) NEMO-ERSEM: 0.06° x 0.06° (approx. 7km) Horizontal resolution POLCOMS-ERSEM: 0.1° x 0.1° (approx. 11km) NEMO-ERSEM: 0.06° x 0.06° (approx. 7km) POLCOMS-ERSEM: 0.1° x 0.1° (approx. 11km) NEMO-ERSEM: 0.06° x 0.06° (approx. 7km) Vertical coverage Surface Vertical coverage Surface Vertical resolution Variables are provided on a single level and may differ among variables Vertical resolution Variables are provided on a single level and may differ among variables Temporal coverage POLCOMS-ERSEM: 2006 up to 2100 NEMO-ERSEM: 2006 up to 2050 ESA Climate Change Initiative: 1991 to 2010 (satellite sea surface temperature) ESA Climate Change Initiative: 1997 to 2016 (satellite chlorophyll-a) Temporal coverage POLCOMS-ERSEM: 2006 up to 2100 NEMO-ERSEM: 2006 up to 2050 ESA Climate Change Initiative: 1991 to 2010 (satellite sea surface temperature) ESA Climate Change Initiative: 1997 to 2016 (satellite chlorophyll-a) POLCOMS-ERSEM: 2006 up to 2100 NEMO-ERSEM: 2006 up to 2050 ESA Climate Change Initiative: 1991 to 2010 (satellite sea surface temperature) ESA Climate Change Initiative: 1997 to 2016 (satellite chlorophyll-a) Temporal resolution Monthly Temporal resolution Monthly File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.4 Conventions Climate and Forecast (CF) Metadata Convention v1.4 MAIN VARIABLES Name Units Description Change in distance to nearest major front km The change in ocean front location, defined as the monthly mean change in the distance to the nearest major front relative to the baseline. The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Change in frontal gradient magnitude °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) Monthly mean change in thermal or chlorophyll-a frontal gradient magnitude relative to the baseline (depending on the selected indicator). The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Change in frontal persistence % The monthly mean change in ocean front persistence relative to the baseline. The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Distance to nearest major front km Distance to the nearest major ocean front using a simplified version of the frontal map. Frontal gradient magnitude °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) The monthly mean thermal or chlorophyll-a frontal gradient magnitude (depending on the selected indicator). Frontal gradient magnitude can also be referred to as ocean front strength. Generally, as the strength of an ocean front increases, so does the productivity associated with it. Frontal persistence % The fraction of cloud-free observations of a pixel for which a front was detected per month. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Change in distance to nearest major front km The change in ocean front location, defined as the monthly mean change in the distance to the nearest major front relative to the baseline. The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Change in distance to nearest major front km The change in ocean front location, defined as the monthly mean change in the distance to the nearest major front relative to the baseline. The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Change in frontal gradient magnitude °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) Monthly mean change in thermal or chlorophyll-a frontal gradient magnitude relative to the baseline (depending on the selected indicator). The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Change in frontal gradient magnitude °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) Monthly mean change in thermal or chlorophyll-a frontal gradient magnitude relative to the baseline (depending on the selected indicator). The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Change in frontal persistence % The monthly mean change in ocean front persistence relative to the baseline. The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Change in frontal persistence % The monthly mean change in ocean front persistence relative to the baseline. The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Distance to nearest major front km Distance to the nearest major ocean front using a simplified version of the frontal map. Distance to nearest major front km Distance to the nearest major ocean front using a simplified version of the frontal map. Frontal gradient magnitude °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) The monthly mean thermal or chlorophyll-a frontal gradient magnitude (depending on the selected indicator). Frontal gradient magnitude can also be referred to as ocean front strength. Generally, as the strength of an ocean front increases, so does the productivity associated with it. Frontal gradient magnitude °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) The monthly mean thermal or chlorophyll-a frontal gradient magnitude (depending on the selected indicator). Frontal gradient magnitude can also be referred to as ocean front strength. Generally, as the strength of an ocean front increases, so does the productivity associated with it. Frontal persistence % The fraction of cloud-free observations of a pixel for which a front was detected per month. Frontal persistence % The fraction of cloud-free observations of a pixel for which a front was detected per month. 71 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-sea-and-ice-surface-temperature-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_ARC_PHY_CLIMATE_L4_MY_011_016 Arctic Ocean - Sea and Ice Surface Temperature REPROCESSED Short description: Arctic Sea and Ice surface temperature Detailed description: Arctic Sea and Ice surface temperature product based upon reprocessed AVHRR, (A)ATSR and SLSTR SST observations from the ESA CCI project, the Copernicus C3S project and the AASTI dataset. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone. DOI (product) :https://doi.org/10.48670/moi-00123 https://doi.org/10.48670/moi-00123 72 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/snow-cover-extent-2018-present-raster-1-km-northern https://land.copernicus.eu/global/products/sce Snow Cover Extent 2018-present (raster 1 km), northern hemisphere, daily - version 1 Snow cover is highly sensitive to changes in temperature (freezing/thaw) and precipitation (snowfall, rain, hail) and affects directly the albedo and thus the energy balance of the Earth?s surface. It is a relevant input parameter for weather forecasts and climate change observations. Snow stores a significant mass of water and, with its high dynamic, has a strong effect on regional and global energy and water cycles. Together with the Snow Water Equivalent (SWE) product from passive microwave sensors, that provides information on the water content in the snow on plain areas, up-to-date knowledge about the snow cover extent is an important information for hydrological runoff modelling and for assessing natural hazards such as flood events. Snow cover is specified as Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). 73 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/snow-cover-extent-2017-present-raster-500-m-europe-daily https://land.copernicus.eu/global/access Snow Cover Extent 2017-present (raster 500 m), Europe, daily - version 1 Snow cover is highly sensitive to changes in temperature (freezing/thaw) and precipitation (snowfall, rain, hail) and affects directly the albedo and thus the energy balance of the Earth?s surface. It is a relevant input parameter for weather forecasts and climate change observations. Snow stores a significant mass of water and, with its high dynamic, has a strong effect on regional and global energy and water cycles. Together with the Snow Water Equivalent (SWE) product from passive microwave sensors, that provides information on the water content in the snow on plain areas, up-to-date knowledge about the snow cover extent is an important information for hydrological runoff modelling and for assessing natural hazards such as flood events. Snow cover is specified as Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). 74 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/burnt-area-2020-present-raster-300-m-global-monthly https://land.copernicus.eu/global/access Burnt Area 2020-present (raster 300 m), global, monthly - version 3 Burnt Area products map burn scars, surfaces which have been sufficiently affected by fire to display significant changes in the vegetation cover (destruction of dry material, reduction or loss of green material) and in the ground surface (temporarily darker because of ash). As fire can occur in any type of environmental context, the properties of the burnt surface may differ significantly from place to place. Therefore their identification is based on a combination of surface's properties and change detection, i.e. differences in spectral properties before and after the fire occurance. The burnt area detections are provided at 333 m resolution. 75 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/impervious-built-2018-raster-10-m-europe-3-yearly-aug https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/impervious-built-up-2018 Impervious Built-up 2018 (raster 10 m), Europe, 3-yearly, Aug. 2020 The Impervious Built-up (IBU) layer for the reference year 2018 is a thematic product showing the binary information of building (class 1) and no building (class 0) within the sealing outline derived from the Imperviousness Density layer for the period 2018 for the EEA38 countries and the United Kingdom. The production of the high resolution imperviousness layers is coordinated by the EEA in the frame of the EU Copernicus programme. The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. The dataset is provided as 10 meter rasters (fully conformant with the EEA reference grid) in 100 x 100 km tiles grouped according to the EEA38 and the United Kingdom. 76 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/iberia-biscay-ireland-sea-surface-temperature-time-series http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=IBI_OMI_TEMPSAL_sst_area_averaged_anomalies Iberia Biscay Ireland Sea Surface Temperature time series and trend from Observations Reprocessing DEFINITION The ibi_omi_tempsal_sst_area_averaged_anomalies product for 2021 includes Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1993 and averaged over the Iberia-Biscay-Irish Seas. The IBI SST OMI is built from the CMEMS Reprocessed European North West Shelf Iberai-Biscay-Irish Seas (SST_MED_SST_L4_REP_OBSERVATIONS_010_026, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf), which provided the SSTs used to compute the evolution of SST anomalies over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05° grid resolution SST maps over the European North West Shelf Iberai-Biscay-Irish Seas built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Anomalies are computed against the 1993-2014 reference period. http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf CONTEXT Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). CMEMS KEY FINDINGS The overall trend in the SST anomalies in this region is 0.011 ±0.001 °C/year over the period 1993-2021. DOI (product):https://doi.org/10.48670/moi-00256 https://doi.org/10.48670/moi-00256 77 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-era-interim https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era-interim reanalysis-era-interim ERA-Interim is the fourth generation ECMWF atmospheric reanalysis of the global climate covering the period from January 1979 to August 2019. It has been superseded by the ERA5 reanalysis and users are advised to use this newer product, instead. ERA5 ERA-Interim was produced at ECMWF and provides 3-hourly estimates of a large number of atmospheric, land and ocean-surface climate variables. The data cover the Earth on a 80km grid and resolve the atmosphere using 60 levels from the surface up to a height of 0.1 hPa (about 65km). ERA-Interim used a numerical weather prediction model that was based on a lower-resolution version of the ECMWF model that was operational in 2006 to assimilate a variety of observations, including satellite and ground-based measurements. DATA DESCRIPTION Data type Gridded Projection Atmosphere and land variables are on a reduced Gaussian grid of N128 (about 80km) Ocean wave variables are on a reduced latitude/longitude grid at a resolution of 1.0 degree Horizontal coverage Global: Horizontal resolution 80km x 80km for atmosphere and land variables (N128) 1.0 x 1.0 degrees for ocean wave variables Vertical coverage Near surface or one level for single level variables From surface to about 65km for model level variables From 1000 hPa to 1 hPa for pressure level variables Vertical resolution 60 model levels from 65km to the surface 37 pressure levels from 1000 to 1 hPa 16 potential temperature levels from 265 K to 850 K 1 potential vorticity level at 2 PVU Temporal coverage January 1979 to August 2019 Temporal resolution Analysis variables are availabe 6-hourly Forecast variables are availabe 3-hourly File format GRIB-1 Update frequency None DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Atmosphere and land variables are on a reduced Gaussian grid of N128 (about 80km) Ocean wave variables are on a reduced latitude/longitude grid at a resolution of 1.0 degree Projection Atmosphere and land variables are on a reduced Gaussian grid of N128 (about 80km) Ocean wave variables are on a reduced latitude/longitude grid at a resolution of 1.0 degree Atmosphere and land variables are on a reduced Gaussian grid of N128 (about 80km) Ocean wave variables are on a reduced latitude/longitude grid at a resolution of 1.0 degree Horizontal coverage Global: Horizontal coverage Global: Horizontal resolution 80km x 80km for atmosphere and land variables (N128) 1.0 x 1.0 degrees for ocean wave variables Horizontal resolution 80km x 80km for atmosphere and land variables (N128) 1.0 x 1.0 degrees for ocean wave variables 80km x 80km for atmosphere and land variables (N128) 1.0 x 1.0 degrees for ocean wave variables Vertical coverage Near surface or one level for single level variables From surface to about 65km for model level variables From 1000 hPa to 1 hPa for pressure level variables Vertical coverage Near surface or one level for single level variables From surface to about 65km for model level variables From 1000 hPa to 1 hPa for pressure level variables Near surface or one level for single level variables From surface to about 65km for model level variables From 1000 hPa to 1 hPa for pressure level variables Vertical resolution 60 model levels from 65km to the surface 37 pressure levels from 1000 to 1 hPa 16 potential temperature levels from 265 K to 850 K 1 potential vorticity level at 2 PVU Vertical resolution 60 model levels from 65km to the surface 37 pressure levels from 1000 to 1 hPa 16 potential temperature levels from 265 K to 850 K 1 potential vorticity level at 2 PVU 60 model levels from 65km to the surface 37 pressure levels from 1000 to 1 hPa 16 potential temperature levels from 265 K to 850 K 1 potential vorticity level at 2 PVU Temporal coverage January 1979 to August 2019 Temporal coverage January 1979 to August 2019 Temporal resolution Analysis variables are availabe 6-hourly Forecast variables are availabe 3-hourly Temporal resolution Analysis variables are availabe 6-hourly Forecast variables are availabe 3-hourly Analysis variables are availabe 6-hourly Forecast variables are availabe 3-hourly File format GRIB-1 File format GRIB-1 Update frequency None Update frequency None 78 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-energy-derived-projections https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-energy-derived-projections sis-energy-derived-projections This dataset provides climate and energy indicators for the Europe as part of the Copernicus climate change service (C3S) Energy operational service. The climate-relevant indicators for the energy sector considered are: air temperature, precipitation, incoming solar radiation, wind speed at 10 m and 100 m, and mean sea level air pressure. The energy indicators are electricity demand and power generation from various sources: wind (both onshore and offshore), solar and hydro (run-of-river and reservoir) power. Depending on the indicator, the data are available at the national, regional and grid (approximately 30x30 km) level for most European countries. The spatial aggregation of data over land uses the Eurostat NUTS0 & NUTS2 (Nomenclature des unités territoriales statistiques, 2016) regions. The offshore variables (e.g. offshore wind power) use the European maritime region definitions MAR0 and MAR1. Further information on the NUTS and MAR regions can be found in the documentation. Data is provided for the European domain, in a multi-variable, multi-timescale view of the climate and energy systems. This is beneficial in anticipating important climate-driven changes in the energy sector, through either long-term planning or medium-term operational activities. This is also used to investigate the role of temperature on electricity demand across Europe, as well as its interaction with the variability of renewable energy generation. The C3S Energy operational service is composed of three main streams: historical (1979-present), seasonal forecasts and projections (typically covering the period 2005-2100). This projections dataset (2005-2100) produces reference climate variables based on the European regional climate model experiment, CORDEX. Energy variables are generated by transforming the climate variables using a combination of statistical models and physically based data. A comprehensive set of measured energy supply and demand data has been collected from various sources such as the European Network of Transmission System Operators (ENTSO-E). These data provide a crucial reference to assess the robustness of the models used to convert climate into electric energy variables. DATA DESCRIPTION Data type Gridded and aggregated over shapes Horizontal coverage European domain (27N - 72N; 22W – 45E) Horizontal resolution 0.25° x 0.25° Vertical coverage 0 to 100 metres depending on the variable Vertical resolution Single level Temporal coverage From 2005 to 2100 Temporal resolution 3 hourly or daily File format NetCDF or CSV (spatial aggregation dependant) Versions Only one version of the dataset is available Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded and aggregated over shapes Data type Gridded and aggregated over shapes Horizontal coverage European domain (27N - 72N; 22W – 45E) Horizontal coverage European domain (27N - 72N; 22W – 45E) Horizontal resolution 0.25° x 0.25° Horizontal resolution 0.25° x 0.25° Vertical coverage 0 to 100 metres depending on the variable Vertical coverage 0 to 100 metres depending on the variable Vertical resolution Single level Vertical resolution Single level Temporal coverage From 2005 to 2100 Temporal coverage From 2005 to 2100 Temporal resolution 3 hourly or daily Temporal resolution 3 hourly or daily File format NetCDF or CSV (spatial aggregation dependant) File format NetCDF or CSV (spatial aggregation dependant) Versions Only one version of the dataset is available Versions Only one version of the dataset is available Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description 2m air temperature K The ambient air temperature near to the surface, typically at height of 2m. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Electricity demand MWh, MW (or multiple there of, e.g. GW and GWh) Electricity Demand is the consumption of electricity expressed in energy units (MWh or GWh) or as mean power (MW or GW). The data is provided at the country level (NUTS level 0). Solar photovoltaic power generation Dimensionless, MWh or MW Solar photovoltaic power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Surface downwelling shortwave radiation W m-2 The amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Values are derived from ERA5 surface downwelling shortwave radiation and bias corrected using Climate Research Unit (CRU) cloud cover and effects of inter-annual changes in atmospheric aerosol loading. Total precipitation m Depth of rain water accumulated on a flat, horizontal and impermeable surface per unit area during a given time period. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are accumulated measures. Wind power generation offshore Dimensionless, MWh or MW Offshore wind power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point and maritime regions (MAR level 0 and MAR level 1). The data values are instantaneous measures. Wind power generation onshore Dimensionless, MWh or MW Onshore wind power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Wind speed at 100m m s-1 Magnitude of the two-dimensional horizontal air velocity at 100 metres. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2) and maritime regions (MAR level 0 and MAR level 1). The data values are instantaneous measures. Wind speed at 10m m s-1 Magnitude of the two-dimensional horizontal air velocity at 10 metres. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2) and maritime regions (MAR level 0 and MAR level 1). The data values are instantaneous measures. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 2m air temperature K The ambient air temperature near to the surface, typically at height of 2m. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. 2m air temperature K The ambient air temperature near to the surface, typically at height of 2m. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Electricity demand MWh, MW (or multiple there of, e.g. GW and GWh) Electricity Demand is the consumption of electricity expressed in energy units (MWh or GWh) or as mean power (MW or GW). The data is provided at the country level (NUTS level 0). Electricity demand MWh, MW (or multiple there of, e.g. GW and GWh) Electricity Demand is the consumption of electricity expressed in energy units (MWh or GWh) or as mean power (MW or GW). The data is provided at the country level (NUTS level 0). Solar photovoltaic power generation Dimensionless, MWh or MW Solar photovoltaic power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Solar photovoltaic power generation Dimensionless, MWh or MW Solar photovoltaic power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Surface downwelling shortwave radiation W m-2 The amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Values are derived from ERA5 surface downwelling shortwave radiation and bias corrected using Climate Research Unit (CRU) cloud cover and effects of inter-annual changes in atmospheric aerosol loading. Surface downwelling shortwave radiation W m-2 The amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Values are derived from ERA5 surface downwelling shortwave radiation and bias corrected using Climate Research Unit (CRU) cloud cover and effects of inter-annual changes in atmospheric aerosol loading. Total precipitation m Depth of rain water accumulated on a flat, horizontal and impermeable surface per unit area during a given time period. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are accumulated measures. Total precipitation m Depth of rain water accumulated on a flat, horizontal and impermeable surface per unit area during a given time period. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are accumulated measures. Wind power generation offshore Dimensionless, MWh or MW Offshore wind power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point and maritime regions (MAR level 0 and MAR level 1). The data values are instantaneous measures. Wind power generation offshore Dimensionless, MWh or MW Offshore wind power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point and maritime regions (MAR level 0 and MAR level 1). The data values are instantaneous measures. Wind power generation onshore Dimensionless, MWh or MW Onshore wind power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Wind power generation onshore Dimensionless, MWh or MW Onshore wind power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Wind speed at 100m m s-1 Magnitude of the two-dimensional horizontal air velocity at 100 metres. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2) and maritime regions (MAR level 0 and MAR level 1). The data values are instantaneous measures. Wind speed at 100m m s-1 Magnitude of the two-dimensional horizontal air velocity at 100 metres. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2) and maritime regions (MAR level 0 and MAR level 1). The data values are instantaneous measures. Wind speed at 10m m s-1 Magnitude of the two-dimensional horizontal air velocity at 10 metres. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2) and maritime regions (MAR level 0 and MAR level 1). The data values are instantaneous measures. Wind speed at 10m m s-1 Magnitude of the two-dimensional horizontal air velocity at 10 metres. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2) and maritime regions (MAR level 0 and MAR level 1). The data values are instantaneous measures. 79 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-european-wind-storm-synthetic-events https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-wind-storm-synthetic-events sis-european-wind-storm-synthetic-events This dataset contains a set of synthetic windstorm events consisting of 22,980 individual storm footprints over Europe. These are a physically realistic set of plausible windstorm events based on the modelled climatic conditions. It is not designed to reproduce actual historical observations but as a comparator for the stochastic event sets generally used for windstorm risk analysis in the insurance industry. This is because there is no data assimilation process used to align the model output to historical observations. The dataset aims to capture the windstorms' risk profile over the winter months and consider the characteristics of windstorms in a warming climate. The dataset may be downloaded by selecting the required years and months from which daily files are produced. These are there as mere reference; the associated storms bare no resembelence to what was actually observed and recorded at the time. The windstorms are identified from the maximum 3 second wind gusts derived from the 10m wind speed from the Met Office HadGEM3 model using the Global Atmosphere 3 and Global Land 3 configurations. The dataset covers the northern hemisphere winter months between September and May only. It includes three individual iterations of the event sets, whose methodology and inputs are slightly different: Synthetic set 1.2: recalibrated using four historical windstorm events; Xynthia, Kyrill, Daria and 87J. Synthetic set 2: A storm severity index combining maximum wind gust speed above a threshold of 25 m s-1 and land area was used to select the strongest six events from the Met Office historical events database. Synthetic set 3: downscaling was based on station observations from the four named storms used in Synthetic event set v1.2; Xynthia, Kyrill, Daria and 87J. Synthetic set 1.2: recalibrated using four historical windstorm events; Xynthia, Kyrill, Daria and 87J. Synthetic set 1.2: recalibrated using four historical windstorm events; Xynthia, Kyrill, Daria and 87J. Synthetic set 2: A storm severity index combining maximum wind gust speed above a threshold of 25 m s-1 and land area was used to select the strongest six events from the Met Office historical events database. Synthetic set 2: A storm severity index combining maximum wind gust speed above a threshold of 25 m s-1 and land area was used to select the strongest six events from the Met Office historical events database. Synthetic set 3: downscaling was based on station observations from the four named storms used in Synthetic event set v1.2; Xynthia, Kyrill, Daria and 87J. Synthetic set 3: downscaling was based on station observations from the four named storms used in Synthetic event set v1.2; Xynthia, Kyrill, Daria and 87J. Five simulation ensembles initialized from one of five consecutive days and allowed to evolve independently were used to increase the number of events compared to the input data and to account for the uncertainty in forecasted climatic conditions. The three sets and all simulation ensembles may be combined and used together in further analysis. This synthetic event set is complementary to the catalogue of actual windstorms in Europe from 1979 to 2021 also available in the Climate Data Store. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage 25W to 40.5E, 34.4N to 71.5N Horizontal resolution 25km regridded to 4.4km by linear interpolation Vertical coverage Surface Vertical resolution Single level Temporal coverage From 1986 to 2011; September-May Temporal resolution 72 hours, centred on the time of the maximum vorticity at 850 hPa along the storm track File format NetCDF-4 Conventions Climate and Forecast Metadata Convention v1.6 Versions 1.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage 25W to 40.5E, 34.4N to 71.5N Horizontal coverage 25W to 40.5E, 34.4N to 71.5N Horizontal resolution 25km regridded to 4.4km by linear interpolation Horizontal resolution 25km regridded to 4.4km by linear interpolation Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage From 1986 to 2011; September-May Temporal coverage From 1986 to 2011; September-May Temporal resolution 72 hours, centred on the time of the maximum vorticity at 850 hPa along the storm track Temporal resolution 72 hours, centred on the time of the maximum vorticity at 850 hPa along the storm track File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast Metadata Convention v1.6 Conventions Climate and Forecast Metadata Convention v1.6 Versions 1.0 Versions 1.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Wind speed of gusts m s-1 Maximum 3s wind gust at 10m over a 72-hour period centred on time of the maximum vorticity at 850 hPa along the storm track. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Wind speed of gusts m s-1 Maximum 3s wind gust at 10m over a 72-hour period centred on time of the maximum vorticity at 850 hPa along the storm track. Wind speed of gusts m s-1 Maximum 3s wind gust at 10m over a 72-hour period centred on time of the maximum vorticity at 850 hPa along the storm track. 80 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-surface-temperature-anomaly-time-series-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTIC_OMI_TEMPSAL_sst_area_averaged_anomalies Baltic Sea Surface Temperature anomaly time series and trend from Observations Reprocessing DEFINITION The BALTIC_OMI_TEMPSAL_sst_area_averaged_anomalies product includes time series of monthly mean SST anomalies over the period 1993-2021, relative to the 1993-2014 climatology, averaged for the Baltic Sea. The OMI time series runs from Jan 1, 1993 to December 31, 2021 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 product from 1993 to 2021. See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the OMI product. CONTEXT Sea Surface Temperature (SST) is an Essential Climate Variable (GCOS), that is an important input for initialis-ing numerical weather prediction models and fundamental for understanding air-sea interactions and moni-toring climate change (GCOS 2010). The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (Høyer and She 2007; Høyer and Karagali 2016). The Baltic Sea is a semi-enclosed basin affected bynatural variability, influenced by large-scale atmos-pheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which re-quires dedicated regional and validated SST products. Satellite observations have previously been used to ana-lyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, Høyer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2 oC from 1982 to 2012 considering all months of the year and 3-5 oC when only July-September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016 and section 3 in Von Schuckmann et al., 2018). CMEMS KEY FINDINGS The basin-average trend of SST anomalies for Baltic Sea region amounts to 0.049±0.006 °C/year over the pe-riod 1993-2021 which corresponds to an average warming of 1.42°C. Adding the North Sea area, the average trend amounts to 0.03±0.003 °C/year over the same period, which corresponds to an average warming of 0.87°C for the entire region since 1993. DOI (product):https://doi.org/10.48670/moi-00205 https://doi.org/10.48670/moi-00205 81 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-1990-raster-100-m-europe-6-yearly https://land.copernicus.eu/pan-european/corine-land-cover/clc-1990/view CORINE Land Cover 1990 (raster 100 m), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover 1990 (CLC1990) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 1990. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 82 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-change-2012-2018-raster-100-m-europe-6 https://land.copernicus.eu/pan-european/corine-land-cover/lcc-2012-2018 CORINE Land Cover Change 2012-2018 (raster 100 m), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover Change 2012-2018 (CHA1218) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to changes in land cover / land use status between the years 2012 and 2018. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (CHA), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 83 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-change-2000-2006-raster-100-m-europe-6 https://land.copernicus.eu/pan-european/corine-land-cover/lcc-2000-2006/view CORINE Land Cover Change 2000-2006 (raster 100 m), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover Change 2000-2006 (CHA0006) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to changes in land cover / land use status between the years 2000 and 2006. CHA is derived from satellite imagery by direct mapping of changes taken place between two consecutive inventories, based on image-to-image comparison. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (CHA), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 84 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-change-1990-2000-vector-europe-6-yearly https://land.copernicus.eu/pan-european/corine-land-cover/lcc-1990-2000/view CORINE Land Cover Change 1990-2000 (vector), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover Change 1990-2000 (CHA9000) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to changes in land cover / land use status between the years 1990 and 2000. CHA is derived from satellite imagery by direct mapping of changes taken place between two consecutive inventories, based on image-to-image comparison. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (CHA), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 85 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-sst-esa-cci https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sst-esa-cci satellite-sst-esa-cci This deprecated dataset provides daily values for sea surface temperature (SST) and sea ice fraction over a regular grid with no missing values in space or in time. The initial satellite data from the Along Track Scanning Radiometer (ATSR) and from the Advanced Very High Resolution Radiometer (AVHRR) sensors where interpolated and combined using the NEMOVAR and the OSTIA algorithms. deprecated sea surface temperature sea ice fraction Sea surface temperature plays important roles in the exchanges of energy, momentum, moisture and gases between the ocean and atmosphere. The final records provided in this dataset have sufficient length, consistency, and continuity to detect climate variability and change in sea surface temperature. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 0.05°x0.05° Temporal coverage 1991 to 2010 Temporal resolution Daily File format NetCDF DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.05°x0.05° Horizontal resolution 0.05°x0.05° Temporal coverage 1991 to 2010 Temporal coverage 1991 to 2010 Temporal resolution Daily Temporal resolution Daily File format NetCDF File format NetCDF MAIN VARIABLES Name Units Description Sea ice fraction % Fraction of the cell covered by sea ice Sea surface temperature K Analysed sea surface temperature corresponding approximately to the daily average of the temperature of the water at 20cm depth MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Sea ice fraction % Fraction of the cell covered by sea ice Sea ice fraction % Fraction of the cell covered by sea ice Sea surface temperature K Analysed sea surface temperature corresponding approximately to the daily average of the temperature of the water at 20cm depth Sea surface temperature K Analysed sea surface temperature corresponding approximately to the daily average of the temperature of the water at 20cm depth RELATED VARIABLES A number of variables accounting for the uncertainty on the data provided are also included in the files along the main variables. They provide estimates on possible variations of the values of the main variables due to changes in processing and sampling algorithms. A variable containing information on whether there is ocean, land or sea ice in each grid cell is also provided. RELATED VARIABLES RELATED VARIABLES A number of variables accounting for the uncertainty on the data provided are also included in the files along the main variables. They provide estimates on possible variations of the values of the main variables due to changes in processing and sampling algorithms. A variable containing information on whether there is ocean, land or sea ice in each grid cell is also provided. A number of variables accounting for the uncertainty on the data provided are also included in the files along the main variables. They provide estimates on possible variations of the values of the main variables due to changes in processing and sampling algorithms. A variable containing information on whether there is ocean, land or sea ice in each grid cell is also provided. 86 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-change-2000-2006-vector-europe-6-yearly https://land.copernicus.eu/pan-european/corine-land-cover/lcc-2000-2006/view CORINE Land Cover Change 2000-2006 (vector), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover Change 2000-2006 (CHA0006) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to changes in land cover / land use status between the years 2000 and 2006. CHA is derived from satellite imagery by direct mapping of changes taken place between two consecutive inventories, based on image-to-image comparison. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (CHA), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 87 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/coastal-zones-land-coverland-use-change-2012-2018-vector https://land.copernicus.eu/local/coastal-zones/coastal-zones-change-2012-2018?tab=download Coastal Zones Land Cover/Land Use Change 2012-2018 (vector), Europe, 6-yearly, Feb. 2021 The Coastal Zones LC/LU Change (CZC) 2012-2018 is providing the Land Cover / Land Use (LC/ LU) change for areas along the coastline of the EEA38 countries and the United Kingdom, between the reference years 2012 and 2018. The Coastal Zones product monitors landscape dynamics in European coastal territory to an inland depth of 10 km with a total area of approximately 730,000 km², with all the relevant areas (estuaries, coastal lowlands, nature reserves). The production of the coastal zone layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme, as part of the Copernicus Land Monitoring Service (CLMS) Local Component. The Coastal Zones Change product covers a buffer zone of coastline derived from EU-Hydro v1.1. The Land Cover/Land Use (LC/LU) Change layer is extracted from Very High Resolution (VHR) satellite data and other available data. The reference years for the change are 2012 and 2018. The class definitions follow the pre-defined nomenclature on the basis of Mapping and Assessment of Ecosystems and their Services (MAES) typology of ecosystems (Level 1 to Level 4) and CORINE Land Cover adapted to the specific characteristics of coastal zones. The classification provides 71 distinct thematic classes with a Minimum Mapping Unit (MMU) of 0.5 ha and a Minimum Mapping Width (MMW) of 10 m. The status product is available for the 2012 and 2018 reference years. This CZC dataset is distributed in vector format, in a single OGC GeoPackage file covering the area of interest. 88 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/nordic-seas-volume-transports-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_CIRCULATION_VOLTRANS_ARCTIC_averaged Nordic Seas Volume Transports from Reanalysis DEFINITION Net (positive minus negative) volume transport of Atlantic Water through the sections (see Figure 1): Faroe Shetland Channel (Water mass criteria, T > 5 °C); Barents Sea Opening (T > 3 °C) and the Fram Strait (T > 2 °C). Net volume transport of Overflow Waters (σθ >27.8 kg/m3) exiting from the Nordic Seas to the North Atlantic via the Denmark Strait and Faroe Shetland Channel. For further details, see Ch. 3.2 in von Schuckmann et al. (2018). CONTEXT The poleward flow of relatively warm and saline Atlantic Water through the Nordic Seas to the Arctic Basin, balanced by the overflow waters exiting the Nordic Seas, governs the exchanges between the North Atlantic and the Arctic as well as the distribution of oceanic heat within the Arctic (e.g., Mauritzen et al., 2011; Rudels, 2012). Atlantic Water transported poleward has been found to significantly influence the sea-ice cover in the Barents Sea (Sandø et al., 2010; Årthun et al., 2012; Onarheim et al., 2015) and near Svalbard (Piechura and Walczowski, 2009). Furthermore, Atlantic Water flow through the eastern Nordic seas and its associated heat loss and densification are important factors for the formation of overflow waters in the region (Mauritzen, 1996; Eldevik et al., 2009). These overflow waters together with those generated in the Arctic, exit the Greenland Scotland Ridge, which further contribute to the North Atlantic Deep Water (Dickson and Brown, 1994) and thus play an important role in the Atlantic Meridional Overturning Circulation (Eldevik et al., 2009; Ch. 2.3 in von Schuckmann et al., 2016). In addition to the transport of heat, the Atlantic Water also transports nutrients and zooplankton (e.g., Sundby, 2000), and it carries large amounts of ichthyoplankton of commercially important species, such as Arcto-Norwegian cod (Gadus morhua) and Norwegian spring-spawning herring (Clupea harengus) along the Norwegian coast. The Atlantic Water flow thus plays an integral part in defining both the physical and biological border between the boreal and Arctic realm. Variability of Atlantic Water flow to the Barents Sea has been found to move the position of the ice edge (Onarheim et al., 2015) as well as habitats of various species in the Barents Sea ecosystem (Fossheim et al., 2015). CMEMS KEY FINDINGS The flow of Atlantic Water through the Færøy-Shetland Channel amounts to 2.7 Sv (Berx et al., 2013). The corresponding model-based estimate was 2.5 Sv for the period 1993-2021. In the Barents Sea Opening, the model indicates a long-term average net Atlantic Water inflow of 2.2 Sv, as compared with the long-term estimate from observations of 1.8 Sv (Smedsrud et al., 2013). In the Fram Strait, the model data indicates a positive trend in the Atlantic Water transport to the Arctic. This trend may be explained by increased temperature in the West Spitsbergen Current during the period 2005-2010 (e.g., Walczowski et al., 2012), which caused a larger fraction of the water mass to be characterized as Atlantic Water (T > 2 °C). DOI (product):https://doi.org/10.48670/moi-00189 https://doi.org/10.48670/moi-00189 89 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-change-1990-2000-raster-100-m-europe-6 https://land.copernicus.eu/pan-european/corine-land-cover/lcc-1990-2000/view CORINE Land Cover Change 1990-2000 (raster 100 m), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover Change 1990-2000 (CHA9000) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to changes in land cover / land use status between the years 1990 and 2000. CHA is derived from satellite imagery by direct mapping of changes taken place between two consecutive inventories, based on image-to-image comparison. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (CHA), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 90 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-change-2006-2012-vector-europe-6-yearly https://land.copernicus.eu/pan-european/corine-land-cover/lcc-2006-2012/view CORINE Land Cover Change 2006-2012 (vector), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover Change 2006-2012 (CHA0612) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to changes in land cover / land use status between the years 2006 and 2012. CHA is derived from satellite imagery by direct mapping of changes taken place between two consecutive inventories, based on image-to-image comparison. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (CHA), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 91 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/odyssea-global-sea-surface-temperature-gridded-level-4 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_GLO_PHY_L4_NRT_010_043 ODYSSEA Global Sea Surface Temperature Gridded Level 4 Daily Multi-Sensor Observations This dataset provide a times series of gap free map of Sea Surface Temperature (SST) foundation at high resolution on a 0.10 x 0.10 degree grid (approximately 10 x 10 km) for the Global Ocean, every 24 hours. Whereas along swath observation data essentially represent the skin or sub-skin SST, the Level 4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed. The processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. An optimal interpolation is used to fill in where observations are missing. DOI (product) :https://doi.org/10.48670/mds-00321 https://doi.org/10.48670/mds-00321 92 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-high-resolution-diurnal-subskin-sea http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_MED_PHY_SUBSKIN_L4_NRT_010_036 Mediterranean Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis Short description: For the Mediterranean Sea - the CNR diurnal sub-skin Sea Surface Temperature (SST) product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16° (0.0625°) horizontal resolution over the CMEMS Mediterranean Sea (MED) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS MED Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014). [https://help.marine.copernicus.eu/en/articles/4444611-how-to-cite-or-re… How to cite] https://help.marine.copernicus.eu/en/articles/4444611-how-to-cite-or-re… DOI (product) :https://doi.org/10.48670/moi-00170 https://doi.org/10.48670/moi-00170 93 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-uerra-europe-single-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-uerra-europe-single-levels reanalysis-uerra-europe-single-levels This UERRA dataset contains analyses of surface and near-surface essential climate variables from UERRA-HARMONIE and MESCAN-SURFEX systems. Forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC are available only through the CDS-API (see Documentation). UERRA-HARMONIE is a 3-dimensional variational data assimilation system, while MESCAN-SURFEX is a complementary surface analysis system. Using the Optimal Interpolation method, MESCAN provides the best estimate of daily accumulated precipitation and six-hourly air temperature and relative humidity at 2 meters above the model topography. The land surface platform SURFEX is forced with downscaled forecast fields from UERRA-HARMONIE as well as MESCAN analyses. It is run offline, i.e. without feedback to the atmospheric analysis performed in MESCAN or the UERRA-HARMONIE data assimilation cycles. Using SURFEX offline allows to take full benefit of precipitation analysis and to use the more advanced physics options to better represent surface variables such as surface temperature and surface fluxes, and soil processes related to water and heat transfer in the soil and snow. In general, the assimilation systems are able to estimate biases between observations and to sift good-quality data from poor data. The laws of physics allow for estimates at locations where data coverage is low. The provision of estimates at each grid point in Europe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with. The observing system has changed drastically over time, and although the assimilation system can resolve data holes, the much sparser observational networks, e.g. in 1960s, will have an impact on the quality of analyses leading to less accurate estimates. The improvement over global reanalysis products comes with the higher horizontal resolution that allows incorporating more regional details (e.g. topography). Moreover, it enables the system even to use more observations at places with dense observation networks. DATA DESCRIPTION Data type Gridded Projection Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Horizontal coverage Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Horizontal resolution 11km x 11km for the UERRA-HARMONIE system. 5.5km x 5.5km for the MESCAN-SURFEX system. Vertical coverage Near surface. Vertical resolution Single level. Temporal coverage January 1961 to July 2019. Temporal resolution Analysis are availabe each day at 00, 06, 12 and 18 UTC. File format GRIB2 Update frequency No expected updates. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Projection Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Horizontal coverage Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Horizontal coverage Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Horizontal resolution 11km x 11km for the UERRA-HARMONIE system. 5.5km x 5.5km for the MESCAN-SURFEX system. Horizontal resolution 11km x 11km for the UERRA-HARMONIE system. 5.5km x 5.5km for the MESCAN-SURFEX system. 11km x 11km for the UERRA-HARMONIE system. 5.5km x 5.5km for the MESCAN-SURFEX system. Vertical coverage Near surface. Vertical coverage Near surface. Vertical resolution Single level. Vertical resolution Single level. Temporal coverage January 1961 to July 2019. Temporal coverage January 1961 to July 2019. Temporal resolution Analysis are availabe each day at 00, 06, 12 and 18 UTC. Temporal resolution Analysis are availabe each day at 00, 06, 12 and 18 UTC. File format GRIB2 File format GRIB2 Update frequency No expected updates. Update frequency No expected updates. MAIN VARIABLES Name Units Description 10m wind direction Degrees Wind direction valid for a grid cell at the height of 10m above the surface. Values are in the interval [0,360). A value of 0° means a northerly wind and 90° indicates an easterly wind. 10m wind speed m s-1 Wind speed valid for a grid cell at the height of 10m above the surface. It is computed from both the zonal (u) and the meridional (v) wind components by sqrt(u2 + v2 ). 2m relative humidity % Relation between actual humidity and saturation humidity. Values are in the interval [0,100]. 0%means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. 2m temperature K Air temperature valid for a grid cell at the height of 2m above the surface. Albedo % Amount of radiation reflected by a grid cell, both for ground and water surfaces, relatively to the incoming radiation. Small values mean that large amounts of the radiation are absorbed whereas large values mean that more radiation is reflected. High cloud cover % Percentage of the grid cell for which the sky is covered with clouds at high altitude. Land sea mask Dimensionless The values are between 0 (sea) and 1 (land) and are constant over time. Low cloud cover % Percentage of the grid cell for which the sky is covered with clouds at low altitude. Mean sea level pressure Pa Air pressure in the grid cell reduced to mean sea level. Medium cloud cover % Percentage of the grid cell for which the sky is covered with clouds at medium altitude. Orography gpm (geopotential height in meter) Average height of the surface grid cell with respect to the model defined globe. Skin temperature K Boundary temperature in grid cells between the ground and water surfaces and the atmosphere above. Snow density kg m-3 Average density of snow over a grid cell. Snow depth water equivalent Kg m-2 Amount of snow in kg over a square meter in average on a grid cell. Surface pressure Pa Air pressure in the grid cell at the land and water surface. Surface roughness m Mean value over a grid cell of the aerodynamic roughness length. Only values over land are available. Total cloud cover % Percentage of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. Total column integrated water vapour kg m-2 Total amount of water vapour from surface to the top of the atmosphere for each grid cell. Total precipitation kg m-2 Amount of water falling onto the ground/water surface. It includes all kind of precipitation forms as convective precipitation, large scale precipitation, liquid and solid. It is an accumulated parameter over the 24 hours from 06:00 to 06:00 of the next day. Values are valid for a grid cell. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 10m wind direction Degrees Wind direction valid for a grid cell at the height of 10m above the surface. Values are in the interval [0,360). A value of 0° means a northerly wind and 90° indicates an easterly wind. 10m wind direction Degrees Wind direction valid for a grid cell at the height of 10m above the surface. Values are in the interval [0,360). A value of 0° means a northerly wind and 90° indicates an easterly wind. 10m wind speed m s-1 Wind speed valid for a grid cell at the height of 10m above the surface. It is computed from both the zonal (u) and the meridional (v) wind components by sqrt(u2 + v2 ). 10m wind speed m s-1 Wind speed valid for a grid cell at the height of 10m above the surface. It is computed from both the zonal (u) and the meridional (v) wind components by sqrt(u2 + v2 ). 2m relative humidity % Relation between actual humidity and saturation humidity. Values are in the interval [0,100]. 0%means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. 2m relative humidity % Relation between actual humidity and saturation humidity. Values are in the interval [0,100]. 0%means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. 2m temperature K Air temperature valid for a grid cell at the height of 2m above the surface. 2m temperature K Air temperature valid for a grid cell at the height of 2m above the surface. Albedo % Amount of radiation reflected by a grid cell, both for ground and water surfaces, relatively to the incoming radiation. Small values mean that large amounts of the radiation are absorbed whereas large values mean that more radiation is reflected. Albedo % Amount of radiation reflected by a grid cell, both for ground and water surfaces, relatively to the incoming radiation. Small values mean that large amounts of the radiation are absorbed whereas large values mean that more radiation is reflected. High cloud cover % Percentage of the grid cell for which the sky is covered with clouds at high altitude. High cloud cover % Percentage of the grid cell for which the sky is covered with clouds at high altitude. Land sea mask Dimensionless The values are between 0 (sea) and 1 (land) and are constant over time. Land sea mask Dimensionless The values are between 0 (sea) and 1 (land) and are constant over time. Low cloud cover % Percentage of the grid cell for which the sky is covered with clouds at low altitude. Low cloud cover % Percentage of the grid cell for which the sky is covered with clouds at low altitude. Mean sea level pressure Pa Air pressure in the grid cell reduced to mean sea level. Mean sea level pressure Pa Air pressure in the grid cell reduced to mean sea level. Medium cloud cover % Percentage of the grid cell for which the sky is covered with clouds at medium altitude. Medium cloud cover % Percentage of the grid cell for which the sky is covered with clouds at medium altitude. Orography gpm (geopotential height in meter) Average height of the surface grid cell with respect to the model defined globe. Orography gpm (geopotential height in meter) Average height of the surface grid cell with respect to the model defined globe. Skin temperature K Boundary temperature in grid cells between the ground and water surfaces and the atmosphere above. Skin temperature K Boundary temperature in grid cells between the ground and water surfaces and the atmosphere above. Snow density kg m-3 Average density of snow over a grid cell. Snow density kg m-3 Average density of snow over a grid cell. Snow depth water equivalent Kg m-2 Amount of snow in kg over a square meter in average on a grid cell. Snow depth water equivalent Kg m-2 Amount of snow in kg over a square meter in average on a grid cell. Surface pressure Pa Air pressure in the grid cell at the land and water surface. Surface pressure Pa Air pressure in the grid cell at the land and water surface. Surface roughness m Mean value over a grid cell of the aerodynamic roughness length. Only values over land are available. Surface roughness m Mean value over a grid cell of the aerodynamic roughness length. Only values over land are available. Total cloud cover % Percentage of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. Total cloud cover % Percentage of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. Total column integrated water vapour kg m-2 Total amount of water vapour from surface to the top of the atmosphere for each grid cell. Total column integrated water vapour kg m-2 Total amount of water vapour from surface to the top of the atmosphere for each grid cell. Total precipitation kg m-2 Amount of water falling onto the ground/water surface. It includes all kind of precipitation forms as convective precipitation, large scale precipitation, liquid and solid. It is an accumulated parameter over the 24 hours from 06:00 to 06:00 of the next day. Values are valid for a grid cell. Total precipitation kg m-2 Amount of water falling onto the ground/water surface. It includes all kind of precipitation forms as convective precipitation, large scale precipitation, liquid and solid. It is an accumulated parameter over the 24 hours from 06:00 to 06:00 of the next day. Values are valid for a grid cell. RELATED VARIABLES In order to make data access more manageable, the UERRA dataset has been split into several records. Complementary records to the present one are: UERRA on height levels, UERRA on pressure levels and UERRA on soil levels. For the present single level dataset, forecast data are not accessible through this form. However, the complete dataset can be accessed through the CDS application programming interface (API). RELATED VARIABLES RELATED VARIABLES In order to make data access more manageable, the UERRA dataset has been split into several records. Complementary records to the present one are: UERRA on height levels, UERRA on pressure levels and UERRA on soil levels. For the present single level dataset, forecast data are not accessible through this form. However, the complete dataset can be accessed through the CDS application programming interface (API). In order to make data access more manageable, the UERRA dataset has been split into several records. Complementary records to the present one are: UERRA on height levels, UERRA on pressure levels and UERRA on soil levels. For the present single level dataset, forecast data are not accessible through this form. However, the complete dataset can be accessed through the CDS application programming interface (API). 94 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-tourism-fire-danger-indicators https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-tourism-fire-danger-indicators sis-tourism-fire-danger-indicators The dataset presents projections of fire danger indicators for Europe based upon the Canadian Fire Weather Index System (FWI) under future climate conditions. The FWI is a meteorologically based index used worldwide to estimate the fire danger and is implemented in the Global ECMWF Fire Forecasting model (GEFF). In this dataset, daily FWI values, seasonal FWI values, and other FWI derived, threshold-specific, indicators were modelled using the GEFF model to estimate the fire danger in future climate scenarios. These indicators include the number of days with moderate, high, or very high fire danger conditions as classified by the European Forest Fire Information System (EFFIS) during the northern hemisphere's fire season (June-September): very low: <5.2 low: 5.2 - 11.2 moderate: 11.2 - 21.3 high: 21.3 - 38.0 very high: 38.0 - 50 extreme: >=50.0 very low: <5.2 low: 5.2 - 11.2 moderate: 11.2 - 21.3 high: 21.3 - 38.0 very high: 38.0 - 50 extreme: >=50.0 This dataset may serve to assess future fire danger conditions for regions across Europe and support the development of a long-term tourism strategy to reduce the risk of forest fires on nature-based tourism infrastructure. The FWI is a meteorologically based index that accounts for the effect of fuel moisture and weather conditions on fire behaviour. Daily noon values of air temperature, relative humidity, wind speed and 24-h accumulated precipitation are required for the calculation of the index. In order to attain the meteorological variables, projections from multiple global climate models downscaled to a regional climate model were used as input to the GEFF model. The climate models were developed within the EURO-CORDEX initiative, providing high-resolution and comparable model output centered on the European domain. In order to assess the impact of climate change, the GEFF model is run for four different climate scenarios: the present climate (labelled 'historical'), and three Representative Concentration Pathway (RCP) scenarios consistent with an optimistic emission scenario where emissions start declining beyond 2020 (RCP2.6), a scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century (RCP8.5). Historical simulations, for the period 1970-2005, are included to provide a reference for the FWI projections. An estimate of the statistical uncertainty associated with climate projections may be derived with the use of multiple climate model outcomes. This may be achieved by the user both implicitly or explicitly by selecting from a choice of mean, best case, or worst case multi-model outcomes. It should be noted, however, that the multi-model approach may improve the robustness of the outcomes but does not take into account all possible aspects of uncertainty associated with modelling a future climate. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage Europe Horizontal resolution 0.11° x 0.11° Vertical coverage Surface Vertical resolution Single level Temporal coverage 1970-2098 Temporal resolution Daily, seasonal and annual File format NetCDF-4 Versions 1.0 (deprecated), 2.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Europe Horizontal coverage Europe Horizontal resolution 0.11° x 0.11° Horizontal resolution 0.11° x 0.11° Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage 1970-2098 Temporal coverage 1970-2098 Temporal resolution Daily, seasonal and annual Temporal resolution Daily, seasonal and annual File format NetCDF-4 File format NetCDF-4 Versions 1.0 (deprecated), 2.0 Versions 1.0 (deprecated), 2.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Daily fire weather index Dimensionless The fire weather index values at a daily temporal resolution for the selected year. The higher the index value, the more favorable the meteorological conditions to trigger a wildfire are. Number of days with high fire danger Count Number of days per year with a fire weather index greater than 30 based upon the European Forest Fire Information System (EFFIS) classification. Number of days with moderate fire danger Count The number of days per year with a daily fire weather index greater than 15 based upon the European Forest Fire Information System (EFFIS) classification. Number of days with very high fire danger Count Number of days per year with a fire weather index greater than 45 based upon the European Forest Fire Information System (EFFIS) classification. Seasonal fire weather index Dimensionless The mean fire weather index value over the European fire season (June-September). This is calculated as the sum of the daily fire weather index over the European fire season divided by the total number of days within this date range. The higher the index value, the more favorable the meteorological conditions to trigger a wildfire are. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Daily fire weather index Dimensionless The fire weather index values at a daily temporal resolution for the selected year. The higher the index value, the more favorable the meteorological conditions to trigger a wildfire are. Daily fire weather index Dimensionless The fire weather index values at a daily temporal resolution for the selected year. The higher the index value, the more favorable the meteorological conditions to trigger a wildfire are. Number of days with high fire danger Count Number of days per year with a fire weather index greater than 30 based upon the European Forest Fire Information System (EFFIS) classification. Number of days with high fire danger Count Number of days per year with a fire weather index greater than 30 based upon the European Forest Fire Information System (EFFIS) classification. Number of days with moderate fire danger Count The number of days per year with a daily fire weather index greater than 15 based upon the European Forest Fire Information System (EFFIS) classification. Number of days with moderate fire danger Count The number of days per year with a daily fire weather index greater than 15 based upon the European Forest Fire Information System (EFFIS) classification. Number of days with very high fire danger Count Number of days per year with a fire weather index greater than 45 based upon the European Forest Fire Information System (EFFIS) classification. Number of days with very high fire danger Count Number of days per year with a fire weather index greater than 45 based upon the European Forest Fire Information System (EFFIS) classification. Seasonal fire weather index Dimensionless The mean fire weather index value over the European fire season (June-September). This is calculated as the sum of the daily fire weather index over the European fire season divided by the total number of days within this date range. The higher the index value, the more favorable the meteorological conditions to trigger a wildfire are. Seasonal fire weather index Dimensionless The mean fire weather index value over the European fire season (June-September). This is calculated as the sum of the daily fire weather index over the European fire season divided by the total number of days within this date range. The higher the index value, the more favorable the meteorological conditions to trigger a wildfire are. 95 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/share-built-2018-raster-100-m-europe-3-yearly-aug-2020 https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/impervious-built-up-2018 Share of Built-up 2018 (raster 100 m), Europe, 3-yearly, Aug. 2020 The Share of Built-up (SBU) layer for the reference year 2018 represents share (percentage) of built-up (IBU) for the reference year 2018 in an aggregated version of 100m spatial resolution for the EEA38 countries and the United Kingdom. The production of the high resolution imperviousness layers is coordinated by the EEA in the frame of the EU Copernicus programme. The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. The 100 meter aggregate raster (fully conformant with the EEA reference grid) is provided as a full EEA38 and United Kingdom mosaic. 96 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-ocean-heat-content-anomaly-0-700m-time http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MEDSEA_OMI_OHC_area_averaged_anomalies Mediterranean Ocean Heat Content Anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing DEFINITION Ocean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 700 m depth: OHC=∫_(z_1)^(z_2)ρ_0 c_p (T_yr-T_clim )dz [1] with a reference density of = 1030 kgm-3 and a specific heat capacity of cp = 3980 J kg-1 °C-1 (e.g. von Schuckmann et al., 2009). Time series of annual mean values area averaged ocean heat content is provided for the Mediterranean Sea (30°N, 46°N; 6°W, 36°E) and is evaluated for topography deeper than 300m. CONTEXT Knowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the oceans shape our perspectives for the future. The quality evaluation of MEDSEA_OMI_OHC_area_averaged_anomalies is based on the “multi-product” approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean’s experience (Masina et al., 2017). Six global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are: The Mediterranean Sea Reanalysis at 1/24 degree horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020) Four global reanalyses at 1/4 degree horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031): GLORYS, C-GLORS, ORAS5, FOAM Two observation based products: CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) and ARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Details on the products are delivered in the PUM and QUID of this OMI. https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1 CMEMS KEY FINDINGS The ensemble mean ocean heat content anomaly time series over the Mediterranean Sea shows a continuous increase in the period 1993-2019 at rate of 1.4±0.3 W/m2 in the upper 700m. After 2005 the rate has clearly increased with respect the previous decade, in agreement with Iona et al. (2018). DOI (product):https://doi.org/10.48670/moi-00261 https://doi.org/10.48670/moi-00261 97 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/seasonal-monthly-single-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal-monthly-single-levels seasonal-monthly-single-levels This entry covers single-level data aggregated on a monthly time resolution. single-level data monthly time resolution Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Seasonal forecasts Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). multi-system seasonal forecast service The variables available in this data set are listed in the table below. The data includes forecasts created in real-time (since 2017) and retrospective forecasts (hindcasts) initialised at equivalent intervals during the period 1993-2016. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 1° x 1° Temporal coverage 1993 to 2016 (hindcasts); 2017 to present (forecasts) Temporal resolution Monthly File format GRIB Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 1° x 1° Horizontal resolution 1° x 1° Temporal coverage 1993 to 2016 (hindcasts); 2017 to present (forecasts) Temporal coverage 1993 to 2016 (hindcasts); 2017 to present (forecasts) Temporal resolution Monthly Temporal resolution Monthly File format GRIB File format GRIB Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. MAIN VARIABLES Name Units 10m u-component of wind m s-1 10m v-component of wind m s-1 10m wind gust since previous post-processing m s-1 10m wind speed m s-1 2m dewpoint temperature K 2m temperature K East-west surface stress rate of accumulation N m-2 Evaporation m of water s-1 Maximum 2m temperature in the last 24 hours K Mean sea level pressure Pa Mean sub-surface runoff rate m of water equivalent s-1 Mean surface runoff rate m of water equivalent s-1 Minimum 2m temperature in the last 24 hours K North-south surface stress rate of accumulation N m-2 Runoff m s-1 Sea surface temperature K Sea-ice cover (0 - 1) Snow density kg m-3 Snow depth m of water equivalent Snowfall m of water equivalent s-1 Soil temperature level 1 K Solar insolation rate of accumulation W m-2 Surface latent heat flux W m-2 Surface sensible heat flux W m-2 Surface solar radiation W m-2 Surface solar radiation downwards W m-2 Surface thermal radiation W m-2 Surface thermal radiation downwards W m-2 Top solar radiation W m-2 Top thermal radiation W m-2 Total cloud cover (0 - 1) Total column cloud ice water kg m-2 Total column cloud liquid water kg m-2 Total column water vapour kg m-2 Total precipitation m s-1 MAIN VARIABLES MAIN VARIABLES Name Units Name Units 10m u-component of wind m s-1 10m u-component of wind m s-1 10m v-component of wind m s-1 10m v-component of wind m s-1 10m wind gust since previous post-processing m s-1 10m wind gust since previous post-processing m s-1 10m wind speed m s-1 10m wind speed m s-1 2m dewpoint temperature K 2m dewpoint temperature K 2m temperature K 2m temperature K East-west surface stress rate of accumulation N m-2 East-west surface stress rate of accumulation N m-2 Evaporation m of water s-1 Evaporation m of water s-1 Maximum 2m temperature in the last 24 hours K Maximum 2m temperature in the last 24 hours K Mean sea level pressure Pa Mean sea level pressure Pa Mean sub-surface runoff rate m of water equivalent s-1 Mean sub-surface runoff rate m of water equivalent s-1 Mean surface runoff rate m of water equivalent s-1 Mean surface runoff rate m of water equivalent s-1 Minimum 2m temperature in the last 24 hours K Minimum 2m temperature in the last 24 hours K North-south surface stress rate of accumulation N m-2 North-south surface stress rate of accumulation N m-2 Runoff m s-1 Runoff m s-1 Sea surface temperature K Sea surface temperature K Sea-ice cover (0 - 1) Sea-ice cover (0 - 1) Snow density kg m-3 Snow density kg m-3 Snow depth m of water equivalent Snow depth m of water equivalent Snowfall m of water equivalent s-1 Snowfall m of water equivalent s-1 Soil temperature level 1 K Soil temperature level 1 K Solar insolation rate of accumulation W m-2 Solar insolation rate of accumulation W m-2 Surface latent heat flux W m-2 Surface latent heat flux W m-2 Surface sensible heat flux W m-2 Surface sensible heat flux W m-2 Surface solar radiation W m-2 Surface solar radiation W m-2 Surface solar radiation downwards W m-2 Surface solar radiation downwards W m-2 Surface thermal radiation W m-2 Surface thermal radiation W m-2 Surface thermal radiation downwards W m-2 Surface thermal radiation downwards W m-2 Top solar radiation W m-2 Top solar radiation W m-2 Top thermal radiation W m-2 Top thermal radiation W m-2 Total cloud cover (0 - 1) Total cloud cover (0 - 1) Total column cloud ice water kg m-2 Total column cloud ice water kg m-2 Total column cloud liquid water kg m-2 Total column cloud liquid water kg m-2 Total column water vapour kg m-2 Total column water vapour kg m-2 Total precipitation m s-1 Total precipitation m s-1 98 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/lake-surface-water-temperature-2002-2012-raster-1-km https://land.copernicus.eu/global/products/ Lake Surface Water Temperature 2002-2012 (raster 1 km), global, 10-daily - version 1 Lake surface water temperature (LSWT) describes the temperature of the lake surface, one important indicator of lake hydrology and biogeochemistry. Temperature trends observed over many years can be an indicator of how climate change affects the lake. LSWT is recognized internationally as an Essential Climate Variable (ECV) and complements the water quality information, in environmental monitoring of a large number of lakes globally. 99 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/lake-surface-water-temperature-2016-present-raster-1-km https://land.copernicus.eu/global/products/ Lake Surface Water Temperature 2016-present (raster 1 km), global, 10-daily - version 1 Lake surface water temperature (LSWT) describes the temperature of the lake surface, one important indicator of lake hydrology and biogeochemistry. Temperature trends observed over many years can be an indicator of how climate change affects the lake. LSWT is recognized internationally as an Essential Climate Variable (ECV) and complements the water quality information, in environmental monitoring of a large number of lakes globally. 100 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-ocean-colour https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-ocean-colour satellite-ocean-colour This dataset provides global daily estimates of ocean surface chlorophyll-a concentration and remote sensing reflectance derived from multiple satellite sensors. These two products are part of the broader discipline of ocean colour remote sensing, which analyses ocean surface radiances measured from space to derive information about the optical properties and constituents of the upper ocean. This information plays an essential role in our ability to monitor the health and productivity of marine ecosystems, assess the role of the oceans in the global carbon cycle, and quantify the impacts of climate change. Satellite remote sensing is the only method for regular monitoring of ocean biology on a global scale. Remote-sensing reflectance (or Rrs) is defined as the ratio of water-leaving radiance to downwelling irradiance and serves as the main input to algorithms used to derive other ocean colour products. Chlorophyll-a (Chl-a) is the main photosynthetic pigment found in phytoplankton, which form the base of the marine food-web and are responsible for approximately half of global photosynthesis. Chl-a can be estimated from Rrs data using different algorithms (see details in the Documentation). Here, we provide a blended Chl-a estimate from multiple algorithms, where blending is based on the suitability of each candidate algorithm to the optical typology of a given pixel. This approach provides the best estimates of global Chl-a across a range of water types. The files from this dataset contain global daily composites of merged sensor products: SeaWiFS, MERIS, MODIS Aqua, VIIRS, and (from version 5.0 onward) OLCI. Note that Rrs and Chl-a data are only available over cloud- and ice-free areas. As a result, more complete spatial coverage (as shown in the map in the upper-right corner) can be achieved by aggregating data over longer time periods. This dataset is produced using the processing chain software developed by the Ocean Colour component of the European Space Agency Climate Change Initiative project (ESA OC-CCI). Version 5.0 of the dataset is produced by the C3S service whereas previous versions are brokered from ESA OC-CCI. DATA DESCRIPTION Data type Grid Projection Regular latitude-longitude grid and sinusoidal grid Horizontal coverage Global Horizontal resolution Sinusoidal equal-area grid: 4km x 4km Regular latitude-longitude grid: 0.042° x 0.042° (4km x 4km at the Equator) Vertical coverage Surface Vertical resolution Single level Temporal coverage From September 1997 to present Temporal resolution Daily Temporal gaps No gaps File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions v6.0: New sensor added (S3B); MERIS 4th retrocession; bug fixes mainly for OLCI; updated SVC and masking; QAA updated to v6. v5.0.1: Correct anomaly in some v5.0 data. v5.0: Updated processing chain; use of OLCI data; SeaWIFS replaced with MERIS as reference sensor. v4.2: Data for Chl-a and Rrs; based on reprocessed satellite data and updated processing chain. Update frequency Quarterly with a 9-12 month latency behind real time DATA DESCRIPTION DATA DESCRIPTION Data type Grid Data type Grid Projection Regular latitude-longitude grid and sinusoidal grid Projection Regular latitude-longitude grid and sinusoidal grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution Sinusoidal equal-area grid: 4km x 4km Regular latitude-longitude grid: 0.042° x 0.042° (4km x 4km at the Equator) Horizontal resolution Sinusoidal equal-area grid: 4km x 4km Regular latitude-longitude grid: 0.042° x 0.042° (4km x 4km at the Equator) Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage From September 1997 to present Temporal coverage From September 1997 to present Temporal resolution Daily Temporal resolution Daily Temporal gaps No gaps Temporal gaps No gaps File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions v6.0: New sensor added (S3B); MERIS 4th retrocession; bug fixes mainly for OLCI; updated SVC and masking; QAA updated to v6. v5.0.1: Correct anomaly in some v5.0 data. v5.0: Updated processing chain; use of OLCI data; SeaWIFS replaced with MERIS as reference sensor. v4.2: Data for Chl-a and Rrs; based on reprocessed satellite data and updated processing chain. Versions v6.0: New sensor added (S3B); MERIS 4th retrocession; bug fixes mainly for OLCI; updated SVC and masking; QAA updated to v6. v5.0.1: Correct anomaly in some v5.0 data. v5.0: Updated processing chain; use of OLCI data; SeaWIFS replaced with MERIS as reference sensor. v4.2: Data for Chl-a and Rrs; based on reprocessed satellite data and updated processing chain. Update frequency Quarterly with a 9-12 month latency behind real time Update frequency Quarterly with a 9-12 month latency behind real time MAIN VARIABLES Name Units Description Mass concentration of chlorophyll-a mg m-3 Mass chlorophyll-a per unit of volume of near-surface water. Remote sensing reflectance sr-1 (per steradian) Fraction of the downwelling solar irradiance reflected by the ocean surface at a given wavelength. It is the basic input used to derive chlorophyll-a concentrations (as well as other ocean colour products). Here, estimates of Rrs measured at six wavelengths (410, 443, 490, 510, 555, and 670 nm) are provided as separate variables in the NetCDF files. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Mass concentration of chlorophyll-a mg m-3 Mass chlorophyll-a per unit of volume of near-surface water. Mass concentration of chlorophyll-a mg m-3 Mass chlorophyll-a per unit of volume of near-surface water. Remote sensing reflectance sr-1 (per steradian) Fraction of the downwelling solar irradiance reflected by the ocean surface at a given wavelength. It is the basic input used to derive chlorophyll-a concentrations (as well as other ocean colour products). Here, estimates of Rrs measured at six wavelengths (410, 443, 490, 510, 555, and 670 nm) are provided as separate variables in the NetCDF files. Remote sensing reflectance sr-1 (per steradian) Fraction of the downwelling solar irradiance reflected by the ocean surface at a given wavelength. It is the basic input used to derive chlorophyll-a concentrations (as well as other ocean colour products). Here, estimates of Rrs measured at six wavelengths (410, 443, 490, 510, 555, and 670 nm) are provided as separate variables in the NetCDF files. RELATED VARIABLES The files include several ancillary variables such as observation count per grid cell, water classes, bias and root-mean-square difference with respect to in situ observations, providing uncertainty information about estimates of chlorophyll-a and remote sensing reflectance. RELATED VARIABLES RELATED VARIABLES The files include several ancillary variables such as observation count per grid cell, water classes, bias and root-mean-square difference with respect to in situ observations, providing uncertainty information about estimates of chlorophyll-a and remote sensing reflectance. The files include several ancillary variables such as observation count per grid cell, water classes, bias and root-mean-square difference with respect to in situ observations, providing uncertainty information about estimates of chlorophyll-a and remote sensing reflectance. 101 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/insitu-gridded-observations-global-and-regional https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-global-and-regional insitu-gridded-observations-global-and-regional This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Depends on the model: global, quasi-global, Africa and contiguous United States (CONUS). Precise limits are given in the documentattion. Vertical coverage Surface Vertical resolution Single level Temporal coverage Depends on the model Temporal resolution Monthly and daily File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions Depends on the model and follows the data provider versioning policy Update frequency Monthly DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Depends on the model: global, quasi-global, Africa and contiguous United States (CONUS). Precise limits are given in the documentattion. Horizontal coverage Depends on the model: global, quasi-global, Africa and contiguous United States (CONUS). Precise limits are given in the documentattion. Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage Depends on the model Temporal coverage Depends on the model Temporal resolution Monthly and daily Temporal resolution Monthly and daily File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions Depends on the model and follows the data provider versioning policy Versions Depends on the model and follows the data provider versioning policy Update frequency Monthly Update frequency Monthly MAIN VARIABLES Name Units Description Precipitation Depends on the model: mm day-1 or mm month-1 Time mean flux of rain, snow and hail measured as the height of the equivalent liquid water in a square meter per time interval. Temperature °C Temperature of air at a height of 2 metres above the Earth’s surface. Temperature anomaly °C Anomaly with respect to the 1950-1980 climatology of air temperature at a height of 2 metres above the Earth’s surface. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Precipitation Depends on the model: mm day-1 or mm month-1 Time mean flux of rain, snow and hail measured as the height of the equivalent liquid water in a square meter per time interval. Precipitation Depends on the model: mm day-1 or mm month-1 Time mean flux of rain, snow and hail measured as the height of the equivalent liquid water in a square meter per time interval. Temperature °C Temperature of air at a height of 2 metres above the Earth’s surface. Temperature °C Temperature of air at a height of 2 metres above the Earth’s surface. Temperature anomaly °C Anomaly with respect to the 1950-1980 climatology of air temperature at a height of 2 metres above the Earth’s surface. Temperature anomaly °C Anomaly with respect to the 1950-1980 climatology of air temperature at a height of 2 metres above the Earth’s surface. 102 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-offshore-windfarm-indicators https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-offshore-windfarm-indicators sis-offshore-windfarm-indicators The dataset presents operational indicators relating to offshore wind farm operations and maintenance and are based upon turbine availability (energy and time-based) and energy production (available and generated). These indicators were chosen because they are typical key performance indicators in the industry. They are commonly used by offshore wind farm developers and operators to measure the performance of wind farm maintenance systems, as well as revenue calculation and estimating running costs. To compute these indicators, the JBA Consulting’s metocean risk management software, ForeCoast Marine, is used together with input metocean data generated from a member of the EURO-CORDEX climate model ensemble - the HIRHAM5 regional climate model downscaled from the global climate model EC-EARTH. The software steps through the metocean input, and for each time step calculates the statistical probability of weather windows that influence the operational capacity of the wind farm, including downtime and power production. In order to assess the impact of climate change, three different climate scenarios are used: the current climate (also termed historical), and two Representative Concentration Pathway (RCP) scenarios that correspond to an optimistic emission scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century often called the business-as-usual scenario (RCP8.5). This dataset is created as a demonstrator project to show the capability of combining climate projections with operational tools to plan for future climate scenarios. Given that the projections of these climate scenarios are based on a single combination of the regional and global climate models, users of these data should take in consideration that there is an inevitable underestimation of the uncertainty associated with this dataset. Whilst this is less than ideal, it does provide for the purpose of this demonstrator, an indication of the likely effects of climate change on offshore wind farm operations. In addition, the parameters selected to represent the wind farm assests are based on a single wind farm configuration, modelled with metocean data that is specific to location, time period and climate scenario. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Point data Horizontal coverage Seven discrete locations in north west European seas Horizontal resolution Locations are typically 200-300 km from their nearest neighbour Vertical coverage Surface Vertical resolution Single level Temporal coverage Historical: from 1977 to 2003 RCP4.5 mid-century: from 2041 to 2070 RCP4.5 end-century: from 2071 to 2100 RCP8.5 mid-century: from 2041 to 2070 RCP8.5 end-century: from 2071 to 2100 Temporal resolution Monthly, annually, campaign (5-year) File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Point data Data type Point data Horizontal coverage Seven discrete locations in north west European seas Horizontal coverage Seven discrete locations in north west European seas Horizontal resolution Locations are typically 200-300 km from their nearest neighbour Horizontal resolution Locations are typically 200-300 km from their nearest neighbour Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage Historical: from 1977 to 2003 RCP4.5 mid-century: from 2041 to 2070 RCP4.5 end-century: from 2071 to 2100 RCP8.5 mid-century: from 2041 to 2070 RCP8.5 end-century: from 2071 to 2100 Temporal coverage Historical: from 1977 to 2003 RCP4.5 mid-century: from 2041 to 2070 RCP4.5 end-century: from 2071 to 2100 RCP8.5 mid-century: from 2041 to 2070 RCP8.5 end-century: from 2071 to 2100 Historical: from 1977 to 2003 RCP4.5 mid-century: from 2041 to 2070 RCP4.5 end-century: from 2071 to 2100 RCP8.5 mid-century: from 2041 to 2070 RCP8.5 end-century: from 2071 to 2100 Temporal resolution Monthly, annually, campaign (5-year) Temporal resolution Monthly, annually, campaign (5-year) File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Energy-based availability % Calculated as the simulated energy output produced by online turbines divided by the theoretical maximum energy output that is produced if all turbines in the wind farm are available, averaged over the campaign period. Generated energy with downtime TWh Simulated values for the actual energy output generated by the wind farm, per annum. Generated energy without downtime TWh Simulated values for the theoretical maximum energy output generated by the wind farm if all the turbines are available, per annum. Jack-up barge duration days Simulated values for the total annual charter duration of jack-up barge(s). Time-based availability % Calculated as the simulated number of turbines online divided by the total number of turbines in the wind farm, averaged over the campaign period. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Energy-based availability % Calculated as the simulated energy output produced by online turbines divided by the theoretical maximum energy output that is produced if all turbines in the wind farm are available, averaged over the campaign period. Energy-based availability % Calculated as the simulated energy output produced by online turbines divided by the theoretical maximum energy output that is produced if all turbines in the wind farm are available, averaged over the campaign period. Generated energy with downtime TWh Simulated values for the actual energy output generated by the wind farm, per annum. Generated energy with downtime TWh Simulated values for the actual energy output generated by the wind farm, per annum. Generated energy without downtime TWh Simulated values for the theoretical maximum energy output generated by the wind farm if all the turbines are available, per annum. Generated energy without downtime TWh Simulated values for the theoretical maximum energy output generated by the wind farm if all the turbines are available, per annum. Jack-up barge duration days Simulated values for the total annual charter duration of jack-up barge(s). Jack-up barge duration days Simulated values for the total annual charter duration of jack-up barge(s). Time-based availability % Calculated as the simulated number of turbines online divided by the total number of turbines in the wind farm, averaged over the campaign period. Time-based availability % Calculated as the simulated number of turbines online divided by the total number of turbines in the wind farm, averaged over the campaign period. 103 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-significant-wave-height-extreme-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_VAR_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs Baltic Sea Significant Wave Height extreme from Observations Reprocessing DEFINITION The OMI_EXTREME_WAVE_BALTIC_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). CONTEXT Projections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell et al., 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). CMEMS KEY FINDINGS The mean 99th percentiles showed in the area are around 3 meters in the two stations included this year (Arkona and FinngrundetWR), and the standard deviation ranges from 0.23 to 0.26 m. Results for this year show a slight negative anomaly in both stations, with -0.315 meters in Arkona, and less than -0.1 meter in FinngrundetWR, both around the standard deviation range in the area. DOI (product):https://doi.org/10.48670/moi-00199 https://doi.org/10.48670/moi-00199 104 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-2006-raster-100-m-europe-6-yearly https://land.copernicus.eu/pan-european/corine-land-cover/clc-2006/view CORINE Land Cover 2006 (raster 100 m), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover 2006 (CLC2006) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2006. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 105 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-sea-surface-temperature-complete https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-surface-temperature-complete satellite-sea-surface-temperature-complete This dataset provides daily estimates of global sea surface temperature (SST) based on observations from multiple satellite sensors since September 1981. SST is known to be a significant driver of global weather and climate patterns and to play important roles in the exchanges of energy, momentum, moisture and gases between the ocean and atmosphere. As such, its knowledge is essential to understand and assess variability and long-term changes in the Earth’s climate. sea surface temperature The SST data provided here are based on measurements carried out by the following infrared sensors flown onboard multiple polar-orbiting satellites: the series of Advanced Very High Resolution Radiometers (AVHRRs), the series of Along Track Scanning Radiometers (ATSRs), and the Sea and Land Surface Temperature Radiometer (SLSTR). The dataset provides SST products of different processing levels. Only Level-3 Collated and Level-4 and served through this entry in the Catalogue. Due to the large number of files at Level-2 Pre-processed and Level-3 Collated these products are served through the Climate Data Store API. For more information on how to access these levels consult the documentation. The four types of products are: Level-2 Pre-processed (L2P): SST data on the native satellite swath grid and derived from single-sensor measurements. Level-3 Uncollated (L3U): SST product generated by regridding L2P data onto a global latitude-longitude grid. Level-3 Collated (L3C): global daily (day and night) single-sensor SST product based on collated L3U data. Level-4 (L4): spatially complete global SST product based on data from multiple sensors. Level-2 Pre-processed (L2P): SST data on the native satellite swath grid and derived from single-sensor measurements. Level-3 Uncollated (L3U): SST product generated by regridding L2P data onto a global latitude-longitude grid. Level-3 Collated (L3C): global daily (day and night) single-sensor SST product based on collated L3U data. Level-4 (L4): spatially complete global SST product based on data from multiple sensors. These products are available as Climate Data Records (CDRs), which have sufficient length, consistency, and continuity to be used to assess climate variability and changes. These SST CDRs are identical to those produced as part of the European Space Agency (ESA) SST Climate Change Initiative (CCI) project. Interim CDRs (ICDRs) are produced at levels L3C and L4 within C3S to extend the baseline CDRs. Both SST CDRs and ICDRs are generated using software and algorithms developed as part of the ESA SST CCI. Users should use the most recent version of the dataset whenever possible. Data from the previous version are also made available but cover shorter periods. DATA DESCRIPTION Data type Gridded Horizontal coverage Global for Level 3 and Level 4 products Satellite swath for Level 2P products Horizontal resolution 1km x 1km for ATSR and SLSTR Level 2P products ~4km x 4km for AVHRR Level 2P products 0.05° x 0.05° for Level 3 and Level 4 products Temporal coverage 1992 to 2012 for ATSR products 2017 to present for SLSTR products 1981 to present for AVHRR and L4 Analysis products Temporal resolution Daily File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.5, Unidata Observation Dataset v1.0 Update frequency Monthly with a 12 month delay DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global for Level 3 and Level 4 products Satellite swath for Level 2P products Horizontal coverage Global for Level 3 and Level 4 products Satellite swath for Level 2P products Global for Level 3 and Level 4 products Satellite swath for Level 2P products Horizontal resolution 1km x 1km for ATSR and SLSTR Level 2P products ~4km x 4km for AVHRR Level 2P products 0.05° x 0.05° for Level 3 and Level 4 products Horizontal resolution 1km x 1km for ATSR and SLSTR Level 2P products ~4km x 4km for AVHRR Level 2P products 0.05° x 0.05° for Level 3 and Level 4 products 1km x 1km for ATSR and SLSTR Level 2P products ~4km x 4km for AVHRR Level 2P products 0.05° x 0.05° for Level 3 and Level 4 products Temporal coverage 1992 to 2012 for ATSR products 2017 to present for SLSTR products 1981 to present for AVHRR and L4 Analysis products Temporal coverage 1992 to 2012 for ATSR products 2017 to present for SLSTR products 1981 to present for AVHRR and L4 Analysis products 1992 to 2012 for ATSR products 2017 to present for SLSTR products 1981 to present for AVHRR and L4 Analysis products Temporal resolution Daily Temporal resolution Daily File format NetCDF 4 File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.5, Unidata Observation Dataset v1.0 Conventions Climate and Forecast (CF) Metadata Convention v1.5, Unidata Observation Dataset v1.0 Update frequency Monthly with a 12 month delay Update frequency Monthly with a 12 month delay MAIN VARIABLES Name Units Description Analysed sea surface temperature K Global and spatially complete estimate of daily average ocean temperature adjusted to a standard depth of 20 cm. This variable is only available in the Level 4 product. Sea surface temperature at depth K Ocean temperature adjusted to a standard depth of 20 cm and 10:30 local time (equivalent to daily average). This temperature is derived from the skin SST measurements and allows comparison with in situ observations. This variable is only available in Level 2 and Level 3 products. Skin sea surface temperature K Ocean temperature at a depth of approximately 10 μm. This is the temperature measured by satellite infrared radiometers. This variable is only available in Level 2 and Level 3 products. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Analysed sea surface temperature K Global and spatially complete estimate of daily average ocean temperature adjusted to a standard depth of 20 cm. This variable is only available in the Level 4 product. Analysed sea surface temperature K Global and spatially complete estimate of daily average ocean temperature adjusted to a standard depth of 20 cm. This variable is only available in the Level 4 product. Sea surface temperature at depth K Ocean temperature adjusted to a standard depth of 20 cm and 10:30 local time (equivalent to daily average). This temperature is derived from the skin SST measurements and allows comparison with in situ observations. This variable is only available in Level 2 and Level 3 products. Sea surface temperature at depth K Ocean temperature adjusted to a standard depth of 20 cm and 10:30 local time (equivalent to daily average). This temperature is derived from the skin SST measurements and allows comparison with in situ observations. This variable is only available in Level 2 and Level 3 products. Skin sea surface temperature K Ocean temperature at a depth of approximately 10 μm. This is the temperature measured by satellite infrared radiometers. This variable is only available in Level 2 and Level 3 products. Skin sea surface temperature K Ocean temperature at a depth of approximately 10 μm. This is the temperature measured by satellite infrared radiometers. This variable is only available in Level 2 and Level 3 products. RELATED VARIABLES A number of variables accounting for the uncertainty on the data provided are also included in the files along the main variables. They provide estimates on possible variations of the values of the main variables due to changes in processing and sampling algorithms. A variable containing information on whether there is ocean, land or sea ice in each grid cell is also provided. For more information on the contents of the downloaded files, please refer to the documentation. RELATED VARIABLES RELATED VARIABLES A number of variables accounting for the uncertainty on the data provided are also included in the files along the main variables. They provide estimates on possible variations of the values of the main variables due to changes in processing and sampling algorithms. A variable containing information on whether there is ocean, land or sea ice in each grid cell is also provided. For more information on the contents of the downloaded files, please refer to the documentation. A number of variables accounting for the uncertainty on the data provided are also included in the files along the main variables. They provide estimates on possible variations of the values of the main variables due to changes in processing and sampling algorithms. A variable containing information on whether there is ocean, land or sea ice in each grid cell is also provided. For more information on the contents of the downloaded files, please refer to the documentation. 106 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-change-2012-2018-vector-europe-6-yearly https://land.copernicus.eu/pan-european/corine-land-cover/lcc-2012-2018 CORINE Land Cover Change 2012-2018 (vector), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover Change 2012-2018 (CHA1218) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to changes in land cover / land use status between the years 2012 and 2018. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (CHA), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 107 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-change-2006-2012-raster-100-m-europe-6 https://land.copernicus.eu/pan-european/corine-land-cover/lcc-2006-2012/view CORINE Land Cover Change 2006-2012 (raster 100 m), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover Change 2006-2012 (CHA0612) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to changes in land cover / land use status between the years 2006 and 2012. CHA is derived from satellite imagery by direct mapping of changes taken place between two consecutive inventories, based on image-to-image comparison. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (CHA), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/ https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 108 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-energy-derived-reanalysis https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-energy-derived-reanalysis sis-energy-derived-reanalysis The Copernicus climate change service (C3S) operational energy dataset provides climate and energy indicators for the European energy sector. The climate-relevant indicators for the energy sector considered are: air temperature, precipitation, incoming solar radiation, wind speed at 10 m and 100 m, and mean sea level air pressure. The energy indicators are electricity demand and power generation from various sources: wind (both onshore and offshore), solar and hydro (run-of-river and reservoir) power. Depending on the indicator, the data are available at the national, regional and grid (approximately 30x30 km) level for most European countries. The spatial aggregation of data over land uses the Eurostat NUTS0 & NUTS2 (Nomenclature des unités territoriales statistiques, 2016) regions. The offshore variables (e.g. offshore wind power) use the European maritime region definitions MAR0 and MAR1. Further information on the NUTS and MAR regions can be found in the documentation. The C3S Energy operational service is composed of three main streams: historical (1979-present), seasonal forecasts and projections (typically covering the period 1970-2100). This historical dataset (1979-present) produces reference climate variables based on the ERA5 reanalysis. Energy variables are generated by transforming the climate variables using a combination of statistical models and physically based data. A comprehensive set of measured energy supply and demand data has been collected from various sources such as the European Network of Transmission System Operators (ENTSO-E). These data provide a crucial reference to assess the robustness of the models used to convert climate into electric energy variables. Data is provided for the European domain, in a multi-variable, multi-timescale view of the climate and energy systems. This is beneficial in anticipating important climate-driven changes in the energy sector, through either long-term planning or medium-term operational activities. This is also used to investigate the role of temperature on electricity demand across Europe, as well as its interaction with the variability of renewable energy generation. DATA DESCRIPTION Data type Gridded and aggregated over shapes Horizontal coverage European (27N - 72N; 22W – 45E) Horizontal resolution 0.25° x 0.25° Vertical coverage 0 to 100 metres (indicator dependant) Vertical resolution Single level Temporal coverage January 1979 to present Temporal resolution 1 hour, 3 hours, 6 hours, daily, monthly, 3 monthly and yearly File format NetCDF or CSV (spatial aggregation dependant) Versions Only one version of the data is available Update frequency Monthly DATA DESCRIPTION DATA DESCRIPTION Data type Gridded and aggregated over shapes Data type Gridded and aggregated over shapes Horizontal coverage European (27N - 72N; 22W – 45E) Horizontal coverage European (27N - 72N; 22W – 45E) Horizontal resolution 0.25° x 0.25° Horizontal resolution 0.25° x 0.25° Vertical coverage 0 to 100 metres (indicator dependant) Vertical coverage 0 to 100 metres (indicator dependant) Vertical resolution Single level Vertical resolution Single level Temporal coverage January 1979 to present Temporal coverage January 1979 to present Temporal resolution 1 hour, 3 hours, 6 hours, daily, monthly, 3 monthly and yearly Temporal resolution 1 hour, 3 hours, 6 hours, daily, monthly, 3 monthly and yearly File format NetCDF or CSV (spatial aggregation dependant) File format NetCDF or CSV (spatial aggregation dependant) Versions Only one version of the data is available Versions Only one version of the data is available Update frequency Monthly Update frequency Monthly MAIN VARIABLES Name Units Description 2m air temperature K The ambient air temperature near to the surface, typically at height of 2m. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Electricity demand MWh, MW (or multiple there of, e.g. GW and GWh) Electricity demand (EDM) is the consumption of electricity expressed in energy units (MWh or GWh) or as mean power (MW or GW). The data is provided at the country level (NUTS level 0). Hydro power generation reservoirs Dimensionless, MWh or MW Hydro power generation from reservoirs (HRE) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided at the country level (NUTS level 0) for countries where HRE production exists. Hydro power generation rivers Dimensionless, MWh or MW Hydro power generation from run-of-river (HRO) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided only at the country level (NUTS level 0) for countries where HRO production exists. Pressure at sea level hPa Expected value of the air-pressure at the virtual vertical level defined by the average level of the sea. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Solar photovoltaic power generation Dimensionless, MWh or MW Solar photovoltaic power generation (SPV) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Surface downwelling shortwave radiation W m-2 The amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Values are derived from ERA5 surface downwelling shortwave radiation and bias corrected using Climate Research Unit (CRU) cloud cover and effects of inter-annual changes in atmospheric aerosol loading. Total precipitation m Depth of rain water accumulated on a flat, horizontal and impermeable surface per unit area during a given time period. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are accumulated measures. Wind power generation offshore Dimensionless, MWh or MW Offshore wind power generation (WOF) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point and maritime regions (MAR level 0 and MAR level 1). The data values are instantaneous measures. Wind power generation onshore Dimensionless, MWh or MW Onshore wind power generation (WON) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity at heights of 10 metres and 100 metres. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2) and maritime regions (MAR level 0 and MAR level 1). The data values are instantaneous measures. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 2m air temperature K The ambient air temperature near to the surface, typically at height of 2m. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. 2m air temperature K The ambient air temperature near to the surface, typically at height of 2m. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Electricity demand MWh, MW (or multiple there of, e.g. GW and GWh) Electricity demand (EDM) is the consumption of electricity expressed in energy units (MWh or GWh) or as mean power (MW or GW). The data is provided at the country level (NUTS level 0). Electricity demand MWh, MW (or multiple there of, e.g. GW and GWh) Electricity demand (EDM) is the consumption of electricity expressed in energy units (MWh or GWh) or as mean power (MW or GW). The data is provided at the country level (NUTS level 0). Hydro power generation reservoirs Dimensionless, MWh or MW Hydro power generation from reservoirs (HRE) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided at the country level (NUTS level 0) for countries where HRE production exists. Hydro power generation reservoirs Dimensionless, MWh or MW Hydro power generation from reservoirs (HRE) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided at the country level (NUTS level 0) for countries where HRE production exists. Hydro power generation rivers Dimensionless, MWh or MW Hydro power generation from run-of-river (HRO) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided only at the country level (NUTS level 0) for countries where HRO production exists. Hydro power generation rivers Dimensionless, MWh or MW Hydro power generation from run-of-river (HRO) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided only at the country level (NUTS level 0) for countries where HRO production exists. Pressure at sea level hPa Expected value of the air-pressure at the virtual vertical level defined by the average level of the sea. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Pressure at sea level hPa Expected value of the air-pressure at the virtual vertical level defined by the average level of the sea. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Solar photovoltaic power generation Dimensionless, MWh or MW Solar photovoltaic power generation (SPV) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Solar photovoltaic power generation Dimensionless, MWh or MW Solar photovoltaic power generation (SPV) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Surface downwelling shortwave radiation W m-2 The amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Values are derived from ERA5 surface downwelling shortwave radiation and bias corrected using Climate Research Unit (CRU) cloud cover and effects of inter-annual changes in atmospheric aerosol loading. Surface downwelling shortwave radiation W m-2 The amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Values are derived from ERA5 surface downwelling shortwave radiation and bias corrected using Climate Research Unit (CRU) cloud cover and effects of inter-annual changes in atmospheric aerosol loading. Total precipitation m Depth of rain water accumulated on a flat, horizontal and impermeable surface per unit area during a given time period. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are accumulated measures. Total precipitation m Depth of rain water accumulated on a flat, horizontal and impermeable surface per unit area during a given time period. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are accumulated measures. Wind power generation offshore Dimensionless, MWh or MW Offshore wind power generation (WOF) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point and maritime regions (MAR level 0 and MAR level 1). The data values are instantaneous measures. Wind power generation offshore Dimensionless, MWh or MW Offshore wind power generation (WOF) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point and maritime regions (MAR level 0 and MAR level 1). The data values are instantaneous measures. Wind power generation onshore Dimensionless, MWh or MW Onshore wind power generation (WON) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Wind power generation onshore Dimensionless, MWh or MW Onshore wind power generation (WON) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity at heights of 10 metres and 100 metres. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2) and maritime regions (MAR level 0 and MAR level 1). The data values are instantaneous measures. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity at heights of 10 metres and 100 metres. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2) and maritime regions (MAR level 0 and MAR level 1). The data values are instantaneous measures. 109 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-change-2015-2018-raster-100-m-europe-3 https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/change-maps/2015-2018/imperviousness-change-2015-2018 Imperviousness Change 2015-2018 (raster 100 m), Europe, 3-yearly, Aug. 2020 The High Resolution Layer Imperviousness Change (IMC) 2015-2018 is a raster dataset showing change in imperviousness between 2015 and 2018 reference years, produced in the frame of the EU Copernicus programme. This metadata refers to the derived product 100 meter aggregated raster (fully conformant with EEA reference grid) provided as a full mosaic of the EEA38 countries and the United Kingdom. The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 110 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-energy-derived-reanalysis https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-energy-derived-reanalysis app-energy-derived-reanalysis This application provides an accessible and easy to use interface to access climate and energy indicators for the European energy sector. Data is provided for the European domain, in a multi-variable, multi-timescale view of the climate and energy systems. This is beneficial in anticipating important climate-driven changes in the energy sector for both long-term planning and medium-term operational activities. The application can also be used to investigate the role of temperature on electricity demand across Europe, as well as its affect on renewable energy generation. This dataset is structured into three distinct streams: historical (currently the only available stream), seasonal forecasts and projections. The historical stream is based on ERA5 reanalysis data as input, from 1979 to near real-time (up to the most recent month). The seasonal stream uses ERA5, Météo-France and Met Office data as input, from 1996, to 6 months ahead forecasts. Except for ERA5, these data are first processed by means of bias adjustments – quantile mapping for seasonal forecasts, and cumulative distribution function transform for projections. The application displays only historical data in this initial version, with the other streams being added in future updates. User-selectable parameters User-selectable parameters Climate and energy indicators: users may select a variable to display on the main map or two variables to compare in the time series plot. Period: The only period available for the moment is the historical period. Statistic: The main map can display either actual values for the climate and energy indicators, or the anomalies relative to the historical period mean (1979-2019). Temporal aggregation: The indicators may be displayed in both the the main map and the time series plot at annual, seasonal, monthly, or daily time steps. Description of the graphical output Description of the graphical output The application consists of an interactive map centred on the European energy domain. The map presents the climate and energy indicators spatially averaged over NUTS0 and NUTS1 regions. The choice of user selectable parameters are available in dropdown menus on the left-hand side of the map. A date slider allows the user to explore the temporal range of the dataset at a time step selected in the temporal aggregation parameter. By clicking on a NUTS region, the user opens a time series plot below the map that can display one or two variables in multiple regions, which the user may select by clicking on the desired regions on the map. MAIN VARIABLES Name Units Description 2m air temperature °C The ambient air temperature near to the surface, typically at height of 2m. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Electricity demand TWh, kW (or multiple there of, e.g. GW and GWh) Electricity Demand is the consumption of electricity expressed in energy units (MWh or GWh) or as mean power (MW or GW). The data is provided at the country level (NUTS level 0). Hydro power generation reservoirs Dimensionless, TWh, kW (or multiple there of, e.g. GW and GWh) Hydro power generation from reservoirs (HRE) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided at the country level (NUTS level 0) for countries where HRE production exists. Hydro power generation rivers Dimensionless, TWh, kW (or multiple there of, e.g. GW and GWh) Hydro power generation from run-of-river (HRO) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided only at the country level (NUTS level 0) for countries where HRO production exists. Pressure at sea level hPa Expected value of the air-pressure at the virtual vertical level defined by the average level of the sea. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Solar photovoltaic power generation Dimensionless, TWh, kW (or multiple there of, e.g. GW and GWh) Solar photovoltaic power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Surface downwelling shortwave radiation W m-2 The amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Values are derived from ERA5 surface downwelling shortwave radiation and bias corrected using Climate Research Unit (CRU) cloud cover and effects of inter-annual changes in atmospheric aerosol loading. Total precipitation m Depth of rain water accumulated on a flat, horizontal and impermeable surface per unit area during a given time period. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are accumulated measures. Wind power generation onshore Dimensionless, TWh, kW (or multiple there of, e.g. GW and GWh) Onshore wind power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity at heights of 10 metres and 100 metres. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 2m air temperature °C The ambient air temperature near to the surface, typically at height of 2m. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. 2m air temperature °C The ambient air temperature near to the surface, typically at height of 2m. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Electricity demand TWh, kW (or multiple there of, e.g. GW and GWh) Electricity Demand is the consumption of electricity expressed in energy units (MWh or GWh) or as mean power (MW or GW). The data is provided at the country level (NUTS level 0). Electricity demand TWh, kW (or multiple there of, e.g. GW and GWh) Electricity Demand is the consumption of electricity expressed in energy units (MWh or GWh) or as mean power (MW or GW). The data is provided at the country level (NUTS level 0). Hydro power generation reservoirs Dimensionless, TWh, kW (or multiple there of, e.g. GW and GWh) Hydro power generation from reservoirs (HRE) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided at the country level (NUTS level 0) for countries where HRE production exists. Hydro power generation reservoirs Dimensionless, TWh, kW (or multiple there of, e.g. GW and GWh) Hydro power generation from reservoirs (HRE) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided at the country level (NUTS level 0) for countries where HRE production exists. Hydro power generation rivers Dimensionless, TWh, kW (or multiple there of, e.g. GW and GWh) Hydro power generation from run-of-river (HRO) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided only at the country level (NUTS level 0) for countries where HRO production exists. Hydro power generation rivers Dimensionless, TWh, kW (or multiple there of, e.g. GW and GWh) Hydro power generation from run-of-river (HRO) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided only at the country level (NUTS level 0) for countries where HRO production exists. Pressure at sea level hPa Expected value of the air-pressure at the virtual vertical level defined by the average level of the sea. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Pressure at sea level hPa Expected value of the air-pressure at the virtual vertical level defined by the average level of the sea. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Solar photovoltaic power generation Dimensionless, TWh, kW (or multiple there of, e.g. GW and GWh) Solar photovoltaic power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Solar photovoltaic power generation Dimensionless, TWh, kW (or multiple there of, e.g. GW and GWh) Solar photovoltaic power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Surface downwelling shortwave radiation W m-2 The amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Values are derived from ERA5 surface downwelling shortwave radiation and bias corrected using Climate Research Unit (CRU) cloud cover and effects of inter-annual changes in atmospheric aerosol loading. Surface downwelling shortwave radiation W m-2 The amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Values are derived from ERA5 surface downwelling shortwave radiation and bias corrected using Climate Research Unit (CRU) cloud cover and effects of inter-annual changes in atmospheric aerosol loading. Total precipitation m Depth of rain water accumulated on a flat, horizontal and impermeable surface per unit area during a given time period. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are accumulated measures. Total precipitation m Depth of rain water accumulated on a flat, horizontal and impermeable surface per unit area during a given time period. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are accumulated measures. Wind power generation onshore Dimensionless, TWh, kW (or multiple there of, e.g. GW and GWh) Onshore wind power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Wind power generation onshore Dimensionless, TWh, kW (or multiple there of, e.g. GW and GWh) Onshore wind power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity at heights of 10 metres and 100 metres. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity at heights of 10 metres and 100 metres. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. 111 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-europe-air-quality-reanalyses https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses cams-europe-air-quality-reanalyses This dataset provides annual air quality reanalyses for Europe based on both unvalidated (interim) and validated observations. CAMS produces annual air quality (interim) reanalyses for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global reanalyses. The production is currently based on an ensemble of nine air quality data assimilation systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models can be used to provide an estimate of the analysis uncertainty. The reanalysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. Additional sources of observations can complement the in-situ data assimilation, like satellite data. An interim reanalysis is provided each year for the year before based on the unvalidated near-real-time observation data stream that has not undergone full quality control by the data providers yet. Once the fully quality-controlled observations are available from the data provider, typically with an additional delay of about 1 year, a final validated annual reanalysis is provided. Both reanalyses are available at hourly time steps at height levels. interim validated More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Horizontal coverage Europe (east boundary=25.0° W, west=45.0° E, south=30.0° N, north=72.0°) Horizontal resolution 0.1°x0.1° (10 km x 10 km) Vertical coverage Surface, 50m, 100m, 250m, 500m, 750m, 1000m, 2000m, 3000m, 5000m Temporal coverage 2013 - 2022 Temporal resolution monthly files containing 1-hourly analyses File format NetCDF Update frequency twice a year DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Europe (east boundary=25.0° W, west=45.0° E, south=30.0° N, north=72.0°) Horizontal coverage Europe (east boundary=25.0° W, west=45.0° E, south=30.0° N, north=72.0°) Horizontal resolution 0.1°x0.1° (10 km x 10 km) Horizontal resolution 0.1°x0.1° (10 km x 10 km) Vertical coverage Surface, 50m, 100m, 250m, 500m, 750m, 1000m, 2000m, 3000m, 5000m Vertical coverage Surface, 50m, 100m, 250m, 500m, 750m, 1000m, 2000m, 3000m, 5000m Temporal coverage 2013 - 2022 Temporal coverage 2013 - 2022 Temporal resolution monthly files containing 1-hourly analyses Temporal resolution monthly files containing 1-hourly analyses File format NetCDF File format NetCDF Update frequency twice a year Update frequency twice a year MAIN VARIABLES Name Units Ammonia µg m-3 Carbon monoxide µg m-3 Nitrogen dioxide µg m-3 Nitrogen monoxide µg m-3 Non-methane volatile organic compounds (VOCs) µg m-3 Ozone µg m-3 PM10 dust fraction µg m-3 PM10, wildfires only µg m-3 PM2.5 secondary inorganic aerosol fraction µg m-3 Particulate matter d < 10 µm (PM10) µg m-3 Particulate matter d < 2.5 µm (PM2.5) µg m-3 Peroxyacyl nitrates µg m-3 Residential elementary carbon µg m-3 Sulphur dioxide µg m-3 Total elementary carbon µg m-3 MAIN VARIABLES MAIN VARIABLES Name Units Name Units Ammonia µg m-3 Ammonia µg m-3 Carbon monoxide µg m-3 Carbon monoxide µg m-3 Nitrogen dioxide µg m-3 Nitrogen dioxide µg m-3 Nitrogen monoxide µg m-3 Nitrogen monoxide µg m-3 Non-methane volatile organic compounds (VOCs) µg m-3 Non-methane volatile organic compounds (VOCs) µg m-3 Ozone µg m-3 Ozone µg m-3 PM10 dust fraction µg m-3 PM10 dust fraction µg m-3 PM10, wildfires only µg m-3 PM10, wildfires only µg m-3 PM2.5 secondary inorganic aerosol fraction µg m-3 PM2.5 secondary inorganic aerosol fraction µg m-3 Particulate matter d < 10 µm (PM10) µg m-3 Particulate matter d < 10 µm (PM10) µg m-3 Particulate matter d < 2.5 µm (PM2.5) µg m-3 Particulate matter d < 2.5 µm (PM2.5) µg m-3 Peroxyacyl nitrates µg m-3 Peroxyacyl nitrates µg m-3 Residential elementary carbon µg m-3 Residential elementary carbon µg m-3 Sulphur dioxide µg m-3 Sulphur dioxide µg m-3 Total elementary carbon µg m-3 Total elementary carbon µg m-3 112 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-ostia-sea-surface-temperature-and-sea-ice http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001 Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis Short description: For the Global Ocean- the OSTIA global foundation Sea Surface Temperature product provides daily gap-free maps of : Foundation Sea Surface Temperature at 0.05° x 0.05° horizontal grid resolution, using in-situ and satellite data from both infrared and microwave radiometers. The Operational Sea Surface Temperature and Ice Analysis (OSTIA) system is run by the UK's Met Office and delivered by IFREMER PU. OSTIA uses satellite data provided by the GHRSST project together with in-situ observations to determine the sea surface temperature. A high resolution (1/20° - approx. 6 km) daily analysis of sea surface temperature (SST) is produced for the global ocean and some lakes. DOI (product) :https://doi.org/10.48670/moi-00165 https://doi.org/10.48670/moi-00165 113 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-health-vector https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-health-vector sis-health-vector This dataset contains climatic suitability indicators for the Aedes albopictus (tiger mosquito) for Europe produced within the C3S European Health service. The provided indicators are the climatic suitability for the presence of Aedes albopictus and the season length of presence of Aedes albopictus. This mosquito transmits vector-borne diseases, such as dengue and chikungunya. Environmental factors such as temperature and rainfall impact the survival chance and seasonal activity of Aedes albopictus. In Europe, the environmental conditions become more favorable for the establishment of Aedes albopictus, which is a serious threat for human health in Europe. The temperature statistics are calculated, either for the season winter and summer or for the whole year, based on a bias-adjusted EURO-CORDEX dataset. Then, the statistics are averaged for 30 years as a smoothed average from 1971 to 2100. This results in a timeseries covering the period from 1986 to 2085. Finally, the timeseries are averaged for the model ensemble and the standard deviation to this ensemble mean is provided. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid. Horizontal coverage European region (approximately 27N – 72N, 22W – 45E) Horizontal resolution 0.1° x 0.1° Temporal coverage 1986 – 2085 Temporal resolution Season or year, that represents the 30-yr smoothed average around that particular season or year. File format NetCDF Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency No updates expected. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid. Projection Regular latitude-longitude grid. Horizontal coverage European region (approximately 27N – 72N, 22W – 45E) Horizontal coverage European region (approximately 27N – 72N, 22W – 45E) Horizontal resolution 0.1° x 0.1° Horizontal resolution 0.1° x 0.1° Temporal coverage 1986 – 2085 Temporal coverage 1986 – 2085 Temporal resolution Season or year, that represents the 30-yr smoothed average around that particular season or year. Temporal resolution Season or year, that represents the 30-yr smoothed average around that particular season or year. File format NetCDF File format NetCDF Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency No updates expected. Update frequency No updates expected. MAIN VARIABLES Name Units Description Season length Day Duration of Aedes albopictus presence in weeks. This is also known as the mosquito season. Outside of this period mosquitoes die off or go into diapause. Suitabilty Dimensionless Likelihood that the area has favourable environmental conditions for Aedes albopictus presence with 0 not suitable (no favourable conditions) and 100 totally suitable. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Season length Day Duration of Aedes albopictus presence in weeks. This is also known as the mosquito season. Outside of this period mosquitoes die off or go into diapause. Season length Day Duration of Aedes albopictus presence in weeks. This is also known as the mosquito season. Outside of this period mosquitoes die off or go into diapause. Suitabilty Dimensionless Likelihood that the area has favourable environmental conditions for Aedes albopictus presence with 0 not suitable (no favourable conditions) and 100 totally suitable. Suitabilty Dimensionless Likelihood that the area has favourable environmental conditions for Aedes albopictus presence with 0 not suitable (no favourable conditions) and 100 totally suitable. 114 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-land-cover https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover satellite-land-cover This dataset provides global maps describing the land surface into 22 classes, which have been defined using the United Nations Food and Agriculture Organization’s (UN FAO) Land Cover Classification System (LCCS). In addition to the land cover (LC) maps, four quality flags are produced to document the reliability of the classification and change detection. In order to ensure continuity, these land cover maps are consistent with the series of global annual LC maps from the 1990s to 2015 produced by the European Space Agency (ESA) Climate Change Initiative (CCI), which are also available on the ESA CCI LC viewer. To produce this dataset, the entire Medium Resolution Imaging Spectrometer (MERIS) Full and Reduced Resolution archive from 2003 to 2012 was first classified into a unique 10-year baseline LC map. This is then back- and up-dated using change detected from (i) Advanced Very-High-Resolution Radiometer (AVHRR) time series from 1992 to 1999, (ii) SPOT-Vegetation (SPOT-VGT) time series from 1998 to 2012 and (iii) PROBA-Vegetation (PROBA-V) and Sentinel-3 OLCI (S3 OLCI) time series from 2013. Beyond the climate-modelling communities, this dataset’s long-term consistency, yearly updates, and high thematic detail on a global scale have made it attractive for a multitude of applications such as land accounting, forest monitoring and desertification, in addition to scientific research. DATA DESCRIPTION Data type Gridded Projection Plate Carrée Horizontal coverage Global Horizontal resolution 300 m Vertical coverage Surface Vertical resolution Single level Temporal coverage 1992 to present with one year delay Temporal resolution Yearly File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 and ESA CCI Data Standards [DSWG 2015] Versions Version 2.0.7cds provides the LC maps for the years 1992 – 2015; version 2.1.1 for the years after 2016. Both versions are produced with the same processing chain. Update frequency Yearly DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Plate Carrée Projection Plate Carrée Horizontal coverage Global Horizontal coverage Global Horizontal resolution 300 m Horizontal resolution 300 m Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage 1992 to present with one year delay Temporal coverage 1992 to present with one year delay Temporal resolution Yearly Temporal resolution Yearly File format NetCDF4 File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 and ESA CCI Data Standards [DSWG 2015] Conventions Climate and Forecast (CF) Metadata Convention v1.6 and ESA CCI Data Standards [DSWG 2015] Versions Version 2.0.7cds provides the LC maps for the years 1992 – 2015; version 2.1.1 for the years after 2016. Both versions are produced with the same processing chain. Versions Version 2.0.7cds provides the LC maps for the years 1992 – 2015; version 2.1.1 for the years after 2016. Both versions are produced with the same processing chain. Update frequency Yearly Update frequency Yearly MAIN VARIABLES Name Units Description Change count Dimensionless Number of years where land cover class changes have occurred, since 1992. 0 for stable, greater than 0 for changes. Current pixel state Dimensionless Pixel identification from satellite surface reflectance observations, mainly distinguishing between land, water, and snow/ice. Six values are used: 1, 2, 3, 4, 5, 6; respectively meaning: clear land, clear water, clear snow ice, cloud, cloud shadow, filled. Land cover class Dimensionless Land cover class per pixel, according to a legend of 22 classes, defined using the Land Cover Classification System developed by the United Nations Food and Agriculture Organization. Distinct values are encoded as unsigned byte (0..255). The complete legend is available in the NetCDF files metadata and in the Product User Guide documentation. Observation count Dimensionless Number of valid satellite observations that have contributed to each pixel's classification Processed flag Dimensionless. Flag to mark areas that could not be classified. Two values are used: 0, 1; respectively meaning: not_processed, processed. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Change count Dimensionless Number of years where land cover class changes have occurred, since 1992. 0 for stable, greater than 0 for changes. Change count Dimensionless Number of years where land cover class changes have occurred, since 1992. 0 for stable, greater than 0 for changes. Current pixel state Dimensionless Pixel identification from satellite surface reflectance observations, mainly distinguishing between land, water, and snow/ice. Six values are used: 1, 2, 3, 4, 5, 6; respectively meaning: clear land, clear water, clear snow ice, cloud, cloud shadow, filled. Current pixel state Dimensionless Pixel identification from satellite surface reflectance observations, mainly distinguishing between land, water, and snow/ice. Six values are used: 1, 2, 3, 4, 5, 6; respectively meaning: clear land, clear water, clear snow ice, cloud, cloud shadow, filled. Land cover class Dimensionless Land cover class per pixel, according to a legend of 22 classes, defined using the Land Cover Classification System developed by the United Nations Food and Agriculture Organization. Distinct values are encoded as unsigned byte (0..255). The complete legend is available in the NetCDF files metadata and in the Product User Guide documentation. Land cover class Dimensionless Land cover class per pixel, according to a legend of 22 classes, defined using the Land Cover Classification System developed by the United Nations Food and Agriculture Organization. Distinct values are encoded as unsigned byte (0..255). The complete legend is available in the NetCDF files metadata and in the Product User Guide documentation. Observation count Dimensionless Number of valid satellite observations that have contributed to each pixel's classification Observation count Dimensionless Number of valid satellite observations that have contributed to each pixel's classification Processed flag Dimensionless. Flag to mark areas that could not be classified. Two values are used: 0, 1; respectively meaning: not_processed, processed. Processed flag Dimensionless. Flag to mark areas that could not be classified. Two values are used: 0, 1; respectively meaning: not_processed, processed. 115 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-uerra-europe-pressure-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-uerra-europe-pressure-levels reanalysis-uerra-europe-pressure-levels The present UERRA dataset contains analyses of atmospheric variables on pressure levels, from 1961 to present. It has been generated using the UERRA-HARMONIE system by combining model data with observations into a complete and consistent dataset using the laws of physics. This principle is called data assimilation. UERRA-HARMONIE employs a 3-dimensional variational data assimilation method. The assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The laws of physics allow for estimates at locations where data coverage is low. The provision of estimates at each grid point in Europe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with. The observing system has evolved drastically over time, and although the assimilation system can resolve data holes, the quality of analyses varies throughout the period, with less accurate estimates in 1960s due to sparse observational networks. In addition to observations in the model domain, a regional reanalysis needs model data which provide a first estimate of the atmospheric state. For the UERRA-HARMONIE system, this information is taken from the global reanalyses ERA40 (until the end of 1978) and ERA-interim (from 1979 onwards). The improvement of regional reanalyses over global products comes with the higher horizontal resolution that allows incorporating more regional details (e.g. topography). Moreover, it enables the system even to ingest more observations at places with dense observation networks. DATA DESCRIPTION Data type Gridded Projection Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Horizontal coverage Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Horizontal resolution 11km x 11km for the UERRA-HARMONIE system. Vertical coverage From 1000 hPa to 10 hPa. Vertical resolution 24 levels. Temporal coverage January 1961 to July 2019. Temporal resolution Analysis are available each day at 00, 06, 12 and 18 UTC. File format GRIB2 Update frequency No expected updates. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Projection Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Horizontal coverage Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Horizontal coverage Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Horizontal resolution 11km x 11km for the UERRA-HARMONIE system. Horizontal resolution 11km x 11km for the UERRA-HARMONIE system. Vertical coverage From 1000 hPa to 10 hPa. Vertical coverage From 1000 hPa to 10 hPa. Vertical resolution 24 levels. Vertical resolution 24 levels. Temporal coverage January 1961 to July 2019. Temporal coverage January 1961 to July 2019. Temporal resolution Analysis are available each day at 00, 06, 12 and 18 UTC. Temporal resolution Analysis are available each day at 00, 06, 12 and 18 UTC. File format GRIB2 File format GRIB2 Update frequency No expected updates. Update frequency No expected updates. MAIN VARIABLES Name Units Description Geopotential m2 s-2 Potential energy relative to the sea level of the unit mass at given pressure level. Geopotential = Geopotential height / gravitational acceleration. Geopotential height gpm (geopotential height in meter) Altitude of the given pressure level relative to the sea level. Relative humidity % Relation between actual humidity and saturation humidity. Values are in the interval [0,100]. 0%means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. Temperature K Air temperature in model grid cell at the given pressure level. U-component of wind m s-1 Zonal component of the wind valid for a grid cell. By model convention westerly wind (blowing from the west to the east) are positive. V-component of wind m s-1 Meridional component of the wind valid for a grid cell. By model convention southerly wind (blowing from the south to the north) are positive. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Geopotential m2 s-2 Potential energy relative to the sea level of the unit mass at given pressure level. Geopotential = Geopotential height / gravitational acceleration. Geopotential m2 s-2 Potential energy relative to the sea level of the unit mass at given pressure level. Geopotential = Geopotential height / gravitational acceleration. Geopotential height gpm (geopotential height in meter) Altitude of the given pressure level relative to the sea level. Geopotential height gpm (geopotential height in meter) Altitude of the given pressure level relative to the sea level. Relative humidity % Relation between actual humidity and saturation humidity. Values are in the interval [0,100]. 0%means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. Relative humidity % Relation between actual humidity and saturation humidity. Values are in the interval [0,100]. 0%means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. Temperature K Air temperature in model grid cell at the given pressure level. Temperature K Air temperature in model grid cell at the given pressure level. U-component of wind m s-1 Zonal component of the wind valid for a grid cell. By model convention westerly wind (blowing from the west to the east) are positive. U-component of wind m s-1 Zonal component of the wind valid for a grid cell. By model convention westerly wind (blowing from the west to the east) are positive. V-component of wind m s-1 Meridional component of the wind valid for a grid cell. By model convention southerly wind (blowing from the south to the north) are positive. V-component of wind m s-1 Meridional component of the wind valid for a grid cell. By model convention southerly wind (blowing from the south to the north) are positive. RELATED VARIABLES In order to make data access more manageable, the UERRA dataset has been split into several records. Complementary records to the present one are: UERRA on height levels, UERRA on sigle levels and UERRA on soil levels. For the present pressure level dataset, forecast data are not accessible through this form. However, the complete dataset can be accessed through the CDS application programming interface (API). See documentation for further details. RELATED VARIABLES RELATED VARIABLES In order to make data access more manageable, the UERRA dataset has been split into several records. Complementary records to the present one are: UERRA on height levels, UERRA on sigle levels and UERRA on soil levels. For the present pressure level dataset, forecast data are not accessible through this form. However, the complete dataset can be accessed through the CDS application programming interface (API). See documentation for further details. In order to make data access more manageable, the UERRA dataset has been split into several records. Complementary records to the present one are: UERRA on height levels, UERRA on sigle levels and UERRA on soil levels. For the present pressure level dataset, forecast data are not accessible through this form. However, the complete dataset can be accessed through the CDS application programming interface (API). See documentation for further details. 116 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-density-2018-raster-100-m-europe-3-yearly https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/imperviousness-density-2018 Imperviousness Density 2018 (raster 100 m), Europe, 3-yearly, Aug. 2020 The High Resolution Layer on Imperviousness Density 2018 with 100 m resolution is a thematic product showing the sealing density in the range from 0-100% in an aggregated version (100m) for the period 2018 (including data from 2017-2019) for the EEA-38 area and the United Kingdom. The production of the high resolution imperviousness layers is coordinated by EEA in the frame of the EU Copernicus programme. The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. The dataset in 100 meter aggregate raster (fully conformant with the EEA reference grid) is provided as a full EEA38 and United Kingdom mosaic. 117 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-carra-single-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-carra-single-levels reanalysis-carra-single-levels The C3S Arctic Regional Reanalysis (CARRA) dataset contains 3-hourly analyses and hourly short term forecasts of atmospheric and surface meteorological variables (surface and near-surface temperature, surface and top of atmosphere fluxes, precipitation, cloud, humidity, wind, pressure, snow and sea variables) at 2.5 km resolution. Additionally, forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC are available. The dataset includes two domains. The West domain covers Greenland, the Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, the Greenland Sea, and parts of Svalbard. The East domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia. The dataset has been produced with the use of the HARMONIE-AROME state-of-the-art non-hydrostatic regional numerical weather prediction model. High resolution reanalysis for the Arctic region is particularly important because the climate change is more pronounced in the Arctic region than elsewhere in the Earth. This fact calls for a better description of this region providing additional details with respect to the global reanalyses (ERA5 for instance). The additional information is provided by the higher horizontal resolution, more local observations (from the Nordic countries and Greenland), better description of surface characteristics (high resolution satellite and physiographic data), high resolution non-hydrostatic dynamics and improved physical parameterisation of clouds and precipitation in particular. The inputs to CARRA reanalysis are the observations, the ERA5 global reanalysis as lateral boundary conditions and the physiographic datasets describing the surface characteristics of the model. The observation values and information about their quality are used together to constrain the reanalysis where observations are available and provide information for the data assimilation system in areas in where less observations are available. More details about the reanalysis dataset and the extensive input data are given in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Horizontal coverage West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia Horizontal resolution 2.5km x 2.5km Vertical coverage From below the surface to the top of the atmosphere Vertical resolution Most variables are provided at a specifc level which includes surface, soil, cloud level and top of the atmosphere. That level may vary among the variables. Some variables are column integrated. Temporal coverage From 1991 to present. There are some days for some variables, where data are not available. For more information on these missing data please see the known issues under the documentation tab. Temporal resolution 3-hourly analysis data. Hourly forecast data File format GRIB2 Update frequency Monthly. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Projection Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Horizontal coverage West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia Horizontal coverage West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia Horizontal resolution 2.5km x 2.5km Horizontal resolution 2.5km x 2.5km Vertical coverage From below the surface to the top of the atmosphere Vertical coverage From below the surface to the top of the atmosphere Vertical resolution Most variables are provided at a specifc level which includes surface, soil, cloud level and top of the atmosphere. That level may vary among the variables. Some variables are column integrated. Vertical resolution Most variables are provided at a specifc level which includes surface, soil, cloud level and top of the atmosphere. That level may vary among the variables. Some variables are column integrated. Temporal coverage From 1991 to present. There are some days for some variables, where data are not available. For more information on these missing data please see the known issues under the documentation tab. Temporal coverage From 1991 to present. There are some days for some variables, where data are not available. For more information on these missing data please see the known issues under the documentation tab. Temporal resolution 3-hourly analysis data. Hourly forecast data Temporal resolution 3-hourly analysis data. Hourly forecast data File format GRIB2 File format GRIB2 Update frequency Monthly. Update frequency Monthly. MAIN VARIABLES Name Units Description 10m eastward wind gust since previous post-processing m s-1 This variable is the maximum wind speed along the model grid's x-axis since the last post-processing step at the grid area. It is determined for a height of 10m above the surface. 10m northward wind gust since previous post-processing m s-1 This variable is the maximum wind speed along the model grid's y-axis since the last post-processing step at the grid area. It is determined for a height of 10m above the surface. 10m u-component of wind m s-1 The average u-component of the wind for a grid box at the height of 10m above the surface. The u-component is defined in terms of the local grid orientation, which differs from the geographic east-west direction. If the grid axes were aligned with geographic east directions, wind speeds towards east would be positive and wind speeds towards west would be negative. The 10m wind component is computed from the wind at the lowest model level, the surface roughness and the atmospheric stability. 10m v-component of wind m s-1 The average v-component of the wind for a grid box at the height of 10m above the surface. The v-component is defined in terms of the local grid orientation, which differs from the geographic north-south direction. If the grid axes were aligned with geographic north directions, wind speeds towards north would be positive and wind speeds towards south would be negative. The 10m wind component is computed from the wind at the lowest model level, the surface roughness and the atmospheric stability. 10m wind direction Degrees Average wind direction for a grid cell at the height of 10m above the surface. The wind direction is the direction from which the wind comes. Values are in the interval [0, 360]. A value of 0° means a northerly wind and 90° indicates an easterly wind. 10m wind gust since previous post-processing m s-1 This variable is the maximum wind speed since the last post-processing step at the grid area. It is determined for a height of 10m above the surface. 10m wind speed m s-1 Average wind speed for a grid column at the height of 10m above the surface. It is computed from the zonal (u) and the meridional (v) wind components by sqrt(u2 + v2). 2m relative humidity % Relation between actual humidity and saturation humidity. Values are in the interval [0,100]. 0% means that the air in the grid box is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. 2m specific humidity kg kg-1 This variable is the mass of water vapour per kilogram of moist air, at 2 meter above surface. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. 2m temperature K Average air temperature valid for a grid column at the height of 2m above the surface. The 2m temperature is an average of temperatures computed for the 4 surface types such as sea, inland water, natural land and urban. Each of these are computed from the temperatures at the surface, at the lowest model level, the surface roughness variables and the atmospheric stability. Albedo % The total reflectance of downward solar radiation at the surface. The albedo is an average of albedos computed for the 4 surface types: sea, inland water, natural land and urban. The natural land albedo is an average of albedos computed for soil, vegetation and snow-covered surfaces. Cloud base m The cloud base height is output in units of m above the surface. It is the lowest model level with more than 4/8 cloud cover. Cloud top m The cloud top height is output in units of m above the surface. It is the highest model level with more than 4/8 cloud cover. Direct solar radiation J m-2 This variable is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface on a plane perpendicular to the direction of the Sun, accumulated since the beginning of the forecast. Evaporation kg m-2 Accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration from vegetation, into vapour in the air above. Fog (lowest model level cloud) % Fog is the cloud cover at the lowest model level that has a thickness of approximately 20 m. Fraction of snow cover Dimensionless This variable represents the fraction (0-1) of the cell/grid-box occupied by snow. High cloud cover % Percentage of the grid column for which the sky is covered with clouds above 5 km height. Land-sea mask Dimensionless The fraction of land in each grid column. Land includes the surface types: inland water, natural land and urban. Values are in the interval (0-1). Low cloud cover % Percentage of the grid column for which the sky is covered with clouds below 2 km height. Maximum 2m temperature since previous post-processing K Maximum temperature at the height of 2 m above the surface. The maximum is for the preceding forecast period and is available in the long forecasts at 00 and 12 UTC. For forecast lengths up to 6 hours, this is for the preceding 1 hour interval. For longer forecast lengths up to 30 hours this is for the preceding 3 hour interval. Mean sea level pressure Pa Surface air pressure in the grid column reduced to mean sea level. Medium cloud cover % Percentage of the grid column for which the sky is covered with clouds between 2 km and 5 km height. Minimum 2m temperature since previous post-processing K Minimum temperature at the height of 2 m above the surface. The minimum is for the preceding forecast period and is available in the long forecasts at 00 and 12 UTC. For forecast lengths up to 6 hours, this is for the preceding 1 hour interval. For longer forecast lengths up to 30 hours this is for the preceding 3 hour interval. Orography m The height above sea level of the land surface. This variable does not change with snow cover. Percolation kg m-2 The mass per unit area of water that drains below the deepest soil level in the model. This variable is calculated for the natural land and urban surface types, including soil, vegetation and snow. Precipitation type Dimensionless This variable describes the type of precipitation at the surface, at the specified time. CARRA can distinguish the following precipitation types (its value is in brackets): drizzle (1), rain (2), sleet (3), snow (4), freezing drizzle (5), freezing rain (6), graupel (7) and hail (8). Sea ice area fraction Dimensionless The fraction of sea ice in each grid column. Values are in the interval (0-1). Sea ice surface temperature K This variable is the temperature of sea ice near the surface. Sea ice thickness m The total thickness of sea ice without taking the snow layer on top of the ice into account. Sea surface temperature K The temperature at the sea surface. For coastal areas, this is the surface temperature for the sea fraction in each grid column. Skin temperature K Average air temperature at the surface of each grid column. The skin temperature is an average of temperatures given by the four surface types in the grid: sea, inland water, natural land and urban. Snow albedo Dimensionless This variable is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. Snow density kg m-3 The density of snow. Snow depth water equivalent kg m-2 The mass of liquid water obtained from melting the snow per unit area. This is equivalent to the depth of this liquid water in units of mm. Snow on ice total depth m This variable is the total depth of snow on top of sea ice. Please note that the routine that produces this variable is not developed to model snow on ice accurately. Rather this variable should be treated as a rough estimate in order to get reasonable estimations for the energy fluxes. Surface latent heat flux J m-2 The average latent heat energy per unit area of the surface in a grid column accumulated from the initial time of the forecast to the forecast time step. Condensation or freezing of water gives a positive latent heat flux, while evaporation or sublimation of water gives a negative latent heat flux. Surface net solar radiation J m-2 The average net solar energy per unit area of a horizontal surface in a grid column accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive towards the surface. It is calculated as the difference between the downward solar energy and the upward solar energy at the surface. Surface net solar radiation, clear sky J m-2 The average net solar energy per unit area of a horizontal surface in a grid column accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive towards the surface. It is calculated as the difference between the downward solar energy and the upward solar energy at the surface assuming clear-sky (cloudless) conditions. Surface net thermal radiation J m-2 The average net thermal energy per unit area of the surface in a grid column accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive towards the surface. It is calculated as the difference between the downward thermal energy and the upward thermal energy at the surface. Surface net thermal radiation, clear sky J m-2 The average net thermal energy per unit area of the surface in a grid column accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive towards the surface. It is calculated as the difference between the downward thermal energy and the upward thermal energy at the surface assuming clear-sky (cloudless) conditions. Surface pressure Pa Surface air pressure in the grid column. Surface roughness m The roughness length of the surface is the height above the surface at which the wind profile is assumed to become zero. It is an average over roughness lengths computed for the surface types: sea, inland water, natural land and urban area. The roughness length for natural land is an average of the roughness lengths for soil, vegetation and snow. Surface roughness length for heat m The surface roughness for heat is a measure of the surface resistance to heat transfer. This variable is used to determine the air to surface transfer of heat. Surface runoff kg m-2 The mass per unit area of water at the surface when saturation occurs. This variable is calculated for the natural land and urban surface types, including soil, vegetation and snow. Surface sensible heat flux J m-2 The average sensible heat energy per unit area of the surface in a grid column accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive towards the surface. Surface solar radiation downwards J m-2 The average solar downwards energy per unit area of a horizontal surface in a grid column accumulated from the initial time of the forecast to the forecast time step. Surface thermal radiation downwards J m-2 The average thermal downwards energy per unit area of the surface in a grid column accumulated from the initial time of the forecast to the forecast time step. Time integral of rain flux kg m-2 The average mass per unit area of water droplets falling on the surface in a grid column. It includes all kinds of liquid precipitation, that is convective precipitation and large scale precipitation. It is an accumulated variable from the initial time of the forecast to the forecast time step. Time integral of snow evaporation flux kg m-2 This variable is the amount of water that has evaporated from snow from the snow-covered area of a grid box, accumulated since the beginning of the forecast. Time integral of surface eastward momentum flux kg m s-1 This variable is the sum of all surface stress components in a direction along the model grid's x-axis. Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. Momentum flux components are associated to orographic gravity waves, the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. For instance, the turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. Positive (negative) values denote stress in the positive (negative) direction along the model grid's x-axis. Time integral of surface latent heat evaporation flux kg m-2 This variable is the transfer of latent heat (resulting from evaporation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface from liquid water to vapour represents a transfer of energy from the surface to the atmosphere. Time integral of surface latent heat sublimation flux J m-2 This variable is the transfer of latent heat (resulting from sublimation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Sublimation from the Earth's surface from solid water to vapour represents a transfer of energy from the surface to the atmosphere. Time integral of surface northward momentum flux kg m s-1 This variable is the sum of all surface stress components in a direction along the model grid's y-axis. Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. Momentum flux components are associated to orographic gravity waves, the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. For instance, the turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. Positive (negative) values denote stress in the positive (negative) direction along the model grid's y-axis. Time integral of total solid precipitation flux kg m-2 The average mass per unit area of snow and ice particles falling on the surface in a grid column. It includes all kinds of solid precipitation, that is convective precipitation and large scale precipitation. It is an accumulated variable from the initial time of the forecast to the forecast time step. Time-integrated surface direct short wave radiation flux J m-2 The average direct solar beam energy per unit area of a horizontal surface in a grid column accumulated from the initial time of the forecast to the forecast time step. The direct solar beam includes solar energy from the solar aureole. This variable is calculated as positive towards the surface. Top net solar radiation J m-2 The average net solar energy per unit area of a horizontal plane at the top of the atmosphere, accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive downwards. It is calculated as the difference between the downward solar energy and the upward solar energy at the top of the atmosphere. Top net thermal radiation J m-2 The average net thermal energy per unit area of a horizontal plane at the top of the atmosphere, accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive downwards. It is calculated as the difference between the downward thermal energy and the upward thermal energy at the top of the atmosphere. Total cloud cover % Percentage of the grid column for which the sky is covered with clouds. Clouds at any height above the surface are considered. Total column cloud ice water kg m-2 This variable is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this variable. Total column cloud liquid water kg m-2 This variable is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this variable. Total column graupel kg m-2 This variable is the amount of snow pellets (graupel) contained within a column extending from the surface of the Earth to the top of the atmosphere. Total column integrated water vapour kg m-2 Total amount of water vapour mass load per unit area from the surface to the top of the atmosphere for each grid column. Total precipitation kg m-2 The average mass per unit area of water droplets and ice particles falling on the surface in a grid column. It includes all kinds of precipitation, that is convective precipitation, large scale precipitation, and liquid and solid precipitation. It is an accumulated variable from the initial time of the forecast to the forecast time step. Visibility m The visibility is given with the unit m and is calculated from the cloud water, cloud ice, rain, snow and graupel present at the lowest model level. For cloud and precipitation free conditions mist is calculated from the lowest model level relative humidity and the concentration of cloud condensation nuclei. Volumetric soil ice m3 m-3 This variable is the water equivalent of volumetric soil ice content. It is the volume that the liquid water would have if the ice melted. Volumetric soil moisture m3 m-3 The volume concentration of liquid water at root depth in the soil. This level varies depending on the surface type and is also defined for surface types without vegetation. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 10m eastward wind gust since previous post-processing m s-1 This variable is the maximum wind speed along the model grid's x-axis since the last post-processing step at the grid area. It is determined for a height of 10m above the surface. 10m eastward wind gust since previous post-processing m s-1 This variable is the maximum wind speed along the model grid's x-axis since the last post-processing step at the grid area. It is determined for a height of 10m above the surface. 10m northward wind gust since previous post-processing m s-1 This variable is the maximum wind speed along the model grid's y-axis since the last post-processing step at the grid area. It is determined for a height of 10m above the surface. 10m northward wind gust since previous post-processing m s-1 This variable is the maximum wind speed along the model grid's y-axis since the last post-processing step at the grid area. It is determined for a height of 10m above the surface. 10m u-component of wind m s-1 The average u-component of the wind for a grid box at the height of 10m above the surface. The u-component is defined in terms of the local grid orientation, which differs from the geographic east-west direction. If the grid axes were aligned with geographic east directions, wind speeds towards east would be positive and wind speeds towards west would be negative. The 10m wind component is computed from the wind at the lowest model level, the surface roughness and the atmospheric stability. 10m u-component of wind m s-1 The average u-component of the wind for a grid box at the height of 10m above the surface. The u-component is defined in terms of the local grid orientation, which differs from the geographic east-west direction. If the grid axes were aligned with geographic east directions, wind speeds towards east would be positive and wind speeds towards west would be negative. The 10m wind component is computed from the wind at the lowest model level, the surface roughness and the atmospheric stability. 10m v-component of wind m s-1 The average v-component of the wind for a grid box at the height of 10m above the surface. The v-component is defined in terms of the local grid orientation, which differs from the geographic north-south direction. If the grid axes were aligned with geographic north directions, wind speeds towards north would be positive and wind speeds towards south would be negative. The 10m wind component is computed from the wind at the lowest model level, the surface roughness and the atmospheric stability. 10m v-component of wind m s-1 The average v-component of the wind for a grid box at the height of 10m above the surface. The v-component is defined in terms of the local grid orientation, which differs from the geographic north-south direction. If the grid axes were aligned with geographic north directions, wind speeds towards north would be positive and wind speeds towards south would be negative. The 10m wind component is computed from the wind at the lowest model level, the surface roughness and the atmospheric stability. 10m wind direction Degrees Average wind direction for a grid cell at the height of 10m above the surface. The wind direction is the direction from which the wind comes. Values are in the interval [0, 360]. A value of 0° means a northerly wind and 90° indicates an easterly wind. 10m wind direction Degrees Average wind direction for a grid cell at the height of 10m above the surface. The wind direction is the direction from which the wind comes. Values are in the interval [0, 360]. A value of 0° means a northerly wind and 90° indicates an easterly wind. 10m wind gust since previous post-processing m s-1 This variable is the maximum wind speed since the last post-processing step at the grid area. It is determined for a height of 10m above the surface. 10m wind gust since previous post-processing m s-1 This variable is the maximum wind speed since the last post-processing step at the grid area. It is determined for a height of 10m above the surface. 10m wind speed m s-1 Average wind speed for a grid column at the height of 10m above the surface. It is computed from the zonal (u) and the meridional (v) wind components by sqrt(u2 + v2). 10m wind speed m s-1 Average wind speed for a grid column at the height of 10m above the surface. It is computed from the zonal (u) and the meridional (v) wind components by sqrt(u2 + v2). 2m relative humidity % Relation between actual humidity and saturation humidity. Values are in the interval [0,100]. 0% means that the air in the grid box is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. 2m relative humidity % Relation between actual humidity and saturation humidity. Values are in the interval [0,100]. 0% means that the air in the grid box is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. 2m specific humidity kg kg-1 This variable is the mass of water vapour per kilogram of moist air, at 2 meter above surface. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. 2m specific humidity kg kg-1 This variable is the mass of water vapour per kilogram of moist air, at 2 meter above surface. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. 2m temperature K Average air temperature valid for a grid column at the height of 2m above the surface. The 2m temperature is an average of temperatures computed for the 4 surface types such as sea, inland water, natural land and urban. Each of these are computed from the temperatures at the surface, at the lowest model level, the surface roughness variables and the atmospheric stability. 2m temperature K Average air temperature valid for a grid column at the height of 2m above the surface. The 2m temperature is an average of temperatures computed for the 4 surface types such as sea, inland water, natural land and urban. Each of these are computed from the temperatures at the surface, at the lowest model level, the surface roughness variables and the atmospheric stability. Albedo % The total reflectance of downward solar radiation at the surface. The albedo is an average of albedos computed for the 4 surface types: sea, inland water, natural land and urban. The natural land albedo is an average of albedos computed for soil, vegetation and snow-covered surfaces. Albedo % The total reflectance of downward solar radiation at the surface. The albedo is an average of albedos computed for the 4 surface types: sea, inland water, natural land and urban. The natural land albedo is an average of albedos computed for soil, vegetation and snow-covered surfaces. Cloud base m The cloud base height is output in units of m above the surface. It is the lowest model level with more than 4/8 cloud cover. Cloud base m The cloud base height is output in units of m above the surface. It is the lowest model level with more than 4/8 cloud cover. Cloud top m The cloud top height is output in units of m above the surface. It is the highest model level with more than 4/8 cloud cover. Cloud top m The cloud top height is output in units of m above the surface. It is the highest model level with more than 4/8 cloud cover. Direct solar radiation J m-2 This variable is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface on a plane perpendicular to the direction of the Sun, accumulated since the beginning of the forecast. Direct solar radiation J m-2 This variable is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface on a plane perpendicular to the direction of the Sun, accumulated since the beginning of the forecast. Evaporation kg m-2 Accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration from vegetation, into vapour in the air above. Evaporation kg m-2 Accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration from vegetation, into vapour in the air above. Fog (lowest model level cloud) % Fog is the cloud cover at the lowest model level that has a thickness of approximately 20 m. Fog (lowest model level cloud) % Fog is the cloud cover at the lowest model level that has a thickness of approximately 20 m. Fraction of snow cover Dimensionless This variable represents the fraction (0-1) of the cell/grid-box occupied by snow. Fraction of snow cover Dimensionless This variable represents the fraction (0-1) of the cell/grid-box occupied by snow. High cloud cover % Percentage of the grid column for which the sky is covered with clouds above 5 km height. High cloud cover % Percentage of the grid column for which the sky is covered with clouds above 5 km height. Land-sea mask Dimensionless The fraction of land in each grid column. Land includes the surface types: inland water, natural land and urban. Values are in the interval (0-1). Land-sea mask Dimensionless The fraction of land in each grid column. Land includes the surface types: inland water, natural land and urban. Values are in the interval (0-1). Low cloud cover % Percentage of the grid column for which the sky is covered with clouds below 2 km height. Low cloud cover % Percentage of the grid column for which the sky is covered with clouds below 2 km height. Maximum 2m temperature since previous post-processing K Maximum temperature at the height of 2 m above the surface. The maximum is for the preceding forecast period and is available in the long forecasts at 00 and 12 UTC. For forecast lengths up to 6 hours, this is for the preceding 1 hour interval. For longer forecast lengths up to 30 hours this is for the preceding 3 hour interval. Maximum 2m temperature since previous post-processing K Maximum temperature at the height of 2 m above the surface. The maximum is for the preceding forecast period and is available in the long forecasts at 00 and 12 UTC. For forecast lengths up to 6 hours, this is for the preceding 1 hour interval. For longer forecast lengths up to 30 hours this is for the preceding 3 hour interval. Mean sea level pressure Pa Surface air pressure in the grid column reduced to mean sea level. Mean sea level pressure Pa Surface air pressure in the grid column reduced to mean sea level. Medium cloud cover % Percentage of the grid column for which the sky is covered with clouds between 2 km and 5 km height. Medium cloud cover % Percentage of the grid column for which the sky is covered with clouds between 2 km and 5 km height. Minimum 2m temperature since previous post-processing K Minimum temperature at the height of 2 m above the surface. The minimum is for the preceding forecast period and is available in the long forecasts at 00 and 12 UTC. For forecast lengths up to 6 hours, this is for the preceding 1 hour interval. For longer forecast lengths up to 30 hours this is for the preceding 3 hour interval. Minimum 2m temperature since previous post-processing K Minimum temperature at the height of 2 m above the surface. The minimum is for the preceding forecast period and is available in the long forecasts at 00 and 12 UTC. For forecast lengths up to 6 hours, this is for the preceding 1 hour interval. For longer forecast lengths up to 30 hours this is for the preceding 3 hour interval. Orography m The height above sea level of the land surface. This variable does not change with snow cover. Orography m The height above sea level of the land surface. This variable does not change with snow cover. Percolation kg m-2 The mass per unit area of water that drains below the deepest soil level in the model. This variable is calculated for the natural land and urban surface types, including soil, vegetation and snow. Percolation kg m-2 The mass per unit area of water that drains below the deepest soil level in the model. This variable is calculated for the natural land and urban surface types, including soil, vegetation and snow. Precipitation type Dimensionless This variable describes the type of precipitation at the surface, at the specified time. CARRA can distinguish the following precipitation types (its value is in brackets): drizzle (1), rain (2), sleet (3), snow (4), freezing drizzle (5), freezing rain (6), graupel (7) and hail (8). Precipitation type Dimensionless This variable describes the type of precipitation at the surface, at the specified time. CARRA can distinguish the following precipitation types (its value is in brackets): drizzle (1), rain (2), sleet (3), snow (4), freezing drizzle (5), freezing rain (6), graupel (7) and hail (8). Sea ice area fraction Dimensionless The fraction of sea ice in each grid column. Values are in the interval (0-1). Sea ice area fraction Dimensionless The fraction of sea ice in each grid column. Values are in the interval (0-1). Sea ice surface temperature K This variable is the temperature of sea ice near the surface. Sea ice surface temperature K This variable is the temperature of sea ice near the surface. Sea ice thickness m The total thickness of sea ice without taking the snow layer on top of the ice into account. Sea ice thickness m The total thickness of sea ice without taking the snow layer on top of the ice into account. Sea surface temperature K The temperature at the sea surface. For coastal areas, this is the surface temperature for the sea fraction in each grid column. Sea surface temperature K The temperature at the sea surface. For coastal areas, this is the surface temperature for the sea fraction in each grid column. Skin temperature K Average air temperature at the surface of each grid column. The skin temperature is an average of temperatures given by the four surface types in the grid: sea, inland water, natural land and urban. Skin temperature K Average air temperature at the surface of each grid column. The skin temperature is an average of temperatures given by the four surface types in the grid: sea, inland water, natural land and urban. Snow albedo Dimensionless This variable is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. Snow albedo Dimensionless This variable is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. Snow density kg m-3 The density of snow. Snow density kg m-3 The density of snow. Snow depth water equivalent kg m-2 The mass of liquid water obtained from melting the snow per unit area. This is equivalent to the depth of this liquid water in units of mm. Snow depth water equivalent kg m-2 The mass of liquid water obtained from melting the snow per unit area. This is equivalent to the depth of this liquid water in units of mm. Snow on ice total depth m This variable is the total depth of snow on top of sea ice. Please note that the routine that produces this variable is not developed to model snow on ice accurately. Rather this variable should be treated as a rough estimate in order to get reasonable estimations for the energy fluxes. Snow on ice total depth m This variable is the total depth of snow on top of sea ice. Please note that the routine that produces this variable is not developed to model snow on ice accurately. Rather this variable should be treated as a rough estimate in order to get reasonable estimations for the energy fluxes. Surface latent heat flux J m-2 The average latent heat energy per unit area of the surface in a grid column accumulated from the initial time of the forecast to the forecast time step. Condensation or freezing of water gives a positive latent heat flux, while evaporation or sublimation of water gives a negative latent heat flux. Surface latent heat flux J m-2 The average latent heat energy per unit area of the surface in a grid column accumulated from the initial time of the forecast to the forecast time step. Condensation or freezing of water gives a positive latent heat flux, while evaporation or sublimation of water gives a negative latent heat flux. Surface net solar radiation J m-2 The average net solar energy per unit area of a horizontal surface in a grid column accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive towards the surface. It is calculated as the difference between the downward solar energy and the upward solar energy at the surface. Surface net solar radiation J m-2 The average net solar energy per unit area of a horizontal surface in a grid column accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive towards the surface. It is calculated as the difference between the downward solar energy and the upward solar energy at the surface. Surface net solar radiation, clear sky J m-2 The average net solar energy per unit area of a horizontal surface in a grid column accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive towards the surface. It is calculated as the difference between the downward solar energy and the upward solar energy at the surface assuming clear-sky (cloudless) conditions. Surface net solar radiation, clear sky J m-2 The average net solar energy per unit area of a horizontal surface in a grid column accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive towards the surface. It is calculated as the difference between the downward solar energy and the upward solar energy at the surface assuming clear-sky (cloudless) conditions. Surface net thermal radiation J m-2 The average net thermal energy per unit area of the surface in a grid column accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive towards the surface. It is calculated as the difference between the downward thermal energy and the upward thermal energy at the surface. Surface net thermal radiation J m-2 The average net thermal energy per unit area of the surface in a grid column accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive towards the surface. It is calculated as the difference between the downward thermal energy and the upward thermal energy at the surface. Surface net thermal radiation, clear sky J m-2 The average net thermal energy per unit area of the surface in a grid column accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive towards the surface. It is calculated as the difference between the downward thermal energy and the upward thermal energy at the surface assuming clear-sky (cloudless) conditions. Surface net thermal radiation, clear sky J m-2 The average net thermal energy per unit area of the surface in a grid column accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive towards the surface. It is calculated as the difference between the downward thermal energy and the upward thermal energy at the surface assuming clear-sky (cloudless) conditions. Surface pressure Pa Surface air pressure in the grid column. Surface pressure Pa Surface air pressure in the grid column. Surface roughness m The roughness length of the surface is the height above the surface at which the wind profile is assumed to become zero. It is an average over roughness lengths computed for the surface types: sea, inland water, natural land and urban area. The roughness length for natural land is an average of the roughness lengths for soil, vegetation and snow. Surface roughness m The roughness length of the surface is the height above the surface at which the wind profile is assumed to become zero. It is an average over roughness lengths computed for the surface types: sea, inland water, natural land and urban area. The roughness length for natural land is an average of the roughness lengths for soil, vegetation and snow. Surface roughness length for heat m The surface roughness for heat is a measure of the surface resistance to heat transfer. This variable is used to determine the air to surface transfer of heat. Surface roughness length for heat m The surface roughness for heat is a measure of the surface resistance to heat transfer. This variable is used to determine the air to surface transfer of heat. Surface runoff kg m-2 The mass per unit area of water at the surface when saturation occurs. This variable is calculated for the natural land and urban surface types, including soil, vegetation and snow. Surface runoff kg m-2 The mass per unit area of water at the surface when saturation occurs. This variable is calculated for the natural land and urban surface types, including soil, vegetation and snow. Surface sensible heat flux J m-2 The average sensible heat energy per unit area of the surface in a grid column accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive towards the surface. Surface sensible heat flux J m-2 The average sensible heat energy per unit area of the surface in a grid column accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive towards the surface. Surface solar radiation downwards J m-2 The average solar downwards energy per unit area of a horizontal surface in a grid column accumulated from the initial time of the forecast to the forecast time step. Surface solar radiation downwards J m-2 The average solar downwards energy per unit area of a horizontal surface in a grid column accumulated from the initial time of the forecast to the forecast time step. Surface thermal radiation downwards J m-2 The average thermal downwards energy per unit area of the surface in a grid column accumulated from the initial time of the forecast to the forecast time step. Surface thermal radiation downwards J m-2 The average thermal downwards energy per unit area of the surface in a grid column accumulated from the initial time of the forecast to the forecast time step. Time integral of rain flux kg m-2 The average mass per unit area of water droplets falling on the surface in a grid column. It includes all kinds of liquid precipitation, that is convective precipitation and large scale precipitation. It is an accumulated variable from the initial time of the forecast to the forecast time step. Time integral of rain flux kg m-2 The average mass per unit area of water droplets falling on the surface in a grid column. It includes all kinds of liquid precipitation, that is convective precipitation and large scale precipitation. It is an accumulated variable from the initial time of the forecast to the forecast time step. Time integral of snow evaporation flux kg m-2 This variable is the amount of water that has evaporated from snow from the snow-covered area of a grid box, accumulated since the beginning of the forecast. Time integral of snow evaporation flux kg m-2 This variable is the amount of water that has evaporated from snow from the snow-covered area of a grid box, accumulated since the beginning of the forecast. Time integral of surface eastward momentum flux kg m s-1 This variable is the sum of all surface stress components in a direction along the model grid's x-axis. Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. Momentum flux components are associated to orographic gravity waves, the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. For instance, the turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. Positive (negative) values denote stress in the positive (negative) direction along the model grid's x-axis. Time integral of surface eastward momentum flux kg m s-1 This variable is the sum of all surface stress components in a direction along the model grid's x-axis. Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. Momentum flux components are associated to orographic gravity waves, the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. For instance, the turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. Positive (negative) values denote stress in the positive (negative) direction along the model grid's x-axis. Time integral of surface latent heat evaporation flux kg m-2 This variable is the transfer of latent heat (resulting from evaporation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface from liquid water to vapour represents a transfer of energy from the surface to the atmosphere. Time integral of surface latent heat evaporation flux kg m-2 This variable is the transfer of latent heat (resulting from evaporation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface from liquid water to vapour represents a transfer of energy from the surface to the atmosphere. Time integral of surface latent heat sublimation flux J m-2 This variable is the transfer of latent heat (resulting from sublimation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Sublimation from the Earth's surface from solid water to vapour represents a transfer of energy from the surface to the atmosphere. Time integral of surface latent heat sublimation flux J m-2 This variable is the transfer of latent heat (resulting from sublimation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Sublimation from the Earth's surface from solid water to vapour represents a transfer of energy from the surface to the atmosphere. Time integral of surface northward momentum flux kg m s-1 This variable is the sum of all surface stress components in a direction along the model grid's y-axis. Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. Momentum flux components are associated to orographic gravity waves, the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. For instance, the turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. Positive (negative) values denote stress in the positive (negative) direction along the model grid's y-axis. Time integral of surface northward momentum flux kg m s-1 This variable is the sum of all surface stress components in a direction along the model grid's y-axis. Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. Momentum flux components are associated to orographic gravity waves, the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. For instance, the turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. Positive (negative) values denote stress in the positive (negative) direction along the model grid's y-axis. Time integral of total solid precipitation flux kg m-2 The average mass per unit area of snow and ice particles falling on the surface in a grid column. It includes all kinds of solid precipitation, that is convective precipitation and large scale precipitation. It is an accumulated variable from the initial time of the forecast to the forecast time step. Time integral of total solid precipitation flux kg m-2 The average mass per unit area of snow and ice particles falling on the surface in a grid column. It includes all kinds of solid precipitation, that is convective precipitation and large scale precipitation. It is an accumulated variable from the initial time of the forecast to the forecast time step. Time-integrated surface direct short wave radiation flux J m-2 The average direct solar beam energy per unit area of a horizontal surface in a grid column accumulated from the initial time of the forecast to the forecast time step. The direct solar beam includes solar energy from the solar aureole. This variable is calculated as positive towards the surface. Time-integrated surface direct short wave radiation flux J m-2 The average direct solar beam energy per unit area of a horizontal surface in a grid column accumulated from the initial time of the forecast to the forecast time step. The direct solar beam includes solar energy from the solar aureole. This variable is calculated as positive towards the surface. Top net solar radiation J m-2 The average net solar energy per unit area of a horizontal plane at the top of the atmosphere, accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive downwards. It is calculated as the difference between the downward solar energy and the upward solar energy at the top of the atmosphere. Top net solar radiation J m-2 The average net solar energy per unit area of a horizontal plane at the top of the atmosphere, accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive downwards. It is calculated as the difference between the downward solar energy and the upward solar energy at the top of the atmosphere. Top net thermal radiation J m-2 The average net thermal energy per unit area of a horizontal plane at the top of the atmosphere, accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive downwards. It is calculated as the difference between the downward thermal energy and the upward thermal energy at the top of the atmosphere. Top net thermal radiation J m-2 The average net thermal energy per unit area of a horizontal plane at the top of the atmosphere, accumulated from the initial time of the forecast to the forecast time step. This variable is calculated as positive downwards. It is calculated as the difference between the downward thermal energy and the upward thermal energy at the top of the atmosphere. Total cloud cover % Percentage of the grid column for which the sky is covered with clouds. Clouds at any height above the surface are considered. Total cloud cover % Percentage of the grid column for which the sky is covered with clouds. Clouds at any height above the surface are considered. Total column cloud ice water kg m-2 This variable is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this variable. Total column cloud ice water kg m-2 This variable is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this variable. Total column cloud liquid water kg m-2 This variable is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this variable. Total column cloud liquid water kg m-2 This variable is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this variable. Total column graupel kg m-2 This variable is the amount of snow pellets (graupel) contained within a column extending from the surface of the Earth to the top of the atmosphere. Total column graupel kg m-2 This variable is the amount of snow pellets (graupel) contained within a column extending from the surface of the Earth to the top of the atmosphere. Total column integrated water vapour kg m-2 Total amount of water vapour mass load per unit area from the surface to the top of the atmosphere for each grid column. Total column integrated water vapour kg m-2 Total amount of water vapour mass load per unit area from the surface to the top of the atmosphere for each grid column. Total precipitation kg m-2 The average mass per unit area of water droplets and ice particles falling on the surface in a grid column. It includes all kinds of precipitation, that is convective precipitation, large scale precipitation, and liquid and solid precipitation. It is an accumulated variable from the initial time of the forecast to the forecast time step. Total precipitation kg m-2 The average mass per unit area of water droplets and ice particles falling on the surface in a grid column. It includes all kinds of precipitation, that is convective precipitation, large scale precipitation, and liquid and solid precipitation. It is an accumulated variable from the initial time of the forecast to the forecast time step. Visibility m The visibility is given with the unit m and is calculated from the cloud water, cloud ice, rain, snow and graupel present at the lowest model level. For cloud and precipitation free conditions mist is calculated from the lowest model level relative humidity and the concentration of cloud condensation nuclei. Visibility m The visibility is given with the unit m and is calculated from the cloud water, cloud ice, rain, snow and graupel present at the lowest model level. For cloud and precipitation free conditions mist is calculated from the lowest model level relative humidity and the concentration of cloud condensation nuclei. Volumetric soil ice m3 m-3 This variable is the water equivalent of volumetric soil ice content. It is the volume that the liquid water would have if the ice melted. Volumetric soil ice m3 m-3 This variable is the water equivalent of volumetric soil ice content. It is the volume that the liquid water would have if the ice melted. Volumetric soil moisture m3 m-3 The volume concentration of liquid water at root depth in the soil. This level varies depending on the surface type and is also defined for surface types without vegetation. Volumetric soil moisture m3 m-3 The volume concentration of liquid water at root depth in the soil. This level varies depending on the surface type and is also defined for surface types without vegetation. 118 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-health-aedes-albopictus-projections https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-health-aedes-albopictus-projections app-health-aedes-albopictus-projections The application presents the season length and a climate suitability index of the recent past and future climate scenarios for the presence of the Asian tiger mosquito (Aedes albopictus) across Europe. The tiger mosquito (Aedes albopictus) is native to tropical and subtropical areas of southeast Asia and is known to transmit vector-borne diseses such as dengue andchikungunya. It arrived in Europe in the 1970's as an invasive species and proceeded to expand in abundance and extent of habitat. Futhermore, the expansion in Europe is predicted to continue under a changing climate as conditions become more suitable for Aedes albopictus survival. Mapping the suitability and season length is important for understanding, and adapting to, the health impacts of climate change. The season length is defined as the time when the mosquito’s eggs hatch after winter until the time that eggs are no longer hatching in autumn when the mosquitos go into diapause (a period of suspended development during unfavourable environmental conditions). The suitability index defines suitability of the environmental conditions for the presence of the Asian tiger mosquito. An index of 0 is completely unsuitable and 100 is completely suitable. The environmental conditions considered are based on the annual rainfall and air-temperature indexes at specific periods. Users can select either the season length or suitability index variable to visualise from the dropdown box. The livemap has five selectable layers: one for the historical period (1976-2005) and two future periods (2031-2060 and 2071-2100) simulated under two projections of future climate (RCP 4.5 and RCP 8.5). The livemap is interactive and can be explored by zooming and panning around Europe. The outlined countries/regions can be selected to provide a detailed analysis of that country/region. This provides a time-series of the 30-year running mean, with upper and lower confidence levels and a carousel of map plots of that region for the 5 periods/scenarios provided in the livemap layers. User-selectable parameters User-selectable parameters Variable - Season length or suitability index Variable - Season length or suitability index Variable INPUT VARIABLES Name Units Description Source Season length Days Duration of Aedes albopictus presence, also known as the mosquito season. Outside of this period mosquitoes die off or go into diapause. Climatic suitability of Aedes aldopictus Suitability index Dimensionless The likelihood that the area has a favourable environmental conditions for the presence of the Asian tiger mosquito with 0 being not suitable (no favourable conditions) and 100 being completely suitable. Climatic suitability of Aedes aldopictus INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Season length Days Duration of Aedes albopictus presence, also known as the mosquito season. Outside of this period mosquitoes die off or go into diapause. Climatic suitability of Aedes aldopictus Season length Days Duration of Aedes albopictus presence, also known as the mosquito season. Outside of this period mosquitoes die off or go into diapause. Climatic suitability of Aedes aldopictus Climatic suitability of Aedes aldopictus Suitability index Dimensionless The likelihood that the area has a favourable environmental conditions for the presence of the Asian tiger mosquito with 0 being not suitable (no favourable conditions) and 100 being completely suitable. Climatic suitability of Aedes aldopictus Suitability index Dimensionless The likelihood that the area has a favourable environmental conditions for the presence of the Asian tiger mosquito with 0 being not suitable (no favourable conditions) and 100 being completely suitable. Climatic suitability of Aedes aldopictus Climatic suitability of Aedes aldopictus 119 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-high-resolution-diurnal-subskin-sea-surface http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_BS_PHY_SUBSKIN_L4_NRT_010_035 Black Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis Short description: For the Black Sea - the CNR diurnal sub-skin Sea Surface Temperature product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16° (0.0625°) horizontal resolution over the CMEMS Black Sea (BS) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS BS Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014). DOI (product) :https://doi.org/10.48670/moi-00157 https://doi.org/10.48670/moi-00157 120 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-upper-troposphere-humidity https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-upper-troposphere-humidity satellite-upper-troposphere-humidity Upper Tropospheric Humidity (UTH) is of key importance to the Earth’s greenhouse effect and understanding of climate change. It is considered an essential climate variable because it controls key atmospheric processes, including water vapour and cloud feedbacks that can amplify the climate system’s response to increases in other greenhouse gases. The UTH Thematic Climate Data Record (TCDR) and Interim Climate Data Record (ICDR) are derived from observations from the AMSU-B and MHS microwave humidity sounder instruments on board the NOAA- and MetOp- satellite series. Instantaneous satellite observations are used to derive a spatio-temporal averaged data record. The data are available as twice daily (one for the ascending and the other for the descending passes) averages on a regular latitude/longitude grid. Additionally, the daily mean UTH is provided as a weighted average of ascending and descending orbits for all the grid points with valid ascending and descending observations. UTH is the Jacobian weighted relative humidity in the upper troposphere. Its natural logarithm can be expressed as a linear function of the radiance emanated from water vapour emissions in the upper troposphere, expressed as brightness temperature. Various threshold masks are applied in order to identify and remove measurements contaminated by clouds, for example due to scattering by large ice particles in the presence of deep convection, and measurements contaminated by the surface. A viewing angle dependent analytical equation is used to convert off-nadir brightness temperatures into nadir equivalents, thereby providing a correction for limb darkening effects. UTH is part of the Water Vapour ECV inventory in the CDS together with Total Column Water Vapour and Tropospheric Humidity Profiles. The TCDR component of this dataset is brokered to the CDS and was originally produced on behalf of EUMETSAT Satellite Application Facility on Climate Monitoring (CMSAF) by the Met Office. The ICDR component has been delivered directly to the CDS and was originally produced on behalf of the C3S by the Met Office. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 1.0° x 1.0° Vertical coverage Upper troposphere (500 hPa - 200 hPa) Vertical resolution Single column value Temporal coverage From 1999 to present Temporal resolution Daily File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions v1.0 Update frequency Updated quarterly DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 1.0° x 1.0° Horizontal resolution 1.0° x 1.0° Vertical coverage Upper troposphere (500 hPa - 200 hPa) Vertical coverage Upper troposphere (500 hPa - 200 hPa) Vertical resolution Single column value Vertical resolution Single column value Temporal coverage From 1999 to present Temporal coverage From 1999 to present Temporal resolution Daily Temporal resolution Daily File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions v1.0 Versions v1.0 Update frequency Updated quarterly Update frequency Updated quarterly MAIN VARIABLES Name Units Description Upper tropospheric humidty % Estimates of the relative humidity in the upper troposphere column provided for the mean of all daily observations, the mean of all ascending (South to North) overpass observations and the mean of all descending (North to South) overpass observations. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Upper tropospheric humidty % Estimates of the relative humidity in the upper troposphere column provided for the mean of all daily observations, the mean of all ascending (South to North) overpass observations and the mean of all descending (North to South) overpass observations. Upper tropospheric humidty % Estimates of the relative humidity in the upper troposphere column provided for the mean of all daily observations, the mean of all ascending (South to North) overpass observations and the mean of all descending (North to South) overpass observations. RELATED VARIABLES The standard deviation, median, number of valid retrievals and total number of observations associated with each of the main variables are also included in the files, as well as the longitude and latitude of the relevant 1 degree by 1 degree grid box and the observation day. Quality controlled brightness temperatures are also provided. RELATED VARIABLES RELATED VARIABLES The standard deviation, median, number of valid retrievals and total number of observations associated with each of the main variables are also included in the files, as well as the longitude and latitude of the relevant 1 degree by 1 degree grid box and the observation day. Quality controlled brightness temperatures are also provided. The standard deviation, median, number of valid retrievals and total number of observations associated with each of the main variables are also included in the files, as well as the longitude and latitude of the relevant 1 degree by 1 degree grid box and the observation day. Quality controlled brightness temperatures are also provided. 121 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-satellite-humidity-seasonal-variation https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-satellite-humidity-seasonal-variation app-satellite-humidity-seasonal-variation The application shows time series of seasonalised (with the seasonal cycles retained in the data) and de-seasonalised monthly mean anomalies of specific humidity, vertically resolved as well as averaged in vertical layers. The statistics were derived from a large number of humidity profiles retrieved from satellite-based Radio Occultation (RO) measurements. Atmospheric humidity plays an important role in the Earth's climate system, both for its strong greenhouse effect but also for its role in the global energy transport. It is central to the hydrological cycle and contributes to determining the fundamental conditions for the biosphere, including distribution of rainfall and droughts. The gridded monthly-mean tropospheric humidity dataset, originating from EUMETSAT's ROM SAF facility, comprises a time series of continuous humidity observations from space, starting in 2006 and regularly extended up to present. As measurements encompass the entire globe, from the surface up to an altitude of 12 kilometers, and have high vertical resolution revealing fine scale details of the variations with height, the dataset is well suited for analysis of the latitudinal and height distributions of humidity. The upper two figures depict time series of anomalies in a user selectable region. The left-hand panel shows height-resolved time series while the panel to the right shows the same data vertically averaged in three 4-km height layers. The anomalies are here defined as the differences between the monthly means in the latitude-height bins and the corresponding long-term averages for the bins, i.e. seasonal cycles are retained in the anomaly data. We find that the time variations in specific humidity at these latitudes are dominated by a somewhat irregular seasonal cycle with amplitudes on the order of 5%, with occasional peaks up to around 10%. Similar time series for northern mid-latitudes and northern high latitudes show that the seasonal cycles are more regular and much stronger at these latitudes, with variations up to a factor of four in specific humidity between winter and summer seasons. The lower two figures show the de-seasonalised anomalies in height-resolved data (left-hand panel) and in vertically averaged data (right-hand panel), for low latitudes and for globally averaged data. The anomalies are computed by subtracting a mean annual cycle, rather than a long-term average, from the monthly means in the latitude-height bins. The mean annual cycle is computed for the entire available time-series. By removing the dominating annual cycle, all other time variations stand out much more clearly. These include the effects of semi-annual variations, such as the El Niño-Southern Oscillation (ENSO), as well as long-term trends in the data. For example, the two episodes in 2009-2010 and 2015-2016 with increased humidity coincide with major El Niño events, the warm phase of the ENSO that is characterised by a warming of the ocean surface in the central and Eastern tropical Pacific Ocean. We find that the humidity anomalies are remarkably similar in tropical and in global data, and that strong ENSO events have truly global consequences. User-selectable parameters User-selectable parameters Region (pre-defined latitude bands) 90S-60S 60S-30S 30S-30N 30N-60N 60N-90N 90N-90S Region (pre-defined latitude bands) 90S-60S 60S-30S 30S-30N 30N-60N 60N-90N 90N-90S Region (pre-defined latitude bands) Region 90S-60S 60S-30S 30S-30N 30N-60N 60N-90N 90N-90S 90S-60S 60S-30S 30S-30N 30N-60N 60N-90N 90N-90S INPUT VARIABLES Name Units Description Source Specific humidity g kg-1 The ratio of the mass of water vapour in air to the total mass of the mixture of air and water vapour. Values represent the monthly mean for 5-degree latitude bands and altitudes below 12 km for all longitudes. Satellite humidity profiles INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Specific humidity g kg-1 The ratio of the mass of water vapour in air to the total mass of the mixture of air and water vapour. Values represent the monthly mean for 5-degree latitude bands and altitudes below 12 km for all longitudes. Satellite humidity profiles Specific humidity g kg-1 The ratio of the mass of water vapour in air to the total mass of the mixture of air and water vapour. Values represent the monthly mean for 5-degree latitude bands and altitudes below 12 km for all longitudes. Satellite humidity profiles Satellite humidity profiles OUTPUT VARIABLES Name Units Description Relative specific humidity anomaly % The relative anomaly of the specific humidity. Anomalies are provided with respect to the mean of the entire time series and with respect to the monthly mean climatology. This allows identification of both seasonal patterns and interannual variability and long-term trends. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Relative specific humidity anomaly % The relative anomaly of the specific humidity. Anomalies are provided with respect to the mean of the entire time series and with respect to the monthly mean climatology. This allows identification of both seasonal patterns and interannual variability and long-term trends. Relative specific humidity anomaly % The relative anomaly of the specific humidity. Anomalies are provided with respect to the mean of the entire time series and with respect to the monthly mean climatology. This allows identification of both seasonal patterns and interannual variability and long-term trends. 122 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-uerra-europe-height-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-uerra-europe-height-levels reanalysis-uerra-europe-height-levels The present UERRA dataset contains analyses of atmospheric variables on height levels, from 1961 to present. The data have been generated using the UERRA-HARMONIE system by combining model data with observations into a complete and consistent dataset using the laws of physics. This principle is called data assimilation. UERRA-HARMONIE employs a 3-dimensional variational data assimilation method. The assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The laws of physics allow for estimates at locations where data coverage is low. The provision of estimates at each grid point in Europe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with. The observing system has evolved drastically over time, and although the assimilation system can resolve data holes, the quality of analyses varies throughout the period, with less accurate estimates in 1960s due to sparse observational networks. In addition to observations in the model domain, a regional reanalysis needs model data which provide a first estimate of the atmospheric state. For the UERRA-HARMONIE system, this information is taken from the global reanalyses ERA40 (until the end of 1978) and ERA-interim (from 1979 onwards). The improvement of regional reanalyses over global products comes with the higher horizontal resolution that allows incorporating more regional details (e.g. topography). Moreover, it enables the system even to ingest more observations at places with dense observation networks. DATA DESCRIPTION Data type Gridded Projection Lambert conformal conic grid with 565 x 565 points Horizontal coverage Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Horizontal resolution 11km x 11km. Vertical coverage 11 height levels from 15m to 500m. Vertical resolution 15m, 30m, 50m, 75m, 100m, 150m, 200m, 250m, 300m, 400m, 500m. Temporal coverage January 1961 to July 2019. Temporal resolution Analyses are available each day at 00, 06, 12 and 18 UTC. File format GRIB2 Update frequency No expected updates. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert conformal conic grid with 565 x 565 points Projection Lambert conformal conic grid with 565 x 565 points Horizontal coverage Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Horizontal coverage Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Horizontal resolution 11km x 11km. Horizontal resolution 11km x 11km. Vertical coverage 11 height levels from 15m to 500m. Vertical coverage 11 height levels from 15m to 500m. Vertical resolution 15m, 30m, 50m, 75m, 100m, 150m, 200m, 250m, 300m, 400m, 500m. Vertical resolution 15m, 30m, 50m, 75m, 100m, 150m, 200m, 250m, 300m, 400m, 500m. Temporal coverage January 1961 to July 2019. Temporal coverage January 1961 to July 2019. Temporal resolution Analyses are available each day at 00, 06, 12 and 18 UTC. Temporal resolution Analyses are available each day at 00, 06, 12 and 18 UTC. File format GRIB2 File format GRIB2 Update frequency No expected updates. Update frequency No expected updates. MAIN VARIABLES Name Units Description Pressure Pa Air pressure in the grid cell at a specified height above the model surface. Relative humidity % Relation between actual humidity and saturation humidity at a specified height above the model surface. Values are in the interval [0,100]. 0%means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. Temperature K Air temperature valid for a grid cell at a specified height above the model surface. Wind direction Degrees Wind direction valid for a grid cell at a specified height above the model surface. Values are in the interval [0,360). A value of 0° means a northerly wind and 90° indicates an easterly wind. Wind speed m s-1 Wind speed valid for a grid cell a at specified height above the model surface. It is computed from both the zonal (u) and the meridional (v) wind components by sqrt(u2 + v2 ). MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Pressure Pa Air pressure in the grid cell at a specified height above the model surface. Pressure Pa Air pressure in the grid cell at a specified height above the model surface. Relative humidity % Relation between actual humidity and saturation humidity at a specified height above the model surface. Values are in the interval [0,100]. 0%means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. Relative humidity % Relation between actual humidity and saturation humidity at a specified height above the model surface. Values are in the interval [0,100]. 0%means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. Temperature K Air temperature valid for a grid cell at a specified height above the model surface. Temperature K Air temperature valid for a grid cell at a specified height above the model surface. Wind direction Degrees Wind direction valid for a grid cell at a specified height above the model surface. Values are in the interval [0,360). A value of 0° means a northerly wind and 90° indicates an easterly wind. Wind direction Degrees Wind direction valid for a grid cell at a specified height above the model surface. Values are in the interval [0,360). A value of 0° means a northerly wind and 90° indicates an easterly wind. Wind speed m s-1 Wind speed valid for a grid cell a at specified height above the model surface. It is computed from both the zonal (u) and the meridional (v) wind components by sqrt(u2 + v2 ). Wind speed m s-1 Wind speed valid for a grid cell a at specified height above the model surface. It is computed from both the zonal (u) and the meridional (v) wind components by sqrt(u2 + v2 ). RELATED VARIABLES In order to make data access more manageable, the UERRA dataset has been split into several records. Complementary records to the present one are: UERRA on height levels, UERRA on pressure levels and UERRA on soil levels. For the present single level dataset, forecast data are not accessible through this form. However, the complete dataset including forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC can be accessed through the CDS application programming interface (API). See documentation. RELATED VARIABLES RELATED VARIABLES In order to make data access more manageable, the UERRA dataset has been split into several records. Complementary records to the present one are: UERRA on height levels, UERRA on pressure levels and UERRA on soil levels. For the present single level dataset, forecast data are not accessible through this form. However, the complete dataset including forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC can be accessed through the CDS application programming interface (API). See documentation. In order to make data access more manageable, the UERRA dataset has been split into several records. Complementary records to the present one are: UERRA on height levels, UERRA on pressure levels and UERRA on soil levels. For the present single level dataset, forecast data are not accessible through this form. However, the complete dataset including forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC can be accessed through the CDS application programming interface (API). See documentation. 123 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-fire-radiative-power https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-fire-radiative-power satellite-fire-radiative-power This dataset provides active fire (AF) and fire radiative power (FRP) products derived from observations made by the Sea and Land Surface Temperature Radiometer (SLSTR) operating concurrently onboard the European Sentinel-3A and -3B satellites. These products are generated from the non-time critical (NTC) Level 2 AF detection and FRP product from European Space Agency (ESA). The AF, FRP and the Burnt Areas are Essential Climate Variables (ECV) from the Global Observing System for Climate (GCOS). Burnt Area is provided through a different catalogue entry. The AF and the FRP products encompass a Level 2 summary product which provides a text-based table summary of the Level 2 AF detection and FRP data collected over the period of one month across the globe. In addition, three gridded Level 3 ‘synthesis products’ are also provided, each one grids the Level 2 AF and the FRP data at various spatial and temporal resolutions along with some adjustments for cloud cover that blocks the fires from the satellite sensors view. The following gridded products are available: daily global Level 3 FRP product generated on a global 0.1 degree resolution grid. AF data coming from observations made by both Sentinel-3A (S3A) and -3B (S3B) have separate layers in this product. Only night-time land observations are included at this moment, the full daytime will be provided in the future once the corresponding NTC Level 2 FRP data are available. 27 day global Level 3 FRP product also derived at 0.1 degree grid cell resolution but summarising the FRP data from both Sentinel-3A and -3B. This time period matches the Sentinel-3 satellites standard orbital repeat cycle. monthly global Level 3 FRP product which provides global AF detection and FRP data at a grid cell size of 0.25 degrees. This resolution matches the MODIS Climate Modelling Grid (CMG) active fire product. daily global Level 3 FRP product generated on a global 0.1 degree resolution grid. AF data coming from observations made by both Sentinel-3A (S3A) and -3B (S3B) have separate layers in this product. Only night-time land observations are included at this moment, the full daytime will be provided in the future once the corresponding NTC Level 2 FRP data are available. 27 day global Level 3 FRP product also derived at 0.1 degree grid cell resolution but summarising the FRP data from both Sentinel-3A and -3B. This time period matches the Sentinel-3 satellites standard orbital repeat cycle. monthly global Level 3 FRP product which provides global AF detection and FRP data at a grid cell size of 0.25 degrees. This resolution matches the MODIS Climate Modelling Grid (CMG) active fire product. The purpose of these AF and FRP products is to provide a summarised and 'easy to use' version of the Level 2 AF and FRP products, as well as providing the AF and FRP information in a consistently gridded format. The latter in particular is designed to be suitable for use in global modelling, trend analysis and model evaluation. The AF and FRP were produced on behalf of the Copernicus Climate Change Service and have been designed to be broadly comparable with the equivalent Fire Burnt Area product, since they are likely to be used in combination with them to provide a long-term FRP time-series. Fire Burnt Area DATA DESCRIPTION Data type Gridded for the gridded product Point data for the summary product Horizontal coverage Global Horizontal resolution 0.1°x 0.1° and 0.25°x 0.25° for the gridded product Point data (based on 1km observations) for the summary product Vertical coverage Surface Vertical resolution Single level Temporal coverage From March 2020 to present Temporal resolution Daily, 27 days (Sentinel-3 repeat cycle) and monthly for the gridded product Individual time stamps of active fire events, monthly files for the summary product File format NetCDF4 for the gridded product CSV for the summary product Conventions Climate and Forecast (CF) Metadata Convention v1.7 ESA CCI Data Standards [DSWG 2015] for the gridded product Versions 1.0 Update frequency February every year DATA DESCRIPTION DATA DESCRIPTION Data type Gridded for the gridded product Point data for the summary product Data type Gridded for the gridded product Point data for the summary product Gridded for the gridded product Point data for the summary product Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.1°x 0.1° and 0.25°x 0.25° for the gridded product Point data (based on 1km observations) for the summary product Horizontal resolution 0.1°x 0.1° and 0.25°x 0.25° for the gridded product Point data (based on 1km observations) for the summary product 0.1°x 0.1° and 0.25°x 0.25° for the gridded product Point data (based on 1km observations) for the summary product Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage From March 2020 to present Temporal coverage From March 2020 to present Temporal resolution Daily, 27 days (Sentinel-3 repeat cycle) and monthly for the gridded product Individual time stamps of active fire events, monthly files for the summary product Temporal resolution Daily, 27 days (Sentinel-3 repeat cycle) and monthly for the gridded product Individual time stamps of active fire events, monthly files for the summary product Daily, 27 days (Sentinel-3 repeat cycle) and monthly for the gridded product Individual time stamps of active fire events, monthly files for the summary product File format NetCDF4 for the gridded product CSV for the summary product File format NetCDF4 for the gridded product CSV for the summary product NetCDF4 for the gridded product CSV for the summary product Conventions Climate and Forecast (CF) Metadata Convention v1.7 ESA CCI Data Standards [DSWG 2015] for the gridded product Conventions Climate and Forecast (CF) Metadata Convention v1.7 ESA CCI Data Standards [DSWG 2015] for the gridded product Climate and Forecast (CF) Metadata Convention v1.7 ESA CCI Data Standards [DSWG 2015] for the gridded product Versions 1.0 Versions 1.0 Update frequency February every year Update frequency February every year MAIN VARIABLES Name Units Description Active pixels Count Total number of active fire pixels in a grid cell for either Sentinel-3A or Sentinel-3B. Called s3a_night_fire or s3b_night_fire in the netCFD files. Fire radiative power MW Fire Radiative Power (FRP) represents the rate of outgoing thermal radiative energy coming from a burning landscape fire, integrated over all emitted wavelengths and over the hemisphere above the fire. It is expressed in Watts (Joules per Sec). Within a single pixels field of view there can be many landscape fires burning and thus the FRP recorded is that of all fires within the pixel, and is typically expressed in MWatts (MW). The FRP is measured at night either by Sentinel-3A or -3B and when derived from MWIR (middle infrared) channel observations, which are those most suitable for landscape fire FRP estimation, is termed FRP_MWIR in the netCDF files. NetCDF files also provides FRP_SWIR, which is the same FRP metric as represented by FRP_MWIR, but now derived using SWIR channel observations. This form of FRP derivation is more suited to phenomena of higher temperature than landscape fires, most notably industrial gas flares. Mean fire radiative power MW Rate of radiant heat output from a fire. This variable is measured during night time either by Sentinel-3A or Sentinel-3B. Called s3a_night_frp or s3b_night_frp in the netCFD files. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Active pixels Count Total number of active fire pixels in a grid cell for either Sentinel-3A or Sentinel-3B. Called s3a_night_fire or s3b_night_fire in the netCFD files. Active pixels Count Total number of active fire pixels in a grid cell for either Sentinel-3A or Sentinel-3B. Called s3a_night_fire or s3b_night_fire in the netCFD files. Fire radiative power MW Fire Radiative Power (FRP) represents the rate of outgoing thermal radiative energy coming from a burning landscape fire, integrated over all emitted wavelengths and over the hemisphere above the fire. It is expressed in Watts (Joules per Sec). Within a single pixels field of view there can be many landscape fires burning and thus the FRP recorded is that of all fires within the pixel, and is typically expressed in MWatts (MW). The FRP is measured at night either by Sentinel-3A or -3B and when derived from MWIR (middle infrared) channel observations, which are those most suitable for landscape fire FRP estimation, is termed FRP_MWIR in the netCDF files. NetCDF files also provides FRP_SWIR, which is the same FRP metric as represented by FRP_MWIR, but now derived using SWIR channel observations. This form of FRP derivation is more suited to phenomena of higher temperature than landscape fires, most notably industrial gas flares. Fire radiative power MW Fire Radiative Power (FRP) represents the rate of outgoing thermal radiative energy coming from a burning landscape fire, integrated over all emitted wavelengths and over the hemisphere above the fire. It is expressed in Watts (Joules per Sec). Within a single pixels field of view there can be many landscape fires burning and thus the FRP recorded is that of all fires within the pixel, and is typically expressed in MWatts (MW). The FRP is measured at night either by Sentinel-3A or -3B and when derived from MWIR (middle infrared) channel observations, which are those most suitable for landscape fire FRP estimation, is termed FRP_MWIR in the netCDF files. NetCDF files also provides FRP_SWIR, which is the same FRP metric as represented by FRP_MWIR, but now derived using SWIR channel observations. This form of FRP derivation is more suited to phenomena of higher temperature than landscape fires, most notably industrial gas flares. Mean fire radiative power MW Rate of radiant heat output from a fire. This variable is measured during night time either by Sentinel-3A or Sentinel-3B. Called s3a_night_frp or s3b_night_frp in the netCFD files. Mean fire radiative power MW Rate of radiant heat output from a fire. This variable is measured during night time either by Sentinel-3A or Sentinel-3B. Called s3a_night_frp or s3b_night_frp in the netCFD files. RELATED VARIABLES The files also contain a number of related variables accounting for the uncertainty of the measurements as well as the land, water and cloud characteristics of each pixel RELATED VARIABLES RELATED VARIABLES The files also contain a number of related variables accounting for the uncertainty of the measurements as well as the land, water and cloud characteristics of each pixel The files also contain a number of related variables accounting for the uncertainty of the measurements as well as the land, water and cloud characteristics of each pixel 124 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-physics-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=ARCTIC_MULTIYEAR_PHY_002_003 Arctic Ocean Physics Reanalysis Short description: The current version of the TOPAZ system - TOPAZ4b - is nearly identical to the real-time forecast system run at MET Norway. It uses a recent version of the Hybrid Coordinate Ocean Model (HYCOM) developed at University of Miami (Bleck 2002). HYCOM is coupled to a sea ice model; ice thermodynamics are described in Drange and Simonsen (1996) and the elastic-viscous-plastic rheology in Hunke and Dukowicz (1997). The model's native grid covers the Arctic and North Atlantic Oceans, has fairly homogeneous horizontal spacing (between 11 and 16 km). 50 hybrid layers are used in the vertical (z-isopycnal), more than the TOPAZ4 system (28 layers). TOPAZ4b uses the Deterministic version of the Ensemble Kalman filter (DEnKF; Sakov and Oke 2008) to assimilate remotely sensed as well as temperature and salinity profiles. The output is interpolated onto standard grids and depths. Daily values are provided at all depths. Data assimilation, including the 100-member ensemble production, is performed weekly. DOI (product) :https://doi.org/10.48670/moi-00007 https://doi.org/10.48670/moi-00007 125 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/projections-climate-atlas https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-climate-atlas projections-climate-atlas This catalogue entry provides gridded data from global (CMIP5 and CMIP6) and regional (CORDEX) projections for the set of 22 variables and indices included in the IPCC Interactive Atlas, a novel contribution from Working Group I (WGI) to the IPCC Sixth Assessment Report (AR6). These variables and indices are relevant for the climatic impact-drivers used in the regional assessments conducted in AR6 (Chapters 10, 11, 12 and Atlas), related to heat and cold, wet and dry, snow and ice, and wind. This dataset is particularly intended for Climate Data Store (CDS) users who want to develop customised products not directly available from the IPCC Interactive Atlas (e.g. regional information at national or subnational scales). This dataset includes gridded information with monthly/annual temporal resolution for historical experiments and climate projections based on Representative Concentration Pathways (RCP) / Shared Socioeconomic Pathways (SSP) scenarios for CMIP5/6 and CORDEX multi-model ensembles for the 22 variables and indices (computed from daily data). The ensembles are harmonised using regular grids with horizontal resolutions of 2° (CMIP5), 1° (CMIP6), 0.5° (CORDEX), and 0.25° (European CORDEX domain); details on the particular ensembles for each dataset are included in the documentation links. This dataset allows the reproduction, expansion and customisation of the climate change products displayed in the IPCC Interactive Atlas. This includes the global/continental maps of CMIP/CORDEX climate changes (for future periods across scenarios or for global warming levels, e.g. +2°C), and the regionally-aggregated time series, scatter plots, or global warming level plots. Related datasets, also available through the CDS, include the CMIP5/6 global climate projections and the CORDEX regional climate projections. The original CMIP and CORDEX data was produced by the institutions and modelling centres participating in these initiatives, as described in AR6 WGI Annex II, with partial support from different programmes, including support from Copernicus for some of the EURO-CORDEX runs and for data curation and publication of world-wide CORDEX datasets. As a result, the dataset is fully reproducible from the CDS for CORDEX, but not for CMIP (some models and versions are different in the CDS and the Atlas ensembles). This dataset is distributed as part of the IPCC-DDC Atlas products under a Creative Commons Attribution 4.0 International License (CC-BY 4.0) and Copernicus has supported the standardisation and technical curation. DATA DESCRIPTION Data type Gridded Horizontal coverage Global (CMIP5, CMIP6), 11 regional domains (CORDEX) Horizontal resolution CMIP5: 2°x2°; CMIP6: 1°x1°; CORDEX: 0.5°x0.5° including EURO-CORDEX with 0.25°x0.25° Vertical coverage Variables are provided at a specific single level for each variable, which may vary among variables. Temporal coverage 1850-2100 overall, but depends on experiment Temporal resolution Monthly or annual File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.9 Versions Frozen version used for the IPCC AR6 Update frequency No updates planned (frozen version of the IPCC Interactive Atlas data) DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global (CMIP5, CMIP6), 11 regional domains (CORDEX) Horizontal coverage Global (CMIP5, CMIP6), 11 regional domains (CORDEX) Horizontal resolution CMIP5: 2°x2°; CMIP6: 1°x1°; CORDEX: 0.5°x0.5° including EURO-CORDEX with 0.25°x0.25° Horizontal resolution CMIP5: 2°x2°; CMIP6: 1°x1°; CORDEX: 0.5°x0.5° including EURO-CORDEX with 0.25°x0.25° Vertical coverage Variables are provided at a specific single level for each variable, which may vary among variables. Vertical coverage Variables are provided at a specific single level for each variable, which may vary among variables. Temporal coverage 1850-2100 overall, but depends on experiment Temporal coverage 1850-2100 overall, but depends on experiment Temporal resolution Monthly or annual Temporal resolution Monthly or annual File format NetCDF4 File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.9 Conventions Climate and Forecast (CF) Metadata Convention v1.9 Versions Frozen version used for the IPCC AR6 Versions Frozen version used for the IPCC AR6 Update frequency No updates planned (frozen version of the IPCC Interactive Atlas data) Update frequency No updates planned (frozen version of the IPCC Interactive Atlas data) MAIN VARIABLES Name Units Description Annual consecutive dry days days Annual maximum of consecutive days when daily accumulated precipitation amount is below 1 mm Annual cooling degree-days °C day Annual energy consumption to cool the excess of temperature above 22 °C Annual heating degree-days °C day Annual energy consumption to heat the deficit of temperature below 15.5 °C Bias adjusted monthly count of days with maximum temperature above 35 °C days Bias adjusted (ISIMIP3 trend preserving method) monthly count of days with maximum near-surface (2 meters) temperature above 35 °C Bias adjusted monthly count of days with maximum temperature above 40 °C days Bias adjusted (ISIMIP3 trend preserving method) monthly count of days with maximum near-surface temperature above 40 °C Monthly count of days with maximum temperature above 35 °C days Monthly count of days with maximum near-surface (2 meters) temperature above 35 °C Monthly count of days with maximum temperature above 40 °C days Monthly count of days with maximum near-surface (2 meters) temperature above 40 °C Monthly count of frost days days Monthly count of days with minimum near-surface (2 meters) temperature below 0 °C Monthly maximum of 1-day accumulated precipitation mm Monthly maximum of 1-day accumulated precipitation of liquid water equivalent from all phases Monthly maximum of 5-day accumulated precipitation mm Monthly maximum of 5-day accumulated precipitation of liquid water equivalent from all phases Monthly maximum of daily maximum temperature °C Monthly maximum of daily maximum near-surface (2 meters) air temperature Monthly mean of acidity (pH) of seawater Dimensionless Monthly mean of negative log of hydrogen ion concentration with the concentration expressed as mol H kg-1 Monthly mean of daily accumulated precipitation mm Monthly mean of daily accumulated precipitation of liquid water equivalent from all phases Monthly mean of daily accumulated snowfall precipitation mm Monthly mean of daily accumulated liquid water equivalent thickness snowfall Monthly mean of daily maximum temperature °C Monthly mean of daily maximum near-surface (2 meters) air temperature Monthly mean of daily mean temperature °C Monthly mean of daily mean near-surface (2 meters) air temperature Monthly mean of daily mean wind speed m s⁻¹ Monthly mean of daily mean near-surface (10 meters) wind speed Monthly mean of daily minimum temperature °C Monthly mean of daily minimum near-surface (2 meters) air temperature Monthly mean of sea surface temperature °C Monthly mean temperature of sea water near the surface Monthly mean of sea-ice area percentage % Monthly mean percentage of sea grid cell area covered by ice Monthly minimum of daily minimum temperature °C Monthly minimum of daily minimum near-surface (2 meters) air temperature Standardized precipitation index for 6 months cumulation period Dimensionless Monthly index that compares accumulated precipitation for 6 months with the long-term precipitation distribution for the same location and accumulation period MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Annual consecutive dry days days Annual maximum of consecutive days when daily accumulated precipitation amount is below 1 mm Annual consecutive dry days days Annual maximum of consecutive days when daily accumulated precipitation amount is below 1 mm Annual cooling degree-days °C day Annual energy consumption to cool the excess of temperature above 22 °C Annual cooling degree-days °C day Annual energy consumption to cool the excess of temperature above 22 °C Annual heating degree-days °C day Annual energy consumption to heat the deficit of temperature below 15.5 °C Annual heating degree-days °C day Annual energy consumption to heat the deficit of temperature below 15.5 °C Bias adjusted monthly count of days with maximum temperature above 35 °C days Bias adjusted (ISIMIP3 trend preserving method) monthly count of days with maximum near-surface (2 meters) temperature above 35 °C Bias adjusted monthly count of days with maximum temperature above 35 °C days Bias adjusted (ISIMIP3 trend preserving method) monthly count of days with maximum near-surface (2 meters) temperature above 35 °C Bias adjusted monthly count of days with maximum temperature above 40 °C days Bias adjusted (ISIMIP3 trend preserving method) monthly count of days with maximum near-surface temperature above 40 °C Bias adjusted monthly count of days with maximum temperature above 40 °C days Bias adjusted (ISIMIP3 trend preserving method) monthly count of days with maximum near-surface temperature above 40 °C Monthly count of days with maximum temperature above 35 °C days Monthly count of days with maximum near-surface (2 meters) temperature above 35 °C Monthly count of days with maximum temperature above 35 °C days Monthly count of days with maximum near-surface (2 meters) temperature above 35 °C Monthly count of days with maximum temperature above 40 °C days Monthly count of days with maximum near-surface (2 meters) temperature above 40 °C Monthly count of days with maximum temperature above 40 °C days Monthly count of days with maximum near-surface (2 meters) temperature above 40 °C Monthly count of frost days days Monthly count of days with minimum near-surface (2 meters) temperature below 0 °C Monthly count of frost days days Monthly count of days with minimum near-surface (2 meters) temperature below 0 °C Monthly maximum of 1-day accumulated precipitation mm Monthly maximum of 1-day accumulated precipitation of liquid water equivalent from all phases Monthly maximum of 1-day accumulated precipitation mm Monthly maximum of 1-day accumulated precipitation of liquid water equivalent from all phases Monthly maximum of 5-day accumulated precipitation mm Monthly maximum of 5-day accumulated precipitation of liquid water equivalent from all phases Monthly maximum of 5-day accumulated precipitation mm Monthly maximum of 5-day accumulated precipitation of liquid water equivalent from all phases Monthly maximum of daily maximum temperature °C Monthly maximum of daily maximum near-surface (2 meters) air temperature Monthly maximum of daily maximum temperature °C Monthly maximum of daily maximum near-surface (2 meters) air temperature Monthly mean of acidity (pH) of seawater Dimensionless Monthly mean of negative log of hydrogen ion concentration with the concentration expressed as mol H kg-1 Monthly mean of acidity (pH) of seawater Dimensionless Monthly mean of negative log of hydrogen ion concentration with the concentration expressed as mol H kg-1 Monthly mean of daily accumulated precipitation mm Monthly mean of daily accumulated precipitation of liquid water equivalent from all phases Monthly mean of daily accumulated precipitation mm Monthly mean of daily accumulated precipitation of liquid water equivalent from all phases Monthly mean of daily accumulated snowfall precipitation mm Monthly mean of daily accumulated liquid water equivalent thickness snowfall Monthly mean of daily accumulated snowfall precipitation mm Monthly mean of daily accumulated liquid water equivalent thickness snowfall Monthly mean of daily maximum temperature °C Monthly mean of daily maximum near-surface (2 meters) air temperature Monthly mean of daily maximum temperature °C Monthly mean of daily maximum near-surface (2 meters) air temperature Monthly mean of daily mean temperature °C Monthly mean of daily mean near-surface (2 meters) air temperature Monthly mean of daily mean temperature °C Monthly mean of daily mean near-surface (2 meters) air temperature Monthly mean of daily mean wind speed m s⁻¹ Monthly mean of daily mean near-surface (10 meters) wind speed Monthly mean of daily mean wind speed m s⁻¹ Monthly mean of daily mean near-surface (10 meters) wind speed Monthly mean of daily minimum temperature °C Monthly mean of daily minimum near-surface (2 meters) air temperature Monthly mean of daily minimum temperature °C Monthly mean of daily minimum near-surface (2 meters) air temperature Monthly mean of sea surface temperature °C Monthly mean temperature of sea water near the surface Monthly mean of sea surface temperature °C Monthly mean temperature of sea water near the surface Monthly mean of sea-ice area percentage % Monthly mean percentage of sea grid cell area covered by ice Monthly mean of sea-ice area percentage % Monthly mean percentage of sea grid cell area covered by ice Monthly minimum of daily minimum temperature °C Monthly minimum of daily minimum near-surface (2 meters) air temperature Monthly minimum of daily minimum temperature °C Monthly minimum of daily minimum near-surface (2 meters) air temperature Standardized precipitation index for 6 months cumulation period Dimensionless Monthly index that compares accumulated precipitation for 6 months with the long-term precipitation distribution for the same location and accumulation period Standardized precipitation index for 6 months cumulation period Dimensionless Monthly index that compares accumulated precipitation for 6 months with the long-term precipitation distribution for the same location and accumulation period 126 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/seasonal-original-single-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal-original-single-levels seasonal-original-single-levels This entry covers single-level data at the original time resolution (once a day, or once every 6 hours, depending on the variable). single-level data original time resolution Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Seasonal forecasts Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). multi-system seasonal forecast service The variables available in this data set are listed in the table below. The data includes forecasts created in real-time (since 2017) and retrospective forecasts (hindcasts) initialised at equivalent intervals during the period 1993-2016. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 1° x 1° Temporal coverage 1993 to 2016 (hindcasts); 2017 to present (forecasts) Temporal resolution Subdaily (6h) and daily File format GRIB Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 1° x 1° Horizontal resolution 1° x 1° Temporal coverage 1993 to 2016 (hindcasts); 2017 to present (forecasts) Temporal coverage 1993 to 2016 (hindcasts); 2017 to present (forecasts) Temporal resolution Subdaily (6h) and daily Temporal resolution Subdaily (6h) and daily File format GRIB File format GRIB Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. MAIN VARIABLES Name Units Frequency 10m u-component of wind m s-1 6h instantaneous 10m v-component of wind m s-1 6h instantaneous 10m wind gust since previous post-processing m s-1 24h aggregation 2m dewpoint temperature K 6h instantaneous 2m temperature K 6h instantaneous Eastward turbulent surface stress N m-2 s 24h aggregation since beginning of forecast Evaporation m of water equivalent 24h aggregation since beginning of forecast Land-sea mask (0 - 1) Static Maximum 2m temperature in the last 24 hours K 24h aggregation Mean sea level pressure Pa 6h instantaneous Minimum 2m temperature in the last 24 hours K 24h aggregation Northward turbulent surface stress N m-2 s 24h aggregation since beginning of forecast Orography m2 s-2 Static Runoff m 24h aggregation since beginning of forecast Sea surface temperature K 6h instantaneous Sea-ice cover (0 - 1) 24h instantaneous Snow density kg m-3 24h instantaneous Snow depth m of water equivalent 24h instantaneous Snowfall m of water equivalent 24h aggregation since beginning of forecast Soil temperature level 1 K 6h instantaneous Sub-surface runoff m Surface latent heat flux J m-2 24h aggregation since beginning of forecast Surface net solar radiation J m-2 24h aggregation since beginning of forecast Surface net thermal radiation J m-2 24h aggregation since beginning of forecast Surface runoff m Surface sensible heat flux J m-2 24h aggregation since beginning of forecast Surface solar radiation downwards J m-2 24h aggregation since beginning of forecast Surface thermal radiation downwards J m-2 24h aggregation since beginning of forecast TOA incident solar radiation J m-2 24h aggregation since beginning of forecast Top net solar radiation J m-2 24h aggregation since beginning of forecast Top net thermal radiation J m-2 24h aggregation since beginning of forecast Total cloud cover (0 - 1) 6h instantaneous Total column cloud ice water kg m-2 24h instantaneous Total column cloud liquid water kg m-2 24h instantaneous Total column water vapour kg m-2 24h instantaneous Total precipitation m 24h aggregation since beginning of forecast MAIN VARIABLES MAIN VARIABLES Name Units Frequency Name Units Frequency 10m u-component of wind m s-1 6h instantaneous 10m u-component of wind m s-1 6h instantaneous 10m v-component of wind m s-1 6h instantaneous 10m v-component of wind m s-1 6h instantaneous 10m wind gust since previous post-processing m s-1 24h aggregation 10m wind gust since previous post-processing m s-1 24h aggregation 2m dewpoint temperature K 6h instantaneous 2m dewpoint temperature K 6h instantaneous 2m temperature K 6h instantaneous 2m temperature K 6h instantaneous Eastward turbulent surface stress N m-2 s 24h aggregation since beginning of forecast Eastward turbulent surface stress N m-2 s 24h aggregation since beginning of forecast Evaporation m of water equivalent 24h aggregation since beginning of forecast Evaporation m of water equivalent 24h aggregation since beginning of forecast Land-sea mask (0 - 1) Static Land-sea mask (0 - 1) Static Maximum 2m temperature in the last 24 hours K 24h aggregation Maximum 2m temperature in the last 24 hours K 24h aggregation Mean sea level pressure Pa 6h instantaneous Mean sea level pressure Pa 6h instantaneous Minimum 2m temperature in the last 24 hours K 24h aggregation Minimum 2m temperature in the last 24 hours K 24h aggregation Northward turbulent surface stress N m-2 s 24h aggregation since beginning of forecast Northward turbulent surface stress N m-2 s 24h aggregation since beginning of forecast Orography m2 s-2 Static Orography m2 s-2 Static Runoff m 24h aggregation since beginning of forecast Runoff m 24h aggregation since beginning of forecast Sea surface temperature K 6h instantaneous Sea surface temperature K 6h instantaneous Sea-ice cover (0 - 1) 24h instantaneous Sea-ice cover (0 - 1) 24h instantaneous Snow density kg m-3 24h instantaneous Snow density kg m-3 24h instantaneous Snow depth m of water equivalent 24h instantaneous Snow depth m of water equivalent 24h instantaneous Snowfall m of water equivalent 24h aggregation since beginning of forecast Snowfall m of water equivalent 24h aggregation since beginning of forecast Soil temperature level 1 K 6h instantaneous Soil temperature level 1 K 6h instantaneous Sub-surface runoff m Sub-surface runoff m Surface latent heat flux J m-2 24h aggregation since beginning of forecast Surface latent heat flux J m-2 24h aggregation since beginning of forecast Surface net solar radiation J m-2 24h aggregation since beginning of forecast Surface net solar radiation J m-2 24h aggregation since beginning of forecast Surface net thermal radiation J m-2 24h aggregation since beginning of forecast Surface net thermal radiation J m-2 24h aggregation since beginning of forecast Surface runoff m Surface runoff m Surface sensible heat flux J m-2 24h aggregation since beginning of forecast Surface sensible heat flux J m-2 24h aggregation since beginning of forecast Surface solar radiation downwards J m-2 24h aggregation since beginning of forecast Surface solar radiation downwards J m-2 24h aggregation since beginning of forecast Surface thermal radiation downwards J m-2 24h aggregation since beginning of forecast Surface thermal radiation downwards J m-2 24h aggregation since beginning of forecast TOA incident solar radiation J m-2 24h aggregation since beginning of forecast TOA incident solar radiation J m-2 24h aggregation since beginning of forecast Top net solar radiation J m-2 24h aggregation since beginning of forecast Top net solar radiation J m-2 24h aggregation since beginning of forecast Top net thermal radiation J m-2 24h aggregation since beginning of forecast Top net thermal radiation J m-2 24h aggregation since beginning of forecast Total cloud cover (0 - 1) 6h instantaneous Total cloud cover (0 - 1) 6h instantaneous Total column cloud ice water kg m-2 24h instantaneous Total column cloud ice water kg m-2 24h instantaneous Total column cloud liquid water kg m-2 24h instantaneous Total column cloud liquid water kg m-2 24h instantaneous Total column water vapour kg m-2 24h instantaneous Total column water vapour kg m-2 24h instantaneous Total precipitation m 24h aggregation since beginning of forecast Total precipitation m 24h aggregation since beginning of forecast 127 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/iberia-biscay-ireland-strong-wave-incidence-index http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=IBI_OMI_SEASTATE_swi Iberia Biscay Ireland Strong Wave Incidence index from Reanalysis 'DEFINITION The Strong Wave Incidence index is proposed to quantify the variability of strong wave conditions in the Iberia-Biscay-Ireland regional seas. The anomaly of exceeding a threshold of Significant Wave Height is used to characterize the wave behavior. A sensitivity test of the threshold has been performed evaluating the differences using several ones (percentiles 75, 80, 85, 90, and 95). From this indicator, it has been chosen the 90th percentile as the most representative, coinciding with the state-of-the-art. Two CMEMS products are used to compute the Strong Wave Incidence index: • IBI-WAV-MYP: IBI_REANALYSIS_WAV_005_006 • IBI-WAV-NRT: IBI_ANALYSIS_FORECAST_WAV_005_005 The Strong Wave Incidence index (SWI) is defined as the difference between the climatic frequency of exceedance (Fclim) and the observational frequency of exceedance (Fobs) of the threshold defined by the 90th percentile (ThP90) of Significant Wave Height (SWH) computed on a monthly basis from hourly data of IBI-WAV-MYP product: SWI = Fobs(SWH > ThP90) – Fclim(SWH > ThP90) Since the Strong Wave Incidence index is defined as a difference of a climatic mean and an observed value, it can be considered an anomaly. Such index represents the percentage that the stormy conditions have occurred above/below the climatic average. Thus, positive/negative values indicate the percentage of hourly data that exceed the threshold above/below the climatic average, respectively. CONTEXT Ocean waves have a high relevance over the coastal ecosystems and human activities. Extreme wave events can entail severe impacts over human infrastructures and coastal dynamics. However, the incidence of severe (90th percentile) wave events also have valuable relevance affecting the development of human activities and coastal environments. The Strong Wave Incidence index based on the CMEMS regional analysis and reanalysis product provides information on the frequency of severe wave events. The IBI-MFC covers the Europe’s Atlantic coast in a region bounded by the 26ºN and 56ºN parallels, and the 19ºW and 5ºE meridians. The western European coast is located at the end of the long fetch of the subpolar North Atlantic (Mørk et al., 2010), one of the world’s greatest wave generating regions (Folley, 2017). Several studies have analyzed changes of the ocean wave variability in the North Atlantic Ocean (Bacon and Carter, 1991; Kursnir et al., 1997; WASA Group, 1998; Bauer, 2001; Wang and Swail, 2004; Dupuis et al., 2006; Wolf and Woolf, 2006; Dodet et al., 2010; Young et al., 2011; Young and Ribal, 2019). The observed variability is composed of fluctuations ranging from the weather scale to the seasonal scale, together with long-term fluctuations on interannual to decadal scales associated with large-scale climate oscillations. Since the ocean surface state is mainly driven by wind stresses, part of this variability in Iberia-Biscay-Ireland region is connected to the North Atlantic Oscillation (NAO) index (Bacon and Carter, 1991; Hurrell, 1995; Bouws et al., 1996, Bauer, 2001; Woolf et al., 2002; Tsimplis et al., 2005; Gleeson et al., 2017). However, later studies have quantified the relationships between the wave climate and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation pattern, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Matínez-Asensio et al., 2016). The Strong Wave Incidence index provides information on incidence of stormy events in four monitoring regions in the IBI domain. The selected monitoring regions are aimed to provide a summarized view of the diverse climatic conditions in the IBI regional domain: Wav1 region monitors the influence of stormy conditions in the West coast of Iberian Peninsula, Wav2 region is devoted to monitor the variability of stormy conditions in the Bay of Biscay, Wav3 region is focused in the northern half of IBI domain, this region is strongly affected by the storms transported by the subpolar front, and Wav4 is focused in the influence of marine storms in the North-East African Coast, the Gulf of Cadiz and Canary Islands. More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Pascual et al., 2020). CMEMS KEY FINDINGS The analysis of the index in the last decades do not show significant trends of the strong wave conditions over the period 1992-2020 with 99% confidence. The maximum wave event reported in region WAV1 (B) occurred in February 2014, producing an increment of 34% of strong wave conditions in the region. Two maximum wave events are found in WAV2 (C) with an increment of 28% of high wave conditions in November 2009 and February 2014. As in regions WAV1 and WAV2, in the region WAV3 (D), a strong wave event took place in February 2014, this event is the one of the maximum events reported in the region with an increment of strong wave conditions of 20%, two months before (December 2013) there was a storm of similar characteristics affecting this region. The region WAV4 (E) present its maximum wave event in January 1996, such event produced a 33% of increment of strong wave conditions in the region. Despite of each monitoring region is affected by independent wave events; the analysis shows several past higher-than-average wave events that were propagated though several monitoring regions: November-December 2010 (WAV3 and WAV2); February 2014 (WAV1, WAV2, and WAV3); and February-March 2018 (WAV1 and WAV4). The analysis of the NRT period (2021 onwards) depicts a significant stormy event affecting the WAV2 region in April 2020 (increment of 20% of high wave conditions). This event is also noticeable in WAV3 region but with lower intensity (increment of 12% of high wave conditions). DOI (product):https://doi.org/10.48670/moi-00251 https://doi.org/10.48670/moi-00251 128 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-c3s-global-temperature-trend-monitor https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-c3s-global-temperature-trend-monitor app-c3s-global-temperature-trend-monitor This application monitors the evolution of the global surface air temperature over recent decades until the present and estimates with a simple model (using a 30-year linear trend), the level of global warming at the date up to which the trend is calculated and the date when global warming will reach the 1.5°C limit of the Paris agreement if the trend continues at that same rate. The application presents a graphic showing, up to the selected month, the change in global average surface air temperature monthly means with respect to the pre-industrial period. Superimposed on it, a line depicts the linear trend of the global temperature increase over the preceding 30-years. A grey box is used to indicate the estimated ‘global warming’ at the time of the selected month and a red marker indicates the time in the future when the trend line reaches the 1.5°C limit. The estimated 1.5°C limit date, i.e. the intersection between the temperature linear trend and 1.5° limit line, varies depending on the set of years used for the trend estimation. By using a slider the user can chose the 30-year window for which these numbers are given and see how the estimates change with time. The application is first and foremost a monitoring tool and the indicative future date is there for illustrative purposes only and should not be interpreted as a forecast. The application is first and foremost a monitoring tool and the indicative future date is there for illustrative purposes only and should not be interpreted as a forecast. The application is presented as a simple x-y plot, where the x-axis is time starting in 1979 and the y-axis the temperature variation with respect to the pre-industrial period. The plot shows: in solid light and dark grey, the monthly average temperature anomalies, with respect to pre-industrial era, up to the month selected via the provided slider; the dark grey line shows values corresponding to the 30 years before the selected month, while the light grey line represents the remaining values back to the first (January 1979) and up to the most recent available month; a solid red line, with the linear global temperature trend calculated from the values of the thirty-year period up until the selected month (the period is indicated by dark grey monthly values); a red circle and a thin vertical red dashed line mark the point where the trend intersects the horizontal dashed redline that represents a global warming increase of 1.5°C, indicated by the red box; a vertical thin grey line placed at the selected month; a horizontal dashed grey line and grey box, mark the point and the corresponding global warming estimate where the trend line intersect the vertical thin grey line placed at the selected month; Sentences at the top left of the plot, indicate the global warming estimate and the corresponding date of the two intersections of the trend line, as described above; an orange shaded area represents the uncertainty associated with both past values of and future climate projections of global warming, as reported in the summary for policymakers of the 2018 IPCC special report “Global Warming of 1.5°C”. an orange line indicate the associated IPCC ‘likely’ estimate of the time interval during which global warming will reach 1.5°C above pre-industrial in solid light and dark grey, the monthly average temperature anomalies, with respect to pre-industrial era, up to the month selected via the provided slider; the dark grey line shows values corresponding to the 30 years before the selected month, while the light grey line represents the remaining values back to the first (January 1979) and up to the most recent available month; a solid red line, with the linear global temperature trend calculated from the values of the thirty-year period up until the selected month (the period is indicated by dark grey monthly values); a red circle and a thin vertical red dashed line mark the point where the trend intersects the horizontal dashed redline that represents a global warming increase of 1.5°C, indicated by the red box; a vertical thin grey line placed at the selected month; a horizontal dashed grey line and grey box, mark the point and the corresponding global warming estimate where the trend line intersect the vertical thin grey line placed at the selected month; Sentences at the top left of the plot, indicate the global warming estimate and the corresponding date of the two intersections of the trend line, as described above; an orange shaded area represents the uncertainty associated with both past values of and future climate projections of global warming, as reported in the summary for policymakers of the 2018 IPCC special report “Global Warming of 1.5°C”. an orange line indicate the associated IPCC ‘likely’ estimate of the time interval during which global warming will reach 1.5°C above pre-industrial In this application: In this application: "Global warming" at a point in time refers to the increase in a 30-year average, centred on the specified time, of Earth’s global surface temperature relative to the pre-industrial period; "Reaching the limit" refers to the moment when the central time of the 30-year average temperature equals 1.5˚C above pre-industrial values; “Pre-industrial values” refers to the approximation of the surface air temperature of this era from the IPCC ‘Global warming of 1.5°C’ report, namely the 1850-1900 average based on instrumental data, an estimated 0.63°C (±0.06°) below the period 1981-2020. "Global warming" at a point in time refers to the increase in a 30-year average, centred on the specified time, of Earth’s global surface temperature relative to the pre-industrial period; "Reaching the limit" refers to the moment when the central time of the 30-year average temperature equals 1.5˚C above pre-industrial values; “Pre-industrial values” refers to the approximation of the surface air temperature of this era from the IPCC ‘Global warming of 1.5°C’ report, namely the 1850-1900 average based on instrumental data, an estimated 0.63°C (±0.06°) below the period 1981-2020. Limitations of the method Limitations of the method The simple model here is not intended as a prediction, rather it is to illustrate the most simple yet still plausible model for how global warming may evolve in the coming years and decades. Introducing further datasets and slightly different methods for estimating the trend would slightly affect the numbers shown, but would not change the fact that the model is a simplification and that a more complex model is needed to encompass the full uncertainty envelope. The envelope given by the projections, as well as the ‘likely’ estimate based on those, as indicated by the orange bar, taken from the IPCC 1.5°C report are indicative of the level of uncertainty associated with making such a prediction. The dataset used only starts in 1971 and is adjusted to be relative to pre-industrial by assuming that the 1981-2010 reference period is 0.63°C warmer than the pre-industrial era, where the definition of pre-industrial values refers to the IPCC’s approximation of the temperature of the pre-industrial era, namely the 1850-1900 average based on instrumental data. User-selectable parameters User-selectable parameters Year and month (slider): the final month of the 30-years-window used to estimate the global temperature anomaly linear trend. Ranging from December 2008 to the most recent available month, with monthly frequency for the most recent twelve months and yearly frequency over the rest of the period. Year and month (slider): the final month of the 30-years-window used to estimate the global temperature anomaly linear trend. Ranging from December 2008 to the most recent available month, with monthly frequency for the most recent twelve months and yearly frequency over the rest of the period. INPUT VARIABLES Name Units Description Source Surface air temperature 1971 to 1978 K Air temperature at surface monthly mean from reanalysis on single levels from 1950 to 1978 ERA5 back-extension Surface air temperature 1979 to present K Air temperature at surface monthly mean from reanalysis on single levels from 1979 to present ERA5 Surface air temperature climatology K Climatology of the air temperature at surface derived from reanalysis from 1980 to 2010 ECV Temperature data from climate projections Celsius Temperature data from climate projections are used to provide an envelope of uncertainty around the global warming estimate derived from reanalysis. IPCC - brokered externally INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Surface air temperature 1971 to 1978 K Air temperature at surface monthly mean from reanalysis on single levels from 1950 to 1978 ERA5 back-extension Surface air temperature 1971 to 1978 K Air temperature at surface monthly mean from reanalysis on single levels from 1950 to 1978 ERA5 back-extension ERA5 back-extension Surface air temperature 1979 to present K Air temperature at surface monthly mean from reanalysis on single levels from 1979 to present ERA5 Surface air temperature 1979 to present K Air temperature at surface monthly mean from reanalysis on single levels from 1979 to present ERA5 ERA5 Surface air temperature climatology K Climatology of the air temperature at surface derived from reanalysis from 1980 to 2010 ECV Surface air temperature climatology K Climatology of the air temperature at surface derived from reanalysis from 1980 to 2010 ECV ECV Temperature data from climate projections Celsius Temperature data from climate projections are used to provide an envelope of uncertainty around the global warming estimate derived from reanalysis. IPCC - brokered externally Temperature data from climate projections Celsius Temperature data from climate projections are used to provide an envelope of uncertainty around the global warming estimate derived from reanalysis. IPCC - brokered externally 129 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/european-north-west-shelfiberia-biscay-irish-seas-high http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_ATL_SST_L4_NRT_OBSERVATIONS_010_025 European North West Shelf/Iberia Biscay Irish Seas – High Resolution ODYSSEA L4 Sea Surface Temperature Analysis Short description: For the Atlantic European North West Shelf Ocean-European North West Shelf/Iberia Biscay Irish Seas. The ODYSSEA NW+IBI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg x 0.02deg horizontal resolution, using satellite data from both infra-red and micro-wave radiometers. It is the sea surface temperature operational nominal product for the Northwest Shelf Sea and Iberia Biscay Irish Seas. DOI (product) :https://doi.org/10.48670/moi-00152 https://doi.org/10.48670/moi-00152 130 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/derived-near-surface-meteorological-variables https://cds.climate.copernicus.eu/cdsapp#!/dataset/derived-near-surface-meteorological-variables derived-near-surface-meteorological-variables This dataset provides bias-corrected reconstruction of near-surface meteorological variables derived from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses (ERA5). It is intended to be used as a meteorological forcing dataset for land surface and hydrological models. The dataset has been obtained using the same methodology used to derive the widely used water, energy and climate change (WATCH) forcing data, and is thus also referred to as WATCH Forcing Data methodology applied to ERA5 (WFDE5). The data are derived from the ERA5 reanalysis product that have been re-gridded to a half-degree resolution. Data have been adjusted using an elevation correction and monthly-scale bias corrections based on Climatic Research Unit (CRU) data (for temperature, diurnal temperature range, cloud-cover, wet days number and precipitation fields) and Global Precipitation Climatology Centre (GPCC) data (for precipitation fields only). Additional corrections are included for varying atmospheric aerosol-loading and separate precipitation gauge observations. For full details please refer to the product user-guide. This dataset was produced on behalf of Copernicus Climate Change Service (C3S) and was generated entirely within the Climate Data Store (CDS) Toolbox. The toolbox source code is provided in the documentation tab. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global land Horizontal resolution 0.5° x 0.5° Vertical coverage Surface Vertical resolution Single level Temporal coverage From 1979 to 2019 Temporal resolution Hourly File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Versions 1.0 (deprecated), 1.1, 2.0 and 2.1 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global land Horizontal coverage Global land Horizontal resolution 0.5° x 0.5° Horizontal resolution 0.5° x 0.5° Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage From 1979 to 2019 Temporal coverage From 1979 to 2019 Temporal resolution Hourly Temporal resolution Hourly File format NetCDF 4 File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Versions 1.0 (deprecated), 1.1, 2.0 and 2.1 Versions 1.0 (deprecated), 1.1, 2.0 and 2.1 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Grid-point altitude m The altitude of each grid-point. Values correspond to altitudes of CRU grid-points. Near-surface air temperature K The temperature of air at 2 metres above the surface of land, sea or inland waters. Values are derived from ERA5 2m air temperature with an elevation correction and bias correction using CRU mean monthly temperature and mean diurnal temperature range. Near-surface specific humidity kg kg-1 The amount of moisture in the air divided by amount of air plus moisture at that location. Values are derived from ERA5 vapor pressure and saturation vapor pressure with an elevation correction. Near-surface wind speed m s-1 The horizontal speed of the wind, or movement of air, at a height of 10 metres above the surface of the Earth. Values are derived from ERA5 near-surface wind speed. Rainfall flux kg m-2 s-1 The rate of rain that falls to the Earth's surface. Values are derived from ERA5 total precipitation and snowfall and are bias corrected primarily using precipitation data from CRU and GPCC. Snowfall flux kg m-2 s-1 The rate of snow that falls to the Earth's surface. Values are derived from ERA5 total precipitation and snowfall and are bias corrected primarily using precipitation data from CRU and GPCC. Surface air pressure Pa The pressure (force per unit area) of the atmosphere at the surface of land, sea and inland water. Values are derived from ERA5 surface air pressure with an elevation correction. Surface downwelling longwave radiation W m-2 The amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. Values are derived from ERA5 surface downwelling longwave radiation with an elevation correction. Surface downwelling shortwave radiation W m-2 The amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Values are derived from ERA5 surface downwelling shortwave radiation and bias corrected using CRU cloud cover and effects of inter-annual changes in atmospheric aerosol loading. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Grid-point altitude m The altitude of each grid-point. Values correspond to altitudes of CRU grid-points. Grid-point altitude m The altitude of each grid-point. Values correspond to altitudes of CRU grid-points. Near-surface air temperature K The temperature of air at 2 metres above the surface of land, sea or inland waters. Values are derived from ERA5 2m air temperature with an elevation correction and bias correction using CRU mean monthly temperature and mean diurnal temperature range. Near-surface air temperature K The temperature of air at 2 metres above the surface of land, sea or inland waters. Values are derived from ERA5 2m air temperature with an elevation correction and bias correction using CRU mean monthly temperature and mean diurnal temperature range. Near-surface specific humidity kg kg-1 The amount of moisture in the air divided by amount of air plus moisture at that location. Values are derived from ERA5 vapor pressure and saturation vapor pressure with an elevation correction. Near-surface specific humidity kg kg-1 The amount of moisture in the air divided by amount of air plus moisture at that location. Values are derived from ERA5 vapor pressure and saturation vapor pressure with an elevation correction. Near-surface wind speed m s-1 The horizontal speed of the wind, or movement of air, at a height of 10 metres above the surface of the Earth. Values are derived from ERA5 near-surface wind speed. Near-surface wind speed m s-1 The horizontal speed of the wind, or movement of air, at a height of 10 metres above the surface of the Earth. Values are derived from ERA5 near-surface wind speed. Rainfall flux kg m-2 s-1 The rate of rain that falls to the Earth's surface. Values are derived from ERA5 total precipitation and snowfall and are bias corrected primarily using precipitation data from CRU and GPCC. Rainfall flux kg m-2 s-1 The rate of rain that falls to the Earth's surface. Values are derived from ERA5 total precipitation and snowfall and are bias corrected primarily using precipitation data from CRU and GPCC. Snowfall flux kg m-2 s-1 The rate of snow that falls to the Earth's surface. Values are derived from ERA5 total precipitation and snowfall and are bias corrected primarily using precipitation data from CRU and GPCC. Snowfall flux kg m-2 s-1 The rate of snow that falls to the Earth's surface. Values are derived from ERA5 total precipitation and snowfall and are bias corrected primarily using precipitation data from CRU and GPCC. Surface air pressure Pa The pressure (force per unit area) of the atmosphere at the surface of land, sea and inland water. Values are derived from ERA5 surface air pressure with an elevation correction. Surface air pressure Pa The pressure (force per unit area) of the atmosphere at the surface of land, sea and inland water. Values are derived from ERA5 surface air pressure with an elevation correction. Surface downwelling longwave radiation W m-2 The amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. Values are derived from ERA5 surface downwelling longwave radiation with an elevation correction. Surface downwelling longwave radiation W m-2 The amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. Values are derived from ERA5 surface downwelling longwave radiation with an elevation correction. Surface downwelling shortwave radiation W m-2 The amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Values are derived from ERA5 surface downwelling shortwave radiation and bias corrected using CRU cloud cover and effects of inter-annual changes in atmospheric aerosol loading. Surface downwelling shortwave radiation W m-2 The amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Values are derived from ERA5 surface downwelling shortwave radiation and bias corrected using CRU cloud cover and effects of inter-annual changes in atmospheric aerosol loading. 131 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-era5-complete https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-complete reanalysis-era5-complete ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate covering the period from January 1940 to present1. It is produced by the Copernicus Climate Change Service (C3S) at ECMWF and provides hourly estimates of a large number of atmospheric, land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes an ensemble component at half the resolution to provide information on synoptic uncertainty of its products. ERA5.1 is a dedicated product with the same horizontal and vertical resolution that was produced for the years 2000 to 2006 inclusive to significantly improve a discontinuity in global-mean temperature in the stratosphere and uppermost troposphere that ERA5 suffers from during that period. Users that are interested in this part of the atmosphere in this era are advised to access ERA5.1 rather than ERA5. ERA5 and ERA5.1 use a state-of-the-art numerical weather prediction model to assimilate a variety of observations, including satellite and ground-based measurements, and produces a comprehensive and consistent view of the Earth's atmosphere. These products are widely used by researchers and practitioners in various fields, including climate science, weather forecasting, energy production and machine learning among others, to understand and analyse past and current weather and climate conditions. DATA DESCRIPTION Data type Gridded Projection Atmosphere and land variables are on a reduced Gaussian grid of N320 (about 31km) for ERA5 and N160 (about 63km) for the ERA5 ensemble component Ocean wave quantities are on a reduced latitude/longitude grid with a resolution of 0.36 degrees and 1.0 degree for ERA5 and its ensemble component, respectively Horizontal coverage Global: Horizontal resolution 31km x 31km for ERA5 atmosphere and land quantities (N320) 63km x 63km for ERA5 ensemble atmosphere and land quantities (N160) 0.5 x 0.5 degrees for ERA5 wave variables 1.0 x 1.0 degrees for ERA5 ensemble wave variables Vertical coverage Near surface or one level for single level variables. From surface to about 80km for model level variables From 1000 hPa to 1 hPa for pressure level variables Vertical resolution 137 model levels from 80km to the surface 37 pressure levels from 1000 to 1 hPa 16 potential temperature levels from 265 K to 850 K 1 potential vorticity level at 2 PVU Temporal coverage ERA5: January 1940 to present ERA5.1: January 2000 to December 2006 Temporal resolution All ERA5 variables are availabe hourly All ERA5 ensemble variables are availabe 3-hourly File format part GRIB-1, part GRIB2 Update frequency ERA5: Daily ERA5.1: None DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Atmosphere and land variables are on a reduced Gaussian grid of N320 (about 31km) for ERA5 and N160 (about 63km) for the ERA5 ensemble component Ocean wave quantities are on a reduced latitude/longitude grid with a resolution of 0.36 degrees and 1.0 degree for ERA5 and its ensemble component, respectively Projection Atmosphere and land variables are on a reduced Gaussian grid of N320 (about 31km) for ERA5 and N160 (about 63km) for the ERA5 ensemble component Ocean wave quantities are on a reduced latitude/longitude grid with a resolution of 0.36 degrees and 1.0 degree for ERA5 and its ensemble component, respectively Atmosphere and land variables are on a reduced Gaussian grid of N320 (about 31km) for ERA5 and N160 (about 63km) for the ERA5 ensemble component Ocean wave quantities are on a reduced latitude/longitude grid with a resolution of 0.36 degrees and 1.0 degree for ERA5 and its ensemble component, respectively Horizontal coverage Global: Horizontal coverage Global: Horizontal resolution 31km x 31km for ERA5 atmosphere and land quantities (N320) 63km x 63km for ERA5 ensemble atmosphere and land quantities (N160) 0.5 x 0.5 degrees for ERA5 wave variables 1.0 x 1.0 degrees for ERA5 ensemble wave variables Horizontal resolution 31km x 31km for ERA5 atmosphere and land quantities (N320) 63km x 63km for ERA5 ensemble atmosphere and land quantities (N160) 0.5 x 0.5 degrees for ERA5 wave variables 1.0 x 1.0 degrees for ERA5 ensemble wave variables 31km x 31km for ERA5 atmosphere and land quantities (N320) 63km x 63km for ERA5 ensemble atmosphere and land quantities (N160) 0.5 x 0.5 degrees for ERA5 wave variables 1.0 x 1.0 degrees for ERA5 ensemble wave variables Vertical coverage Near surface or one level for single level variables. From surface to about 80km for model level variables From 1000 hPa to 1 hPa for pressure level variables Vertical coverage Near surface or one level for single level variables. From surface to about 80km for model level variables From 1000 hPa to 1 hPa for pressure level variables Near surface or one level for single level variables. From surface to about 80km for model level variables From 1000 hPa to 1 hPa for pressure level variables Vertical resolution 137 model levels from 80km to the surface 37 pressure levels from 1000 to 1 hPa 16 potential temperature levels from 265 K to 850 K 1 potential vorticity level at 2 PVU Vertical resolution 137 model levels from 80km to the surface 37 pressure levels from 1000 to 1 hPa 16 potential temperature levels from 265 K to 850 K 1 potential vorticity level at 2 PVU 137 model levels from 80km to the surface 37 pressure levels from 1000 to 1 hPa 16 potential temperature levels from 265 K to 850 K 1 potential vorticity level at 2 PVU Temporal coverage ERA5: January 1940 to present ERA5.1: January 2000 to December 2006 Temporal coverage ERA5: January 1940 to present ERA5.1: January 2000 to December 2006 ERA5: January 1940 to present ERA5.1: January 2000 to December 2006 Temporal resolution All ERA5 variables are availabe hourly All ERA5 ensemble variables are availabe 3-hourly Temporal resolution All ERA5 variables are availabe hourly All ERA5 ensemble variables are availabe 3-hourly All ERA5 variables are availabe hourly All ERA5 ensemble variables are availabe 3-hourly File format part GRIB-1, part GRIB2 File format part GRIB-1, part GRIB2 Update frequency ERA5: Daily ERA5.1: None Update frequency ERA5: Daily ERA5.1: None ERA5: Daily ERA5.1: None 132 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-surface-temperature-time-series-and-trend http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BLKSEA_OMI_TEMPSAL_sst_area_averaged_anomalies Black Sea Surface Temperature time series and trend from Observations Reprocessing "''DEFINITION The blksea_omi_tempsal_sst_area_averaged_anomalies product for 2021 includes unfiltered Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1993 and averaged over the Black Sea, and 24-month filtered SST anomalies, obtained by using the X11-seasonal adjustment procedure. This OMI is derived from the CMEMS Reprocessed Black Sea L4 SST satellite product (SST_BS_SST_L4_REP_OBSERVATIONS_010_022, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf), which provided the SSTs used to compute the evolution of SST anomalies (unfiltered and filtered) over the Black Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05° grid resolution SST maps over the Black Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Anomalies are computed against the 1993-2014 reference period. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018). http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf CONTEXT Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). In the last decades, since the availability of satellite data (beginning of 1980s), the Black Sea has experienced a warming trend in SST (see e.g. Buongiorno Nardelli et al., 2010; Mulet et al., 2018). CMEMS KEY FINDINGS This year clearly evidences a decrease in temperature anomalies with respect to the recent past. The highest temperature anomaly (~2 °C) was reached in January 2021, while the lowest (~-0.27 °C) in May. This year shows a marked change in temperature anomalies after three consecutive years (2018-2020) characterized by peaks of ~3 °C occurred in May 2018, June 2019, and October 2020. Over the period 1993-2021, the Black Sea SST has warmed at a rate of 0.072 ± 0.004 °C/year, which corresponds to an average increase of about 2 °C during these last 29 years. The picture of the trend seems to reveal a switch to a decreasing tendency. DOI (product):https://doi.org/10.48670/moi-00217 https://doi.org/10.48670/moi-00217 133 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-climate-mediterranean-vectors https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-climate-mediterranean-vectors app-climate-mediterranean-vectors The Mediterranean region is densely populated and is susceptible to the transmission of human disease through air-borne vectors, such as the tiger mosquito (Aedes albopictus). The current changes to the climate have already seen an expansion of the area classified as climatically-suitable for the tiger mosquito, as well as changes to the length of the mosquito's breeding season around the Mediterranean. For example, highland regions are now The climatic suitability of air-borne vectors is predicted to further change under future climate change scenarios in many parts of the Mediterranean basin. This may lead to increased transmission of disease as well as the introduction of new diseases to the area. The tiger mosquito transmits diseases such as dengue and chikungunya. The application uses climate variables such as rainfall and temperature to measure the climatic suitability and season length for the establishment of tiger mosquito. Clicking on a point on the map, or selecting a city in the search bar, will produce a mosquito-stripes bar-chart for any location within the Mediterranean bioclimatic region. The colour of the bars represent the relative change of the suitability index from the historical reference period, the actual index is for each year is visible by hovering of the bar chart. While the height of the bar represents the season length for mosquito activity in units of weeks. The application was developed by the Copernicus Climate Change Service in partnership with the Union for the Mediterranean. INPUT VARIABLES Name Units Description Source Season length Day Duration of Aedes albopictus presence in weeks. This is also known as the mosquito season. Outside of this period mosquitoes die off or go into diapause. Climate suitability Suitability Dimensionless Likelihood that the area has favourable environmental conditions for Aedes albopictus presence with 0 not suitable (no favourable conditions) and 100 totally suitable. Climate suitability INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Season length Day Duration of Aedes albopictus presence in weeks. This is also known as the mosquito season. Outside of this period mosquitoes die off or go into diapause. Climate suitability Season length Day Duration of Aedes albopictus presence in weeks. This is also known as the mosquito season. Outside of this period mosquitoes die off or go into diapause. Climate suitability Climate suitability Suitability Dimensionless Likelihood that the area has favourable environmental conditions for Aedes albopictus presence with 0 not suitable (no favourable conditions) and 100 totally suitable. Climate suitability Suitability Dimensionless Likelihood that the area has favourable environmental conditions for Aedes albopictus presence with 0 not suitable (no favourable conditions) and 100 totally suitable. Climate suitability Climate suitability OUTPUT VARIABLES Name Units Description Active season length Day Equivalent to input variable 'season length'. Output may be displayed as 'absolute values' or the 'relative change to the historical period' (future absolute value - historical absolute value). Climate suitability for mosquitoes Dimensionless Likelihood that the area has favourable environmental conditions for Aedes albopictus presence with 0 not suitable (no favourable conditions) and 100 totally suitable. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Active season length Day Equivalent to input variable 'season length'. Output may be displayed as 'absolute values' or the 'relative change to the historical period' (future absolute value - historical absolute value). Active season length Day Equivalent to input variable 'season length'. Output may be displayed as 'absolute values' or the 'relative change to the historical period' (future absolute value - historical absolute value). Climate suitability for mosquitoes Dimensionless Likelihood that the area has favourable environmental conditions for Aedes albopictus presence with 0 not suitable (no favourable conditions) and 100 totally suitable. Climate suitability for mosquitoes Dimensionless Likelihood that the area has favourable environmental conditions for Aedes albopictus presence with 0 not suitable (no favourable conditions) and 100 totally suitable. 134 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-sea-surface-temperature https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-surface-temperature satellite-sea-surface-temperature This dataset provides daily estimates of global sea surface temperature (SST) based on observations from multiple satellite sensors since September 1981. SST is known to be a significant driver of global weather and climate patterns and to play important roles in the exchanges of energy, momentum, moisture and gases between the ocean and atmosphere. As such, its knowledge is essential to understand and assess variability and long-term changes in the Earth’s climate. The SST data provided here are based on measurements carried out by the following infrared sensors flown onboard multiple polar-orbiting satellites: the series of Advanced Very High Resolution Radiometers (AVHRRs), the series of Along Track Scanning Radiometers (ATSRs), and the Sea and Land Surface Temperature Radiometer (SLSTR). The dataset provides SST products of different processing levels. Only Level-3 Collated and Level-4 and served through this entry in the Catalogue. Due to the large number of files at Level-2 Pre-processed and Level-3 Collated these products are served through the Climate Data Store API. For more information on how to access these levels consult the documentation. The four types of products are: Level-2 Pre-processed (L2P): SST data on the native satellite swath grid and derived from single-sensor measurements. Level-3 Uncollated (L3U): SST product generated by regridding L2P data onto a global latitude-longitude grid. Level-3 Collated (L3C): global daily (day and night) single-sensor SST product based on collated L3U data. Level-4 (L4): spatially complete global SST product based on data from multiple sensors. Level-2 Pre-processed (L2P): SST data on the native satellite swath grid and derived from single-sensor measurements. Level-3 Uncollated (L3U): SST product generated by regridding L2P data onto a global latitude-longitude grid. Level-3 Collated (L3C): global daily (day and night) single-sensor SST product based on collated L3U data. Level-4 (L4): spatially complete global SST product based on data from multiple sensors. These products are available as Climate Data Records (CDRs), which have sufficient length, consistency, and continuity to be used to assess climate variability and changes. These SST CDRs are identical to those produced as part of the European Space Agency (ESA) SST Climate Change Initiative (CCI) project. Interim CDRs (ICDRs) are produced at levels L3C and L4 on behalf of the Copernicus Climate Change Service (C3S) to extend the baseline CDRs. Both SST CDRs and ICDRs are generated using software and algorithms developed as part of the ESA SST CCI. Users should use the most recent version of the dataset whenever possible. Data from the previous version are also made available but cover shorter periods. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global ocean for Level-3 and Level-4 products. Satellite swath for Level-2P products. Horizontal resolution 1km x 1km for ATSR and SLSTR Level-2P products. ~4km x 4km for AVHRR Level-2P products. 0.05° x 0.05° for Level-3 and Level-4 products. Temporal coverage 1992 to 2012 for ATSR products. 2017 to present for SLSTR products. 1981 to present for AVHRR and L4 Analysis products. Temporal resolution Daily Temporal gaps Level-4: No gaps. Level-2P and Level-3: Coverage depends on individual satellite missions (see documentation); major gaps in ATSR record in Jan-June 1996 and Jan-June 2001. File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.5, Unidata Observation Dataset v1.0. Versions v2.1: Updated product format based on feedback from v2.0 users. v2.0: Provides data from all processing levels; covers 1981 to present (temporal coverage depends on processing level). v1.1: Provides Level-4 data only; covers September 1991 to December 2010. Update frequency Daily with a latency of about one month behind real time. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global ocean for Level-3 and Level-4 products. Satellite swath for Level-2P products. Horizontal coverage Global ocean for Level-3 and Level-4 products. Satellite swath for Level-2P products. Horizontal resolution 1km x 1km for ATSR and SLSTR Level-2P products. ~4km x 4km for AVHRR Level-2P products. 0.05° x 0.05° for Level-3 and Level-4 products. Horizontal resolution 1km x 1km for ATSR and SLSTR Level-2P products. ~4km x 4km for AVHRR Level-2P products. 0.05° x 0.05° for Level-3 and Level-4 products. Temporal coverage 1992 to 2012 for ATSR products. 2017 to present for SLSTR products. 1981 to present for AVHRR and L4 Analysis products. Temporal coverage 1992 to 2012 for ATSR products. 2017 to present for SLSTR products. 1981 to present for AVHRR and L4 Analysis products. Temporal resolution Daily Temporal resolution Daily Temporal gaps Level-4: No gaps. Level-2P and Level-3: Coverage depends on individual satellite missions (see documentation); major gaps in ATSR record in Jan-June 1996 and Jan-June 2001. Temporal gaps Level-4: No gaps. Level-2P and Level-3: Coverage depends on individual satellite missions (see documentation); major gaps in ATSR record in Jan-June 1996 and Jan-June 2001. File format NetCDF 4 File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.5, Unidata Observation Dataset v1.0. Conventions Climate and Forecast (CF) Metadata Convention v1.5, Unidata Observation Dataset v1.0. Versions v2.1: Updated product format based on feedback from v2.0 users. v2.0: Provides data from all processing levels; covers 1981 to present (temporal coverage depends on processing level). v1.1: Provides Level-4 data only; covers September 1991 to December 2010. Versions v2.1: Updated product format based on feedback from v2.0 users. v2.0: Provides data from all processing levels; covers 1981 to present (temporal coverage depends on processing level). v1.1: Provides Level-4 data only; covers September 1991 to December 2010. Update frequency Daily with a latency of about one month behind real time. Update frequency Daily with a latency of about one month behind real time. MAIN VARIABLES Name Units Description Analysed sea surface temperature K Spatially complete gridded estimate of daily average ocean temperature adjusted to a standard depth of 20 cm. This variable is only provided for the Level-4 product. Sea surface temperature anomaly at depth K Difference between the SST0.2m variable and a daily climatology calculated for the 1982-2010 period. This variable is only provided for Level-2 and Level-3 products from version 2.1. Sea surface temperature at depth K Ocean temperature adjusted to a standard depth of 20 cm and 10:30 local time (equivalent to daily average). This variable (noted SST0.2m) is derived from the skin SST measurements and allows comparison with in situ observations. This variable is only provided for Level-2 and Level-3 products. Skin sea surface temperature K Ocean temperature at a depth of approximately 10 μm. This is the temperature measured by satellite infrared radiometers. This variable is only provided for Level-2 and Level-3 products. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Analysed sea surface temperature K Spatially complete gridded estimate of daily average ocean temperature adjusted to a standard depth of 20 cm. This variable is only provided for the Level-4 product. Analysed sea surface temperature K Spatially complete gridded estimate of daily average ocean temperature adjusted to a standard depth of 20 cm. This variable is only provided for the Level-4 product. Sea surface temperature anomaly at depth K Difference between the SST0.2m variable and a daily climatology calculated for the 1982-2010 period. This variable is only provided for Level-2 and Level-3 products from version 2.1. Sea surface temperature anomaly at depth K Difference between the SST0.2m variable and a daily climatology calculated for the 1982-2010 period. This variable is only provided for Level-2 and Level-3 products from version 2.1. Sea surface temperature at depth K Ocean temperature adjusted to a standard depth of 20 cm and 10:30 local time (equivalent to daily average). This variable (noted SST0.2m) is derived from the skin SST measurements and allows comparison with in situ observations. This variable is only provided for Level-2 and Level-3 products. Sea surface temperature at depth K Ocean temperature adjusted to a standard depth of 20 cm and 10:30 local time (equivalent to daily average). This variable (noted SST0.2m) is derived from the skin SST measurements and allows comparison with in situ observations. This variable is only provided for Level-2 and Level-3 products. Skin sea surface temperature K Ocean temperature at a depth of approximately 10 μm. This is the temperature measured by satellite infrared radiometers. This variable is only provided for Level-2 and Level-3 products. Skin sea surface temperature K Ocean temperature at a depth of approximately 10 μm. This is the temperature measured by satellite infrared radiometers. This variable is only provided for Level-2 and Level-3 products. RELATED VARIABLES A number of variables accounting for the uncertainty of the data are also included in the files along the main variables. They provide estimates on possible variations of the values of the main variables due to changes in processing and sampling algorithms. A variable containing information on whether there is ocean, land or sea ice in each grid cell is also provided. For more information, please consult the Product User Guide. RELATED VARIABLES RELATED VARIABLES A number of variables accounting for the uncertainty of the data are also included in the files along the main variables. They provide estimates on possible variations of the values of the main variables due to changes in processing and sampling algorithms. A variable containing information on whether there is ocean, land or sea ice in each grid cell is also provided. For more information, please consult the Product User Guide. A number of variables accounting for the uncertainty of the data are also included in the files along the main variables. They provide estimates on possible variations of the values of the main variables due to changes in processing and sampling algorithms. A variable containing information on whether there is ocean, land or sea ice in each grid cell is also provided. For more information, please consult the Product User Guide. 135 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/european-north-west-shelf-sea-surface-temperature-time http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=NORTHWESTSHELF_OMI_TEMPSAL_sst_area_averaged_anomalies European North West Shelf Sea Surface Temperature time series and trend from Observations Reprocessing DEFINITION The northwestshelf_omi_tempsal_sst_area_averaged_anomalies product for 2021 includes Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1993 and averaged over the European North West Shelf Seas. The NORTHWESTSHELF SST OMI is built from the CMEMS Reprocessed European North West Shelf Iberai-Biscay-Irish Seas (SST_MED_SST_L4_REP_OBSERVATIONS_010_026, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf), which provided the SSTs used to compute the evolution of SST anomalies over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05° grid resolution SST maps over the European North West Shelf Iberai-Biscay-Irish Seas built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Anomalies are computed against the 1993-2014 reference period. http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf CONTEXT Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). CMEMS KEY FINDINGS The overall trend in the SST anomalies in this region is 0.001 ±0.001 °C/year over the period 1993-2021. DOI (product):https://doi.org/10.48670/moi-00275 https://doi.org/10.48670/moi-00275 136 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-2018-raster-100-m-europe-6-yearly https://land.copernicus.eu/pan-european/corine-land-cover/clc2018 CORINE Land Cover 2018 (raster 100 m), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover 2018 (CLC2018) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2018. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 137 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-c3s-monthly-climate-bulletin-explorer https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-c3s-monthly-climate-bulletin-explorer app-c3s-monthly-climate-bulletin-explorer This application delivers an interactive version of the monthly C3S Climate bulletin. It allows users to explore the dataset on the aggregated regions - the globe, Europe and Europe's macro-regions - that are covered in the bulletin and on individual European countries. It presents a global map showing one out of temperature, precipitation, humidity and soil moisture anomalies with respect to a selectable 30-year reference period, averaged over user-selectable periods. When clicking on one of the highlighted regions, time-series of the anomalies of all mentioned variables averaged over the selected region are shown. Climate bulletin The maps and graphs generated are based on theEssential Climate Variables for assessment of climate variability from 1979 to present dataset. Essential Climate Variables for assessment of climate variability from 1979 to present dataset List of user-selectable parameters List of user-selectable parameters Reference period: "1981 - 2010" or "1991 - 2020" Time aggregation: one out of "Month", "Season" and "12 months", which define the averaging period of the anomalies Year: 1979 to present; the list of available options is updated annually Month/Season: this drop-down menu changes depending on the choice in the Time aggregation menu; if Month (Season), then it shows from January (Spring) to the last available month (season) in the selected Year; the list of available options is updated monthly Regions: one between "Macro-regions" and "Countries (NUTS level 0)" Reference period: "1981 - 2010" or "1991 - 2020" Reference period Time aggregation: one out of "Month", "Season" and "12 months", which define the averaging period of the anomalies Time aggregation Year: 1979 to present; the list of available options is updated annually Year Month/Season: this drop-down menu changes depending on the choice in the Time aggregation menu; if Month (Season), then it shows from January (Spring) to the last available month (season) in the selected Year; the list of available options is updated monthly Month/Season Time aggregation Month Season Year Regions: one between "Macro-regions" and "Countries (NUTS level 0)" Regions Description of the graphical output Description of the graphical output The application presents a map of the globe with average anomalies of the variable selected in the top-right corner radio-buttons menu, displayed for the chosen year and month/season. On the map, selectable regions are highlighted by thicker borders: clicking on one of those, a sidebar pops up showing time-series of the average anomalies aggregated on the selected region. Here, the "Show only selected period" box can be checked to show only the selected month/season in the time-series. The layer of selectable regions can be changed through the "Regions" dropdown menu. If "Macro-regions" is selected, zooming out the selectable regions "Europe" and "Globe" are shown. More details about the products are given in the Documentation section. INPUT VARIABLES Name Units Description Source 0-7 cm volumetric soil moisture anomaly 12-month average m³ m⁻³ 12 months running mean anomalies of the 0-7 cm volumetric soil moisture with respect to 1981-2010 climatology ECVs for climate change 0-7 cm volumetric soil moisture anomaly monthly m³ m⁻³ Monthly anomalies of the 0-7 cm volumetric soil moisture with respect to 1981-2010 climatology ECVs for climate change Precipitation anomaly 12-month average m day⁻¹ 12 months running mean anomalies of the precipitation with respect to 1981-2010 climatology ECVs for climate change Precipitation anomaly monthly m day⁻¹ Monthly anomalies of the precipitation with respect to 1981-2010 climatology ECVs for climate change Surface air relative humidity anomaly 12-month average % 12 months running mean anomalies of the surface air relative humidity with respect to 1981-2010 climatology ECVs for climate change Surface air relative humidity anomaly monthly % Monthly anomalies of the surface air relative humidity with respect to 1981-2010 climatology ECVs for climate change Surface air temperature anomaly 12-month average K 12 months running mean anomalies of the air temperature at surface with respect to 1981-2010 climatology ECVs for climate change Surface air temperature anomaly monthly K Monthly anomalies of the air temperature at surface with respect to 1981-2010 climatology ECVs for climate change INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 0-7 cm volumetric soil moisture anomaly 12-month average m³ m⁻³ 12 months running mean anomalies of the 0-7 cm volumetric soil moisture with respect to 1981-2010 climatology ECVs for climate change 0-7 cm volumetric soil moisture anomaly 12-month average m³ m⁻³ 12 months running mean anomalies of the 0-7 cm volumetric soil moisture with respect to 1981-2010 climatology ECVs for climate change ECVs for climate change 0-7 cm volumetric soil moisture anomaly monthly m³ m⁻³ Monthly anomalies of the 0-7 cm volumetric soil moisture with respect to 1981-2010 climatology ECVs for climate change 0-7 cm volumetric soil moisture anomaly monthly m³ m⁻³ Monthly anomalies of the 0-7 cm volumetric soil moisture with respect to 1981-2010 climatology ECVs for climate change ECVs for climate change Precipitation anomaly 12-month average m day⁻¹ 12 months running mean anomalies of the precipitation with respect to 1981-2010 climatology ECVs for climate change Precipitation anomaly 12-month average m day⁻¹ 12 months running mean anomalies of the precipitation with respect to 1981-2010 climatology ECVs for climate change ECVs for climate change Precipitation anomaly monthly m day⁻¹ Monthly anomalies of the precipitation with respect to 1981-2010 climatology ECVs for climate change Precipitation anomaly monthly m day⁻¹ Monthly anomalies of the precipitation with respect to 1981-2010 climatology ECVs for climate change ECVs for climate change Surface air relative humidity anomaly 12-month average % 12 months running mean anomalies of the surface air relative humidity with respect to 1981-2010 climatology ECVs for climate change Surface air relative humidity anomaly 12-month average % 12 months running mean anomalies of the surface air relative humidity with respect to 1981-2010 climatology ECVs for climate change ECVs for climate change Surface air relative humidity anomaly monthly % Monthly anomalies of the surface air relative humidity with respect to 1981-2010 climatology ECVs for climate change Surface air relative humidity anomaly monthly % Monthly anomalies of the surface air relative humidity with respect to 1981-2010 climatology ECVs for climate change ECVs for climate change Surface air temperature anomaly 12-month average K 12 months running mean anomalies of the air temperature at surface with respect to 1981-2010 climatology ECVs for climate change Surface air temperature anomaly 12-month average K 12 months running mean anomalies of the air temperature at surface with respect to 1981-2010 climatology ECVs for climate change ECVs for climate change Surface air temperature anomaly monthly K Monthly anomalies of the air temperature at surface with respect to 1981-2010 climatology ECVs for climate change Surface air temperature anomaly monthly K Monthly anomalies of the air temperature at surface with respect to 1981-2010 climatology ECVs for climate change ECVs for climate change OUTPUT VARIABLES Name Units Description Aggregated 0-7 cm volumetric soil moisture anomaly 12-month average % 12 months running mean anomalies of the 0-7 cm volumetric soil moisture with respect to 1981-2010 climatology averaged over the selected region Aggregated 0-7 cm volumetric soil moisture anomaly monthly % Monthly anomalies of the 0-7 cm volumetric soil moisture with respect to 1981-2010 climatology averaged over the selected region Aggregated precipitation anomaly 12-month average mm day⁻¹ 12 months running mean anomalies of the precipitation with respect to 1981-2010 climatology averaged over the selected region Aggregated precipitation anomaly monthly mm day⁻¹ Monthly anomalies of the precipitation with respect to 1981-2010 climatology averaged over the selected region Aggregated surface air relative humidity 12-month average % 12 months running mean anomalies of the surface air relative humidity with respect to 1981-2010 climatology averaged over the selected region Aggregated surface air relative humidity anomaly monthly % Monthly anomalies of the surface air relative humidity with respect to 1981-2010 climatology averaged over the selected region Aggregated surface air temperature anomaly 12-month average °C 12 months running mean anomalies of the air temperature at surface with respect to 1981-2010 climatology averaged over the selected region Aggregated surface air temperature anomaly monthly °C Monthly anomalies of the air temperature at surface with respect to 1981-2010 climatology averaged over the selected region OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Aggregated 0-7 cm volumetric soil moisture anomaly 12-month average % 12 months running mean anomalies of the 0-7 cm volumetric soil moisture with respect to 1981-2010 climatology averaged over the selected region Aggregated 0-7 cm volumetric soil moisture anomaly 12-month average % 12 months running mean anomalies of the 0-7 cm volumetric soil moisture with respect to 1981-2010 climatology averaged over the selected region Aggregated 0-7 cm volumetric soil moisture anomaly monthly % Monthly anomalies of the 0-7 cm volumetric soil moisture with respect to 1981-2010 climatology averaged over the selected region Aggregated 0-7 cm volumetric soil moisture anomaly monthly % Monthly anomalies of the 0-7 cm volumetric soil moisture with respect to 1981-2010 climatology averaged over the selected region Aggregated precipitation anomaly 12-month average mm day⁻¹ 12 months running mean anomalies of the precipitation with respect to 1981-2010 climatology averaged over the selected region Aggregated precipitation anomaly 12-month average mm day⁻¹ 12 months running mean anomalies of the precipitation with respect to 1981-2010 climatology averaged over the selected region Aggregated precipitation anomaly monthly mm day⁻¹ Monthly anomalies of the precipitation with respect to 1981-2010 climatology averaged over the selected region Aggregated precipitation anomaly monthly mm day⁻¹ Monthly anomalies of the precipitation with respect to 1981-2010 climatology averaged over the selected region Aggregated surface air relative humidity 12-month average % 12 months running mean anomalies of the surface air relative humidity with respect to 1981-2010 climatology averaged over the selected region Aggregated surface air relative humidity 12-month average % 12 months running mean anomalies of the surface air relative humidity with respect to 1981-2010 climatology averaged over the selected region Aggregated surface air relative humidity anomaly monthly % Monthly anomalies of the surface air relative humidity with respect to 1981-2010 climatology averaged over the selected region Aggregated surface air relative humidity anomaly monthly % Monthly anomalies of the surface air relative humidity with respect to 1981-2010 climatology averaged over the selected region Aggregated surface air temperature anomaly 12-month average °C 12 months running mean anomalies of the air temperature at surface with respect to 1981-2010 climatology averaged over the selected region Aggregated surface air temperature anomaly 12-month average °C 12 months running mean anomalies of the air temperature at surface with respect to 1981-2010 climatology averaged over the selected region Aggregated surface air temperature anomaly monthly °C Monthly anomalies of the air temperature at surface with respect to 1981-2010 climatology averaged over the selected region Aggregated surface air temperature anomaly monthly °C Monthly anomalies of the air temperature at surface with respect to 1981-2010 climatology averaged over the selected region 138 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/projections-cmip5-daily-pressure-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip5-daily-pressure-levels projections-cmip5-daily-pressure-levels This catalogue entry provides daily climate projections on pressure levels from a large number of experiments, models, members and time periods computed in the framework of fifth phase of the Coupled Model Intercomparison Project (CMIP5). The term "pressure levels" is used to express that the variables were computed at multiple vertical levels, which may differ in number and location among the different models. CMIP5 data are used extensively in the Intergovernmental Panel on Climate Change Assessment Reports (the latest one is IPCC AR5, which was published in 2014). The use of these data is mostly aimed at: addressing outstanding scientific questions that arose as part of the IPCC reporting process; improving the understanding of the climate system; providing estimates of future climate change and related uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different models are in simulating the recent past. addressing outstanding scientific questions that arose as part of the IPCC reporting process; improving the understanding of the climate system; providing estimates of future climate change and related uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different models are in simulating the recent past. The term "experiments" refers to the three main categories of CMIP5 simulations: Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2005.; Ensemble of experiments from the Atmospheric Model Intercomparison Project (AMIP), which prescribes the oceanic variables for all models and during all period of the experiment. This configuration removes the added complexity of ocean-atmosphere feedbacks in the climate system. The period covered is typically 1950-2005. Ensemble of climate projection experiments following the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. The RCP scenarios provide different pathways of the future climate forcing. The period covered is typically 2006-2100, some extended RCP experimental data is available from 2100-2300. Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2005.; Ensemble of experiments from the Atmospheric Model Intercomparison Project (AMIP), which prescribes the oceanic variables for all models and during all period of the experiment. This configuration removes the added complexity of ocean-atmosphere feedbacks in the climate system. The period covered is typically 1950-2005. Ensemble of climate projection experiments following the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. The RCP scenarios provide different pathways of the future climate forcing. The period covered is typically 2006-2100, some extended RCP experimental data is available from 2100-2300. In CMIP5, the same experiments were run using different GCMs. In addition, for each model, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. Note that CMIP5 GCM data can be also used as lateral boundary conditions for Regional Climate Models (RCMs). RCMs are also available in the CDS (see CORDEX datasets). The data are produced by the participating institutes of the CMIP5 project. The latest CMIP GCM experiments will form the CMIP6 dataset, which will be published in the CDS in a later stage. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution From 0.125° x 0.125° to 5° x 5° depending on the model Vertical resolution Levels are tipically at 10, 50, 100, 250, 500, 700, 850 and 1000 hPa depending on the model Temporal coverage From 1850 to 2300 (shorter for some experiments) Temporal resolution Day File format NetCDF DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution From 0.125° x 0.125° to 5° x 5° depending on the model Horizontal resolution From 0.125° x 0.125° to 5° x 5° depending on the model Vertical resolution Levels are tipically at 10, 50, 100, 250, 500, 700, 850 and 1000 hPa depending on the model Vertical resolution Levels are tipically at 10, 50, 100, 250, 500, 700, 850 and 1000 hPa depending on the model Temporal coverage From 1850 to 2300 (shorter for some experiments) Temporal coverage From 1850 to 2300 (shorter for some experiments) Temporal resolution Day Temporal resolution Day File format NetCDF File format NetCDF MAIN VARIABLES Name Units Description Geopotential height m Gravitational potential energy per unit mass normalised by the standard gravity at mean sea level at the same latitude. It is also used as vertical coordinate referenced to Earth's mean sea level since its value is proportional to the elevation above the mean sea level. Temperature K Temperature of the air. U-component of wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity. V-component of wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Geopotential height m Gravitational potential energy per unit mass normalised by the standard gravity at mean sea level at the same latitude. It is also used as vertical coordinate referenced to Earth's mean sea level since its value is proportional to the elevation above the mean sea level. Geopotential height m Gravitational potential energy per unit mass normalised by the standard gravity at mean sea level at the same latitude. It is also used as vertical coordinate referenced to Earth's mean sea level since its value is proportional to the elevation above the mean sea level. Temperature K Temperature of the air. Temperature K Temperature of the air. U-component of wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity. U-component of wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity. V-component of wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity. V-component of wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity. 139 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/south-pacific-gyre-area-chlorophyll-time-series-and-trend http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_spg_area_mean South Pacific Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing DEFINITION Oligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021). CONTEXT Oligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth’s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. CMEMS KEY FINDINGS The trend in the South Pacific gyre area for the 1997 Sept – 2021 December period was positive, with a 0.04% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 0.16% change for the 1997-2020 period, with the sign of the trend remaining unchanged and is not statistically significant (p<0.05). An underlying low frequency signal is observed with a period of approximately a decade. During the 1997 Sept – 2021 December period, the trend in chlorophyll concentration was positive (0.66% year-1) in the South Pacific gyre relative to 2000-01-01 values. This rate has increased compared to the rate of 0.45% year-1 for the 1997-2020 period and remains statistically significant (p<0.05). In the last two years of the timeseries, an increase in the variation from the mean is observed. For 2016, the Ocean State Report (Sathyendranath et al. 2018) reported a large increase in gyre area in the Pacific Ocean (both North and South Pacific gyres), probably linked with the 2016 ENSO event which saw large decreases in chlorophyll in the Pacific Ocean. DOI (product):https://doi.org/10.48670/moi-00229 https://doi.org/10.48670/moi-00229 140 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-gridded-l4-sea-surface-heights-and-derived-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057 GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE) Short description: DUACS delayed-time altimeter gridded maps of sea surface heights and derived variables over the global Ocean (https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-…). The processing focuses on the stability and homogeneity of the sea level record (based on a stable two-satellite constellation) and the product is dedicated to the monitoring of the sea level long-term evolution for climate applications and the analysis of Ocean/Climate indicators. These products are produced and distributed by the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/). https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-… https://climate.copernicus.eu/ DOI (product):https://doi.org/10.48670/moi-00145 https://doi.org/10.48670/moi-00145 141 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-lake-water-temperature https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-lake-water-temperature satellite-lake-water-temperature This dataset provides mid-morning daily values for lake surface water temperature (LSWT) retrieved from satellite over a regular grid together with associated LSWT uncertainty and quality levels based on statistical confidence levels. The data represent current state-of-the-art for LSWT record production. The satellite data are from the Along Track Scanning Radiometer (ATSR), Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea and Land Surface Temperature Radiometer (SLSTR) sensors. The retrieved temperatures have been bias adjusted for consistency between sensors using overlap periods. The dataset contains only observations and therefore gaps in time and space may be present. LSWT is a recognised Essential Climate Variable (ECV) of the Global Climate Observing System (GCOS). It is one of the key parameters determining the ecological conditions within a lake, since it influences physical, chemical and biological processes. Lake water temperatures also determine air-water heat and moisture exchange and therefore they are important for understanding the hydrological cycle. This dataset supports the investigation of large-scale lake-climate interaction on inter-annual time scales. This dataset is produced on behalf of the Copernicus Climate Change Service (C3S). DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage The dataset covers 2000 lakes distributed globally (listed in the Product User Guide). Horizontal resolution 0.05° x 0.05° Vertical coverage Lake surface Vertical resolution Single surface layer Temporal coverage June 1995 to September 2022 Temporal resolution Daily (with gaps) File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 4.0 (June 1995 to August 2019) 4.2 (September 2019 to October 2020) 4.5 (June 1995 to September 2022) Update frequency Annually DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage The dataset covers 2000 lakes distributed globally (listed in the Product User Guide). Horizontal coverage The dataset covers 2000 lakes distributed globally (listed in the Product User Guide). Horizontal resolution 0.05° x 0.05° Horizontal resolution 0.05° x 0.05° Vertical coverage Lake surface Vertical coverage Lake surface Vertical resolution Single surface layer Vertical resolution Single surface layer Temporal coverage June 1995 to September 2022 Temporal coverage June 1995 to September 2022 Temporal resolution Daily (with gaps) Temporal resolution Daily (with gaps) File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 4.0 (June 1995 to August 2019) 4.2 (September 2019 to October 2020) 4.5 (June 1995 to September 2022) Versions 4.0 (June 1995 to August 2019) 4.2 (September 2019 to October 2020) 4.5 (June 1995 to September 2022) 4.0 (June 1995 to August 2019) 4.2 (September 2019 to October 2020) 4.5 (June 1995 to September 2022) Update frequency Annually Update frequency Annually MAIN VARIABLES Name Units Description Lake surface water temperature K Daily gridded temperature of the water at the surface of the inland water body. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Lake surface water temperature K Daily gridded temperature of the water at the surface of the inland water body. Lake surface water temperature K Daily gridded temperature of the water at the surface of the inland water body. RELATED VARIABLES Name Units Description Bias correction flag Dimensionless Flag indicating whether there has been an adjustment to a reference sensor applied (ATSR2, AATSR, ATSR2-AATSR) Lake id Dimensionless Lake identification number, where applicable, of the water body in the Global Lakes and Wetlands Database Observation instruments Dimensionless Flag indicating the instrument used (ATSR2, MODIS, AATSR, AVHRRA, AVHRRB, SLSTRA, SLSTRB and combinations) Quality levels Dimensionless Quality level expressing the confidence in the retrieval and uncertainty estimate (no data, bad data, worst quality, low quality, acceptable quality, best quality) Total uncertainty K Uncertainty attributed to the lake surface water temperature RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Bias correction flag Dimensionless Flag indicating whether there has been an adjustment to a reference sensor applied (ATSR2, AATSR, ATSR2-AATSR) Bias correction flag Dimensionless Flag indicating whether there has been an adjustment to a reference sensor applied (ATSR2, AATSR, ATSR2-AATSR) Lake id Dimensionless Lake identification number, where applicable, of the water body in the Global Lakes and Wetlands Database Lake id Dimensionless Lake identification number, where applicable, of the water body in the Global Lakes and Wetlands Database Observation instruments Dimensionless Flag indicating the instrument used (ATSR2, MODIS, AATSR, AVHRRA, AVHRRB, SLSTRA, SLSTRB and combinations) Observation instruments Dimensionless Flag indicating the instrument used (ATSR2, MODIS, AATSR, AVHRRA, AVHRRB, SLSTRA, SLSTRB and combinations) Quality levels Dimensionless Quality level expressing the confidence in the retrieval and uncertainty estimate (no data, bad data, worst quality, low quality, acceptable quality, best quality) Quality levels Dimensionless Quality level expressing the confidence in the retrieval and uncertainty estimate (no data, bad data, worst quality, low quality, acceptable quality, best quality) Total uncertainty K Uncertainty attributed to the lake surface water temperature Total uncertainty K Uncertainty attributed to the lake surface water temperature 142 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-2006-vector-europe-6-yearly-version https://land.copernicus.eu/pan-european/corine-land-cover/clc-2006/view CORINE Land Cover 2006 (vector), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover 2006 (CLC2006) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2006. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 143 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/medium-resolution-vegetation-phenology-and-productivity-9 https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum_d1-0.pdf Medium Resolution Vegetation Phenology and Productivity: Plant phenology index (raster 500m), Oct. 2022 This metadata refers to the Plant Phenology Index (PPI) dataset, one of the near real-time (NRT) Vegetation Index products of the pan-European Medium Resolution Vegetation Phenology and Productivity (MR-VPP), component of the Copernicus Land Monitoring Service (CLMS). The Plant Phenology Index (PPI) is a physically based vegetation index for improved monitoring of plant phenology, that is developed from a simplified solution to the radiative transfer equation by Jin and Eklundh (2014). PPI has a linear relationship with green leaf area index, a strong correlation with gross primary productivity, and is capable of disentangling remotely sensed plant phenology from snow seasonality. It is reported to be superior to other indices for spring phenology retrieval over the northern latitudes and for GPP estimation in African semi-arid ecosystems. Comparison of satellite-derived PPI to ground observations of plant phenology and gross primary productivity (GPP) shows strong similarity of temporal patterns over several Nordic boreal forest sites. Further information is available in the Product User Manual: https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… The PPI time series dataset is made available as raster files with 500 x 500m resolution, in ETRS89-LAEA projection corresponding to the MCD43 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for the period from January 2000 until today. The full on-line access to open and free data for this resource will be made available by the end of 2022. Until then the data will be made available 'on-demand' by filling in the form at: https://land.copernicus.eu/contact-form https://land.copernicus.eu/contact-form 144 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/iberia-biscay-ireland-sea-surface-temperature-trend-map http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=IBI_OMI_TEMPSAL_sst_trend Iberia Biscay Ireland Sea Surface Temperature trend map from Observations Reprocessing DEFINITION The ibi_omi_tempsal_sst_trend product includes the Sea Surface Temperature (SST) trend for the Iberia-Biscay-Irish Seas over the period 1993-2019, i.e. the rate of change (°C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf), which provided the SSTs used to compute the SST trend over the Iberia-Biscay-Irish Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05° grid resolution SST maps built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens’s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf CONTEXT Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). CMEMS KEY FINDINGS Over the period 1993-2021, the Iberia-Biscay-Irish Seas mean Sea Surface Temperature (SST) increased at a rate of 0.011 ± 0.001 °C/Year. DOI (product):https://doi.org/10.48670/moi-00257 https://doi.org/10.48670/moi-00257 145 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-thermosteric-sea-level-trend-map-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_SL_thsl_trend Global Ocean Thermosteric Sea Level trend map from Reanalysis & Multi-Observations Reprocessing DEFINITION The temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60°S-60°N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). CONTEXT Most of the interannual variability and trends in regional sea level is caused by changes in steric sea level. At mid and low latitudes, the steric sea level signal is essentially due to temperature changes, i.e. the thermosteric effect (Stammer et al., 2013, Meyssignac et al., 2016). Salinity changes play only a local role. Regional trends of thermosteric sea level can be significantly larger compared to their globally averaged versions (Storto et al., 2018). Except for shallow shelf sea and high latitudes (> 60° latitude), regional thermosteric sea level variations are mostly related to ocean circulation changes, in particular in the tropics where the sea level variations and trends are the most intense over the last two decades. CMEMS KEY FINDINGS Significant (i.e. when the signal exceeds the noise) regional trends for the period 2005-2019 from the Copernicus Marine Service multi-ensemble approach show a thermosteric sea level rise at rates ranging from the global mean average up to more than 8 mm/year. There are specific regions where a negative trend is observed above noise at rates up to about -8 mm/year such as in the subpolar North Atlantic, or the western tropical Pacific. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020). Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00241 https://doi.org/10.48670/moi-00241 146 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/projections-cmip5-monthly-pressure-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip5-monthly-pressure-levels projections-cmip5-monthly-pressure-levels This catalogue entry provides monthly climate projections on pressure levels from a large number of experiments, models, members and time periods computed in the framework of the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The term "pressure levels" is used to express that the variables were computed at multiple vertical levels, which may differ in number and location among the different models. CMIP5 data are used extensively in the Intergovernmental Panel on Climate Change Assessment Reports (the latest one is IPCC AR5, which was published in 2014). The use of these data is mostly aimed at: addressing outstanding scientific questions that arose as part of the IPCC reporting process; improving the understanding of the climate system; providing estimates of future climate change and related uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different models are in simulating the recent past. addressing outstanding scientific questions that arose as part of the IPCC reporting process; improving the understanding of the climate system; providing estimates of future climate change and related uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different models are in simulating the recent past. The term "experiments" refers to the three main categories of CMIP5 simulations: Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2005.; Ensemble of experiments from the Atmospheric Model Intercomparison Project (AMIP), which prescribes the oceanic variables for all models and during all period of the experiment. This configuration removes the added complexity of ocean-atmosphere feedbacks in the climate system. The period covered is typically 1950-2005. Ensemble of climate projection experiments following the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. The RCP scenarios provide different pathways of the future climate forcing. The period covered is typically 2006-2100, some extended RCP experimental data is available from 2100-2300. Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2005.; Ensemble of experiments from the Atmospheric Model Intercomparison Project (AMIP), which prescribes the oceanic variables for all models and during all period of the experiment. This configuration removes the added complexity of ocean-atmosphere feedbacks in the climate system. The period covered is typically 1950-2005. Ensemble of climate projection experiments following the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. The RCP scenarios provide different pathways of the future climate forcing. The period covered is typically 2006-2100, some extended RCP experimental data is available from 2100-2300. In CMIP5, the same experiments were run using different GCMs. In addition, for each model, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. Note that CMIP5 GCM data can be also used as lateral boundary conditions for Regional Climate Models (RCMs). RCMs are also available in the CDS (see CORDEX datasets). The data are produced by the participating institutes of the CMIP5 project. The latest CMIP GCM experiments will form the CMIP6 dataset, which will be published in the CDS in a later stage. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution From 0.125°x0.125° to 5°x5° depending on the model Vertical resolution Levels are tipically at 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925 and 1000 hPa depending on the model Temporal coverage From 1850 to 2300 (shorter for some experiments) Temporal resolution Month File format NetCDF DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution From 0.125°x0.125° to 5°x5° depending on the model Horizontal resolution From 0.125°x0.125° to 5°x5° depending on the model Vertical resolution Levels are tipically at 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925 and 1000 hPa depending on the model Vertical resolution Levels are tipically at 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925 and 1000 hPa depending on the model Temporal coverage From 1850 to 2300 (shorter for some experiments) Temporal coverage From 1850 to 2300 (shorter for some experiments) Temporal resolution Month Temporal resolution Month File format NetCDF File format NetCDF MAIN VARIABLES Name Units Description Geopotential height m Gravitational potential energy per unit mass normalised by the standard gravity at mean sea level at the same latitude. It is also used as vertical coordinate referenced to Earth's mean sea level since its value is proportional to the elevation above the mean sea level. Relative humidity % Amount of moisture in the air divided by the maximum amount of moisture that could exist in the air at a specific temperature and location. Specific humidity % Amount of moisture in the air divided by amount of air plus moisture at that location. Temperature K Temperature of the air. U-component of wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity. V-component of wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Geopotential height m Gravitational potential energy per unit mass normalised by the standard gravity at mean sea level at the same latitude. It is also used as vertical coordinate referenced to Earth's mean sea level since its value is proportional to the elevation above the mean sea level. Geopotential height m Gravitational potential energy per unit mass normalised by the standard gravity at mean sea level at the same latitude. It is also used as vertical coordinate referenced to Earth's mean sea level since its value is proportional to the elevation above the mean sea level. Relative humidity % Amount of moisture in the air divided by the maximum amount of moisture that could exist in the air at a specific temperature and location. Relative humidity % Amount of moisture in the air divided by the maximum amount of moisture that could exist in the air at a specific temperature and location. Specific humidity % Amount of moisture in the air divided by amount of air plus moisture at that location. Specific humidity % Amount of moisture in the air divided by amount of air plus moisture at that location. Temperature K Temperature of the air. Temperature K Temperature of the air. U-component of wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity. U-component of wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity. V-component of wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity. V-component of wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity. 147 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/derived-reanalysis-energy-moisture-budget https://cds.climate.copernicus.eu/cdsapp#!/dataset/derived-reanalysis-energy-moisture-budget derived-reanalysis-energy-moisture-budget This dataset provides monthly means of mass-consistent, vertically integrated, atmospheric energy and moisture budget quantities derived from 1-hourly ERA5 reanalysis data. The vertically integrated budget diagnostics include the tendencies and lateral fluxes of total energy, water vapour, and latent heat (with the latent heat of vaporization varying with temperature). In addition, the divergences of the lateral fluxes are provided. These divergences and fluxes are also available in the ERA5 archive, but do not employ mass-consistent horizontal wind fields and are contaminated with numerical noise. In the present dataset, mass consistency is achieved by iteratively adjusting the horizontal wind field at every time step. The divergence and tendency terms from this dataset can be combined to indirectly estimate mass-consistent surface flux terms of the atmospheric energy and moisture budget. In the energy budget, horizontal and vertical enthalpy fluxes associated with water and snow are neglected. This framework allows for unambiguous estimation of net surface heat fluxes (sum of radiative and turbulent heat fluxes) when combined with net radiative fluxes at the top of the atmosphere (not included in this dataset). The divergence and tendency term of the atmospheric moisture budget can be combined to indirectly estimate surface freshwater fluxes (P+E) which have an unbiased global mean. In addition, this dataset allows the computation of the following: The divergence of total energy flux with temperature-independent latent heat of vaporization is obtained by subtracting the divergence of latent heat flux and add the divergence of water vapour flux multiplied by Lv = 2.5008 J kg-1 instead (this works analogously for tendency and transport terms). Divergence fields with full spectral resolution can be derived from corresponding east- and northward transports (divergence terms in this dataset are truncated at wave number 180 to reduce artificial noise over high topography). The divergence of total energy flux with temperature-independent latent heat of vaporization is obtained by subtracting the divergence of latent heat flux and add the divergence of water vapour flux multiplied by Lv = 2.5008 J kg-1 instead (this works analogously for tendency and transport terms). The divergence of total energy flux with temperature-independent latent heat of vaporization is obtained by subtracting the divergence of latent heat flux and add the divergence of water vapour flux multiplied by Lv = 2.5008 J kg-1 instead (this works analogously for tendency and transport terms). Divergence fields with full spectral resolution can be derived from corresponding east- and northward transports (divergence terms in this dataset are truncated at wave number 180 to reduce artificial noise over high topography). Divergence fields with full spectral resolution can be derived from corresponding east- and northward transports (divergence terms in this dataset are truncated at wave number 180 to reduce artificial noise over high topography). This dataset is produced on behalf of Copernicus Climate Change Service by the Department of Meteorology and Geophysics, University of Vienna. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 0.25°x 0.25° Vertical coverage Integrated from surface to top of atmosphere Vertical resolution Single level Temporal coverage From January 1979 to present Temporal resolution Monthly File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions Present version is 1.0 Update frequency Yearly with a delay of about 4 months relatively to real time DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.25°x 0.25° Horizontal resolution 0.25°x 0.25° Vertical coverage Integrated from surface to top of atmosphere Vertical coverage Integrated from surface to top of atmosphere Vertical resolution Single level Vertical resolution Single level Temporal coverage From January 1979 to present Temporal coverage From January 1979 to present Temporal resolution Monthly Temporal resolution Monthly File format NetCDF 4 File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions Present version is 1.0 Versions Present version is 1.0 Update frequency Yearly with a delay of about 4 months relatively to real time Update frequency Yearly with a delay of about 4 months relatively to real time MAIN VARIABLES Name Units Description Divergence of vertical integral of latent heat flux W m-2 This variable is the horizontal rate of flow of latent heat integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. Latent heat is the amount of energy required to convert liquid water to water vapour. The latent heat flux is the horizontal rate of flow per metre. Its horizontal divergence is positive for a latent heat flux that is spreading out, or diverging, and negative for a latent heat flux that is concentrating, or converging. Winds used for computation of fluxes of latent heat are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. The latent heat of vaporization is computed as a function of temperature. This variable is truncated at wave number 180 to reduce numerical noise. Divergence of vertical integral of total energy flux W m-2 This variable is the horizontal rate of flow of total energy integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. The total energy in this variable is the sum of sensible heat, latent heat (with latent heat of vaporization varying with temperature), kinetic, and potential energy, which is also referred to as the moist static plus kinetic energy. The total energy flux is the horizontal rate of flow of energy per metre. Its horizontal divergence is positive for a total energy flux that is spreading out, or diverging, and negative for a total energy flux that is concentrating, or converging. The sensible heat is referenced to 0 degree Celsius, whereby sensible heat of water vapour is neglected. Winds used for computation of fluxes of total energy are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. This variable is truncated at wave number 180 to reduce numerical noise. Divergence of vertical integral of water vapour flux kg m-2 s-1 This variable is the horizontal rate of flow of water vapour integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. The water vapour flux is the horizontal rate of flow per metre. Its divergence is positive for a water vapour flux that is spreading out, or diverging, and negative for a water vapour flux that is concentrating, or converging. Winds used for computation of fluxes of water vapour are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. This variable is truncated at wave number 180 to reduce numerical noise. Tendency of vertical integral of latent heat W m-2 This variable is the rate of change of latent heat integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. Latent heat is the amount of energy required to convert liquid water to water vapour. The vertical integral of latent heat is the total amount of latent heat per unit area. Its tendency, or rate of change, is positive if the latent heat increases and negative if the latent heat decreases in an atmospheric column. The latent heat of vaporization is computed as a function of temperature. Tendency of vertical integral of total energy W m-2 This variable is the rate of change of total energy integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. In this variable, the total energy is the sum of internal energy, latent heat (with latent heat of vaporization varying with temperature), kinetic, and potential energy. The vertical integral of total energy is the total amount of atmospheric energy per unit area. Its tendency, or rate of change, is positive if the total energy increases and negative if the total energy decreases in an atmospheric column. The sensible heat is referenced to 0 degree Celsius, whereby sensible heat of water vapour is neglected. Tendency of vertical integral of water vapour kg m-2 s-1 This variable is the rate of change of water vapour integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. The vertical integral of water vapour is the total amount of atmospheric moisture per unit area. Its tendency, or rate of change, is positive if the water vapour increases and negative if the water vapour decreases in an atmospheric column. Vertical integral of eastward latent heat flux W m-1 This variable is the eastward component of the latent heat flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. Latent heat is the amount of energy required to convert liquid water to water vapour. This variable is the horizontal rate of flow of latent heat per metre in east-west direction. It is positive for a latent heat flux in eastward direction, and negative for a latent heat flux in westward direction. Winds used for computation of fluxes of latent heat are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. The latent heat of vaporization is computed as a function of temperature. Vertical integral of eastward total energy flux W m-1 This variable is the eastward component of the total energy flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. The total energy in this variable is the sum of sensible heat, latent heat (with latent heat of vaporization varying with temperature), kinetic, and potential energy, which is also referred to as the moist static plus kinetic energy. This variable is the horizontal rate of flow of energy per metre in east-west direction. It is positive for a total energy flux in eastward direction, and negative for a total energy flux in westward direction. The sensible heat is referenced to 0 degree Celsius, whereby sensible heat of water vapour is neglected. Winds used for computation of fluxes of total energy are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. Vertical integral of eastward water vapour flux kg m-1 s-1 This variable is the eastward component of the water vapour flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. This variable is the horizontal rate of flow of water vapour per metre in east-west direction. It is positive for a water vapour flux in eastward direction, and negative for a water vapour flux in westward direction. Winds used for computation of fluxes of water vapour are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. Vertical integral of northward latent heat flux W m-1 This variable is the northward component of the latent heat flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. Latent heat is the amount of energy required to convert liquid water to water vapour. This variable is the horizontal rate of flow of latent heat per metre in north-south direction. It is positive for a latent heat flux in northward direction, and negative for a latent heat flux in southward direction. Winds used for computation of fluxes of latent heat are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. The latent heat of vaporization is computed as a function of temperature. Vertical integral of northward total energy flux W m-1 This variable is the northward component of the total energy flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. The total energy in this variable is the sum of sensible heat, latent heat (with latent heat of vaporization varying with temperature), kinetic, and potential energy, which is also referred to as the moist static plus kinetic energy. This variable is the horizontal rate of flow of energy per metre in north-south direction. It is positive for a total energy flux in northward direction, and negative for a total energy flux in southward direction. The sensible heat is referenced to 0 degree Celsius, whereby sensible heat of water vapour is neglected. Winds used for computation of fluxes of total energy are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. Vertical integral of northward water vapour flux kg m-1 s-1 This variable is the northward component of the water vapour flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. This variable is the horizontal rate of flow per metre in north-south direction. It is positive for a water vapour flux in northward direction, and negative for a water vapour flux in southward direction. Winds used for computation of fluxes of water vapour are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Divergence of vertical integral of latent heat flux W m-2 This variable is the horizontal rate of flow of latent heat integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. Latent heat is the amount of energy required to convert liquid water to water vapour. The latent heat flux is the horizontal rate of flow per metre. Its horizontal divergence is positive for a latent heat flux that is spreading out, or diverging, and negative for a latent heat flux that is concentrating, or converging. Winds used for computation of fluxes of latent heat are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. The latent heat of vaporization is computed as a function of temperature. This variable is truncated at wave number 180 to reduce numerical noise. Divergence of vertical integral of latent heat flux W m-2 This variable is the horizontal rate of flow of latent heat integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. Latent heat is the amount of energy required to convert liquid water to water vapour. The latent heat flux is the horizontal rate of flow per metre. Its horizontal divergence is positive for a latent heat flux that is spreading out, or diverging, and negative for a latent heat flux that is concentrating, or converging. Winds used for computation of fluxes of latent heat are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. The latent heat of vaporization is computed as a function of temperature. This variable is truncated at wave number 180 to reduce numerical noise. Divergence of vertical integral of total energy flux W m-2 This variable is the horizontal rate of flow of total energy integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. The total energy in this variable is the sum of sensible heat, latent heat (with latent heat of vaporization varying with temperature), kinetic, and potential energy, which is also referred to as the moist static plus kinetic energy. The total energy flux is the horizontal rate of flow of energy per metre. Its horizontal divergence is positive for a total energy flux that is spreading out, or diverging, and negative for a total energy flux that is concentrating, or converging. The sensible heat is referenced to 0 degree Celsius, whereby sensible heat of water vapour is neglected. Winds used for computation of fluxes of total energy are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. This variable is truncated at wave number 180 to reduce numerical noise. Divergence of vertical integral of total energy flux W m-2 This variable is the horizontal rate of flow of total energy integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. The total energy in this variable is the sum of sensible heat, latent heat (with latent heat of vaporization varying with temperature), kinetic, and potential energy, which is also referred to as the moist static plus kinetic energy. The total energy flux is the horizontal rate of flow of energy per metre. Its horizontal divergence is positive for a total energy flux that is spreading out, or diverging, and negative for a total energy flux that is concentrating, or converging. The sensible heat is referenced to 0 degree Celsius, whereby sensible heat of water vapour is neglected. Winds used for computation of fluxes of total energy are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. This variable is truncated at wave number 180 to reduce numerical noise. Divergence of vertical integral of water vapour flux kg m-2 s-1 This variable is the horizontal rate of flow of water vapour integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. The water vapour flux is the horizontal rate of flow per metre. Its divergence is positive for a water vapour flux that is spreading out, or diverging, and negative for a water vapour flux that is concentrating, or converging. Winds used for computation of fluxes of water vapour are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. This variable is truncated at wave number 180 to reduce numerical noise. Divergence of vertical integral of water vapour flux kg m-2 s-1 This variable is the horizontal rate of flow of water vapour integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. The water vapour flux is the horizontal rate of flow per metre. Its divergence is positive for a water vapour flux that is spreading out, or diverging, and negative for a water vapour flux that is concentrating, or converging. Winds used for computation of fluxes of water vapour are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. This variable is truncated at wave number 180 to reduce numerical noise. Tendency of vertical integral of latent heat W m-2 This variable is the rate of change of latent heat integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. Latent heat is the amount of energy required to convert liquid water to water vapour. The vertical integral of latent heat is the total amount of latent heat per unit area. Its tendency, or rate of change, is positive if the latent heat increases and negative if the latent heat decreases in an atmospheric column. The latent heat of vaporization is computed as a function of temperature. Tendency of vertical integral of latent heat W m-2 This variable is the rate of change of latent heat integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. Latent heat is the amount of energy required to convert liquid water to water vapour. The vertical integral of latent heat is the total amount of latent heat per unit area. Its tendency, or rate of change, is positive if the latent heat increases and negative if the latent heat decreases in an atmospheric column. The latent heat of vaporization is computed as a function of temperature. Tendency of vertical integral of total energy W m-2 This variable is the rate of change of total energy integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. In this variable, the total energy is the sum of internal energy, latent heat (with latent heat of vaporization varying with temperature), kinetic, and potential energy. The vertical integral of total energy is the total amount of atmospheric energy per unit area. Its tendency, or rate of change, is positive if the total energy increases and negative if the total energy decreases in an atmospheric column. The sensible heat is referenced to 0 degree Celsius, whereby sensible heat of water vapour is neglected. Tendency of vertical integral of total energy W m-2 This variable is the rate of change of total energy integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. In this variable, the total energy is the sum of internal energy, latent heat (with latent heat of vaporization varying with temperature), kinetic, and potential energy. The vertical integral of total energy is the total amount of atmospheric energy per unit area. Its tendency, or rate of change, is positive if the total energy increases and negative if the total energy decreases in an atmospheric column. The sensible heat is referenced to 0 degree Celsius, whereby sensible heat of water vapour is neglected. Tendency of vertical integral of water vapour kg m-2 s-1 This variable is the rate of change of water vapour integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. The vertical integral of water vapour is the total amount of atmospheric moisture per unit area. Its tendency, or rate of change, is positive if the water vapour increases and negative if the water vapour decreases in an atmospheric column. Tendency of vertical integral of water vapour kg m-2 s-1 This variable is the rate of change of water vapour integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. The vertical integral of water vapour is the total amount of atmospheric moisture per unit area. Its tendency, or rate of change, is positive if the water vapour increases and negative if the water vapour decreases in an atmospheric column. Vertical integral of eastward latent heat flux W m-1 This variable is the eastward component of the latent heat flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. Latent heat is the amount of energy required to convert liquid water to water vapour. This variable is the horizontal rate of flow of latent heat per metre in east-west direction. It is positive for a latent heat flux in eastward direction, and negative for a latent heat flux in westward direction. Winds used for computation of fluxes of latent heat are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. The latent heat of vaporization is computed as a function of temperature. Vertical integral of eastward latent heat flux W m-1 This variable is the eastward component of the latent heat flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. Latent heat is the amount of energy required to convert liquid water to water vapour. This variable is the horizontal rate of flow of latent heat per metre in east-west direction. It is positive for a latent heat flux in eastward direction, and negative for a latent heat flux in westward direction. Winds used for computation of fluxes of latent heat are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. The latent heat of vaporization is computed as a function of temperature. Vertical integral of eastward total energy flux W m-1 This variable is the eastward component of the total energy flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. The total energy in this variable is the sum of sensible heat, latent heat (with latent heat of vaporization varying with temperature), kinetic, and potential energy, which is also referred to as the moist static plus kinetic energy. This variable is the horizontal rate of flow of energy per metre in east-west direction. It is positive for a total energy flux in eastward direction, and negative for a total energy flux in westward direction. The sensible heat is referenced to 0 degree Celsius, whereby sensible heat of water vapour is neglected. Winds used for computation of fluxes of total energy are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. Vertical integral of eastward total energy flux W m-1 This variable is the eastward component of the total energy flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. The total energy in this variable is the sum of sensible heat, latent heat (with latent heat of vaporization varying with temperature), kinetic, and potential energy, which is also referred to as the moist static plus kinetic energy. This variable is the horizontal rate of flow of energy per metre in east-west direction. It is positive for a total energy flux in eastward direction, and negative for a total energy flux in westward direction. The sensible heat is referenced to 0 degree Celsius, whereby sensible heat of water vapour is neglected. Winds used for computation of fluxes of total energy are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. Vertical integral of eastward water vapour flux kg m-1 s-1 This variable is the eastward component of the water vapour flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. This variable is the horizontal rate of flow of water vapour per metre in east-west direction. It is positive for a water vapour flux in eastward direction, and negative for a water vapour flux in westward direction. Winds used for computation of fluxes of water vapour are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. Vertical integral of eastward water vapour flux kg m-1 s-1 This variable is the eastward component of the water vapour flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. This variable is the horizontal rate of flow of water vapour per metre in east-west direction. It is positive for a water vapour flux in eastward direction, and negative for a water vapour flux in westward direction. Winds used for computation of fluxes of water vapour are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. Vertical integral of northward latent heat flux W m-1 This variable is the northward component of the latent heat flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. Latent heat is the amount of energy required to convert liquid water to water vapour. This variable is the horizontal rate of flow of latent heat per metre in north-south direction. It is positive for a latent heat flux in northward direction, and negative for a latent heat flux in southward direction. Winds used for computation of fluxes of latent heat are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. The latent heat of vaporization is computed as a function of temperature. Vertical integral of northward latent heat flux W m-1 This variable is the northward component of the latent heat flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. Latent heat is the amount of energy required to convert liquid water to water vapour. This variable is the horizontal rate of flow of latent heat per metre in north-south direction. It is positive for a latent heat flux in northward direction, and negative for a latent heat flux in southward direction. Winds used for computation of fluxes of latent heat are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. The latent heat of vaporization is computed as a function of temperature. Vertical integral of northward total energy flux W m-1 This variable is the northward component of the total energy flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. The total energy in this variable is the sum of sensible heat, latent heat (with latent heat of vaporization varying with temperature), kinetic, and potential energy, which is also referred to as the moist static plus kinetic energy. This variable is the horizontal rate of flow of energy per metre in north-south direction. It is positive for a total energy flux in northward direction, and negative for a total energy flux in southward direction. The sensible heat is referenced to 0 degree Celsius, whereby sensible heat of water vapour is neglected. Winds used for computation of fluxes of total energy are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. Vertical integral of northward total energy flux W m-1 This variable is the northward component of the total energy flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. The total energy in this variable is the sum of sensible heat, latent heat (with latent heat of vaporization varying with temperature), kinetic, and potential energy, which is also referred to as the moist static plus kinetic energy. This variable is the horizontal rate of flow of energy per metre in north-south direction. It is positive for a total energy flux in northward direction, and negative for a total energy flux in southward direction. The sensible heat is referenced to 0 degree Celsius, whereby sensible heat of water vapour is neglected. Winds used for computation of fluxes of total energy are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. Vertical integral of northward water vapour flux kg m-1 s-1 This variable is the northward component of the water vapour flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. This variable is the horizontal rate of flow per metre in north-south direction. It is positive for a water vapour flux in northward direction, and negative for a water vapour flux in southward direction. Winds used for computation of fluxes of water vapour are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. Vertical integral of northward water vapour flux kg m-1 s-1 This variable is the northward component of the water vapour flux integrated over an atmospheric column extending from the surface of the Earth to the top of the atmosphere. This variable is the horizontal rate of flow per metre in north-south direction. It is positive for a water vapour flux in northward direction, and negative for a water vapour flux in southward direction. Winds used for computation of fluxes of water vapour are mass-adjusted according to the diagnosed imbalance between divergence of vertically integrated dry mass flux and tendency of dry air mass. 148 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/seasonal-postprocessed-single-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal-postprocessed-single-levels seasonal-postprocessed-single-levels This entry covers single-level data post-processed for bias adjustment on a monthly time resolution. single-level data monthly time resolution Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Seasonal forecasts Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). multi-system seasonal forecast service The variables available in this data set are listed in the table below. The data includes forecasts created in real-time since 2017. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 1° x 1° Temporal coverage 2017 to present Temporal resolution Monthly File format GRIB Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 1° x 1° Horizontal resolution 1° x 1° Temporal coverage 2017 to present Temporal coverage 2017 to present Temporal resolution Monthly Temporal resolution Monthly File format GRIB File format GRIB Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. MAIN VARIABLES Name Units 10m u-component of wind anomaly m s-1 10m v-component of wind anomaly m s-1 10m wind gust anomaly m s-1 10m wind speed anomaly m s-1 2m dewpoint temperature anomaly K 2m temperature anomaly K East-west surface stress anomalous rate of accumulation N m-2 Evaporation anomalous rate of accumulation m of water s-1 Maximum 2m temperature in the last 24 hours anomaly K Mean sea level pressure anomaly Pa Mean sub-surface runoff rate anomaly m of water equivalent s-1 Mean surface runoff rate anomaly m of water equivalent s-1 Minimum 2m temperature in the last 24 hours anomaly K North-south surface stress anomalous rate of accumulation N m-2 Runoff anomalous rate of accumulation m s-1 Sea surface temperature anomaly K Sea-ice cover anomaly (0 - 1) Snow density anomaly kg m-3 Snow depth anomaly m of water equivalent Snowfall anomalous rate of accumulation m of water equivalent s-1 Soil temperature anomaly level 1 K Solar insolation anomalous rate of accumulation W m-2 s-1 Surface latent heat flux anomalous rate of accumulation J m-2 Surface sensible heat flux anomalous rate of accumulation J m-2 Surface solar radiation anomalous rate of accumulation J m-2 Surface solar radiation downwards anomalous rate of accumulation J m-2 Surface thermal radiation anomalous rate of accumulation J m-2 Surface thermal radiation downwards anomalous rate of accumulation J m-2 Top solar radiation anomalous rate of accumulation J m-2 Top thermal radiation anomalous rate of accumulation J m-2 Total cloud cover anomaly (0 - 1) Total column cloud ice water anomaly kg m-2 Total column cloud liquid water anomaly kg m-2 Total column water vapour anomaly kg m-2 Total precipitation anomalous rate of accumulation m s-1 MAIN VARIABLES MAIN VARIABLES Name Units Name Units 10m u-component of wind anomaly m s-1 10m u-component of wind anomaly m s-1 10m v-component of wind anomaly m s-1 10m v-component of wind anomaly m s-1 10m wind gust anomaly m s-1 10m wind gust anomaly m s-1 10m wind speed anomaly m s-1 10m wind speed anomaly m s-1 2m dewpoint temperature anomaly K 2m dewpoint temperature anomaly K 2m temperature anomaly K 2m temperature anomaly K East-west surface stress anomalous rate of accumulation N m-2 East-west surface stress anomalous rate of accumulation N m-2 Evaporation anomalous rate of accumulation m of water s-1 Evaporation anomalous rate of accumulation m of water s-1 Maximum 2m temperature in the last 24 hours anomaly K Maximum 2m temperature in the last 24 hours anomaly K Mean sea level pressure anomaly Pa Mean sea level pressure anomaly Pa Mean sub-surface runoff rate anomaly m of water equivalent s-1 Mean sub-surface runoff rate anomaly m of water equivalent s-1 Mean surface runoff rate anomaly m of water equivalent s-1 Mean surface runoff rate anomaly m of water equivalent s-1 Minimum 2m temperature in the last 24 hours anomaly K Minimum 2m temperature in the last 24 hours anomaly K North-south surface stress anomalous rate of accumulation N m-2 North-south surface stress anomalous rate of accumulation N m-2 Runoff anomalous rate of accumulation m s-1 Runoff anomalous rate of accumulation m s-1 Sea surface temperature anomaly K Sea surface temperature anomaly K Sea-ice cover anomaly (0 - 1) Sea-ice cover anomaly (0 - 1) Snow density anomaly kg m-3 Snow density anomaly kg m-3 Snow depth anomaly m of water equivalent Snow depth anomaly m of water equivalent Snowfall anomalous rate of accumulation m of water equivalent s-1 Snowfall anomalous rate of accumulation m of water equivalent s-1 Soil temperature anomaly level 1 K Soil temperature anomaly level 1 K Solar insolation anomalous rate of accumulation W m-2 s-1 Solar insolation anomalous rate of accumulation W m-2 s-1 Surface latent heat flux anomalous rate of accumulation J m-2 Surface latent heat flux anomalous rate of accumulation J m-2 Surface sensible heat flux anomalous rate of accumulation J m-2 Surface sensible heat flux anomalous rate of accumulation J m-2 Surface solar radiation anomalous rate of accumulation J m-2 Surface solar radiation anomalous rate of accumulation J m-2 Surface solar radiation downwards anomalous rate of accumulation J m-2 Surface solar radiation downwards anomalous rate of accumulation J m-2 Surface thermal radiation anomalous rate of accumulation J m-2 Surface thermal radiation anomalous rate of accumulation J m-2 Surface thermal radiation downwards anomalous rate of accumulation J m-2 Surface thermal radiation downwards anomalous rate of accumulation J m-2 Top solar radiation anomalous rate of accumulation J m-2 Top solar radiation anomalous rate of accumulation J m-2 Top thermal radiation anomalous rate of accumulation J m-2 Top thermal radiation anomalous rate of accumulation J m-2 Total cloud cover anomaly (0 - 1) Total cloud cover anomaly (0 - 1) Total column cloud ice water anomaly kg m-2 Total column cloud ice water anomaly kg m-2 Total column cloud liquid water anomaly kg m-2 Total column cloud liquid water anomaly kg m-2 Total column water vapour anomaly kg m-2 Total column water vapour anomaly kg m-2 Total precipitation anomalous rate of accumulation m s-1 Total precipitation anomalous rate of accumulation m s-1 149 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-1990-vector-europe-6-yearly-version https://land.copernicus.eu/pan-european/corine-land-cover/clc-1990/view CORINE Land Cover 1990 (vector), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover 1990 (CLC1990) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 1990. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 150 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-european-energy-sector https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-energy-sector sis-european-energy-sector The dataset contains wind speed, precipitation, relative humidity, global horizontal irradiance, sea level pressure, air temperature, snow depth and dewpoint depression near-surface data relevant to energy industry for the European domain for the period 1979-2016. wind speed, precipitation, relative humidity, global horizontal irradiance, sea level pressure, air temperature, snow depth dewpoint depression The dataset is mostly derived from 6-hourly ERA-Interim reanalysis dataset by bias adjusting against observations using different methods. Data are then aggregated on daily, monthly, seasonal and annual averages. For wind speed, the field at 10 metres was bias-adjusted and then extrapolated to 100 metres using a stability-dependent logarithmic scaling. The dataset was generated by one of the Sectoral Information System (SIS) proof of concept contracts led by the University of East Anglia to the the Copernicus Climate Change Service (C3S). The aim of the European Climatic Energy Mixes (ECEM) contract – which worked in close collaboration with a set of prospective users – was to enable the energy industry and policy makers to assess how well different energy supply mixes in Europe will meet demand, over different time horizons from seasonal to long-term decadal, focusing on the role climate has on the mixes. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage European Horizontal resolution 0.5° x 0.5° Temporal coverage 1979-2016 Temporal resolution Daily File format NetCDF DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage European Horizontal coverage European Horizontal resolution 0.5° x 0.5° Horizontal resolution 0.5° x 0.5° Temporal coverage 1979-2016 Temporal coverage 1979-2016 Temporal resolution Daily Temporal resolution Daily File format NetCDF File format NetCDF MAIN VARIABLES Name Units Description Air temperature C Temperature of the air at about 2m above the surface. Dewpoint depression C Difference between air and dewpoint temperatures. Dewpoint temperature is the estimated temperature at which a parcel of air reaches saturation upon being cooled but keeping its pressure and specific humidity. Global horizontal irradiance W m-2 Time average of the downwelling solar irradiance received on a horizontal plane at ground level Precipitation mm Depth of rain water accumulated on a flat, horizontal and impermeable surface per unit area during a given time period. Pressure at sea level hPa Expected value of the air-pressure at the virtual vertical level defined by the average level of the sea Relative humidity % Amount of moisture in the air divided by the maximum amount of moisture that could exist in the air at a specific temperature. Snow depth m of water equivalent Vertical extent of equivalent liquid water over ice or land if the actual snow over ice or land would theoretically melted instantaneously. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Air temperature C Temperature of the air at about 2m above the surface. Air temperature C Temperature of the air at about 2m above the surface. Dewpoint depression C Difference between air and dewpoint temperatures. Dewpoint temperature is the estimated temperature at which a parcel of air reaches saturation upon being cooled but keeping its pressure and specific humidity. Dewpoint depression C Difference between air and dewpoint temperatures. Dewpoint temperature is the estimated temperature at which a parcel of air reaches saturation upon being cooled but keeping its pressure and specific humidity. Global horizontal irradiance W m-2 Time average of the downwelling solar irradiance received on a horizontal plane at ground level Global horizontal irradiance W m-2 Time average of the downwelling solar irradiance received on a horizontal plane at ground level Precipitation mm Depth of rain water accumulated on a flat, horizontal and impermeable surface per unit area during a given time period. Precipitation mm Depth of rain water accumulated on a flat, horizontal and impermeable surface per unit area during a given time period. Pressure at sea level hPa Expected value of the air-pressure at the virtual vertical level defined by the average level of the sea Pressure at sea level hPa Expected value of the air-pressure at the virtual vertical level defined by the average level of the sea Relative humidity % Amount of moisture in the air divided by the maximum amount of moisture that could exist in the air at a specific temperature. Relative humidity % Amount of moisture in the air divided by the maximum amount of moisture that could exist in the air at a specific temperature. Snow depth m of water equivalent Vertical extent of equivalent liquid water over ice or land if the actual snow over ice or land would theoretically melted instantaneously. Snow depth m of water equivalent Vertical extent of equivalent liquid water over ice or land if the actual snow over ice or land would theoretically melted instantaneously. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity. 151 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-2018-vector-europe-6-yearly-version https://land.copernicus.eu/pan-european/corine-land-cover/clc2018 CORINE Land Cover 2018 (vector), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover 2018 (CLC2018) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2018. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 152 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-satellite-surface-radiation-budget-cloudcci https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-satellite-surface-radiation-budget-cloud_cci app-satellite-surface-radiation-budget-cloud_cci This application presents analyses of surface radiation budget variables from the Cloud_CCI product family. Surface radiation variables are indicative of atmospheric warming globally or in specific regions, and can be used to determine heat flow on an international scale. The Cloud_CCI product family refers to the ESA Cloud_cci version 3.0 surface radiation data record. This record uses 17 years (1995-2012) of radiation and cloud observations from the Along Track Scanning Radiometer (ATSR) instruments. The ATSR2 Instrument on board ERS-2 observed the surface of Earth in the infrared range at a spatial resolution of 1km, useful for scientific studies of land surface, atmosphere, clouds and oceans. AATSR on board ENVISAT succeeded ATSR2, observing three channels of thermal infrared wavelengths, from which surface radiation may be derived. The application output consists of a map of the data and a time-series for the surface radiation variable selected. Users can choose the analysis method to apply to the data and select the region, years and months of interest. User-selectable parameters User-selectable parameters Variable: Downwelling Shortwave Flux Downwelling Longwave Flux Upwelling Shortwave Flux Upwelling Longwave Flux Net Downward Shortwave Flux Net Downward Longwave Flux Net Downward Radiative Flux Region: Global Africa (-55°E to 95°E and -35°N to 40°N) Asia (35°E to 165°E and 0°N to 80°N) Europe (-25°E to 45°E and 35°N to 70°N) North America (-130°E to -60°E and 20°N to 55°N) South America (-140°E to 10°E and -60°N to 15°N) Oceania (-60°E to 150°E and -55°N to 20°N) Analysis method: Mean Anomaly Standard deviation Trend (trend calculations may take a long time to compute) Year range: 1996 to 2012 Month range: January to December Anomaly year: 1996 to 2012 Variable: Downwelling Shortwave Flux Downwelling Longwave Flux Upwelling Shortwave Flux Upwelling Longwave Flux Net Downward Shortwave Flux Net Downward Longwave Flux Net Downward Radiative Flux Downwelling Shortwave Flux Downwelling Longwave Flux Upwelling Shortwave Flux Upwelling Longwave Flux Net Downward Shortwave Flux Net Downward Longwave Flux Net Downward Radiative Flux Downwelling Shortwave Flux Downwelling Longwave Flux Upwelling Shortwave Flux Upwelling Longwave Flux Net Downward Shortwave Flux Net Downward Longwave Flux Net Downward Radiative Flux Region: Global Africa (-55°E to 95°E and -35°N to 40°N) Asia (35°E to 165°E and 0°N to 80°N) Europe (-25°E to 45°E and 35°N to 70°N) North America (-130°E to -60°E and 20°N to 55°N) South America (-140°E to 10°E and -60°N to 15°N) Oceania (-60°E to 150°E and -55°N to 20°N) Global Africa (-55°E to 95°E and -35°N to 40°N) Asia (35°E to 165°E and 0°N to 80°N) Europe (-25°E to 45°E and 35°N to 70°N) North America (-130°E to -60°E and 20°N to 55°N) South America (-140°E to 10°E and -60°N to 15°N) Oceania (-60°E to 150°E and -55°N to 20°N) Global Africa (-55°E to 95°E and -35°N to 40°N) Asia (35°E to 165°E and 0°N to 80°N) Europe (-25°E to 45°E and 35°N to 70°N) North America (-130°E to -60°E and 20°N to 55°N) South America (-140°E to 10°E and -60°N to 15°N) Oceania (-60°E to 150°E and -55°N to 20°N) Analysis method: Mean Anomaly Standard deviation Trend (trend calculations may take a long time to compute) Mean Anomaly Standard deviation Trend (trend calculations may take a long time to compute) Mean Anomaly Standard deviation Trend (trend calculations may take a long time to compute) Year range: 1996 to 2012 Month range: January to December Anomaly year: 1996 to 2012 INPUT VARIABLES Name Units Description Source Surface downwelling longwave flux W m-2 Amount of longwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from the above. Surface radiation budget Surface downwelling shortwave flux W m-2 Amount of shortwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from the above. Surface radiation budget Surface net downward longwave flux W m-2 Difference between the amount of longwave radiation energy reaching the lower boundary of the atmosphere from below (upwelling) and the amount from above (downwelling). Values are provided per unit of time and area. Surface radiation budget Surface net downward radiative flux W m-2 Difference between the amount of radiation energy reaching the lower boundary of the atmosphere from below (upwelling) and the amount from above (downwelling). Values are provided per unit of time and area. Surface radiation budget Surface net downward shortwave flux W m-2 Difference between the amount of shortwave radiation energy reaching the lower boundary of the atmosphere from below (upwelling) and the amount from above (downwelling). Values are provided per unit of time and area. Surface radiation budget Surface upwelling longwave flux W m-2 Amount of longwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from below. Surface radiation budget Surface upwelling shortwave flux W m-2 Amount of shortwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from below. Surface radiation budget INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Surface downwelling longwave flux W m-2 Amount of longwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from the above. Surface radiation budget Surface downwelling longwave flux W m-2 Amount of longwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from the above. Surface radiation budget Surface radiation budget Surface downwelling shortwave flux W m-2 Amount of shortwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from the above. Surface radiation budget Surface downwelling shortwave flux W m-2 Amount of shortwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from the above. Surface radiation budget Surface radiation budget Surface net downward longwave flux W m-2 Difference between the amount of longwave radiation energy reaching the lower boundary of the atmosphere from below (upwelling) and the amount from above (downwelling). Values are provided per unit of time and area. Surface radiation budget Surface net downward longwave flux W m-2 Difference between the amount of longwave radiation energy reaching the lower boundary of the atmosphere from below (upwelling) and the amount from above (downwelling). Values are provided per unit of time and area. Surface radiation budget Surface radiation budget Surface net downward radiative flux W m-2 Difference between the amount of radiation energy reaching the lower boundary of the atmosphere from below (upwelling) and the amount from above (downwelling). Values are provided per unit of time and area. Surface radiation budget Surface net downward radiative flux W m-2 Difference between the amount of radiation energy reaching the lower boundary of the atmosphere from below (upwelling) and the amount from above (downwelling). Values are provided per unit of time and area. Surface radiation budget Surface radiation budget Surface net downward shortwave flux W m-2 Difference between the amount of shortwave radiation energy reaching the lower boundary of the atmosphere from below (upwelling) and the amount from above (downwelling). Values are provided per unit of time and area. Surface radiation budget Surface net downward shortwave flux W m-2 Difference between the amount of shortwave radiation energy reaching the lower boundary of the atmosphere from below (upwelling) and the amount from above (downwelling). Values are provided per unit of time and area. Surface radiation budget Surface radiation budget Surface upwelling longwave flux W m-2 Amount of longwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from below. Surface radiation budget Surface upwelling longwave flux W m-2 Amount of longwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from below. Surface radiation budget Surface radiation budget Surface upwelling shortwave flux W m-2 Amount of shortwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from below. Surface radiation budget Surface upwelling shortwave flux W m-2 Amount of shortwave radiation energy reaching the lower boundary of the atmosphere per unit of time and area from below. Surface radiation budget Surface radiation budget 153 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/land-surface-temperature-daily-cycle-2021-present-raster https://land.copernicus.eu/global/access Land Surface Temperature Daily Cycle 2021-present (raster 5 km), global, 10-daily - version 2 10-day Daily Cycle Land Surface Temperature (LST10-DC) provides a statistical overview of the LST daily cycle over each 10-day compositing for every image pixel. LST10-DC is useful for the scientific community, namely for those dealing with meteorological and climate models. Accurate values of LST are also of special interest in a wide range of areas related to land surface processes, including meteorology, hydrology, agrometeorology, climatology and environmental studies. 154 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/land-surface-temperature-daily-cycle-2017-2021-raster-5 https://land.copernicus.eu/global/access Land Surface Temperature Daily Cycle 2017-2021 (raster 5 km), global, 10-daily - version 1 10-day Daily Cycle Land Surface Temperature (LST10-DC) provides a statistical overview of the LST daily cycle over each 10-day compositing for every image pixel. LST10-DC is useful for the scientific community, namely for those dealing with meteorological and climate models. Accurate values of LST are also of special interest in a wide range of areas related to land surface processes, including meteorology, hydrology, agrometeorology, climatology and environmental studies. 155 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-agriadapt-agroclimatic-explorer https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-agriadapt-agroclimatic-explorer app-agriadapt-agroclimatic-explorer This application allows users to interactively explore agro-climate indicators relevant to agricultural production in Europe under present climate conditions (1970-present) and as well as comparing future climate scenarios (2011-2100). Projected changes in the climate will impact European agriculture by affecting the phenological development of crops thereby changing cropping calendars. Additionally, detrimental impacts on crop production such as that observed with heat stress during extreme weather events can lead to reduced crop yields with consequences across the whole agricultural sector. The time evolution of the agro-climatic indicators in this application can be explored at a spatial resolution of 0.25° x 0.25° for any selected point on the European continent. The essential atmospheric variables used to calculate the agro-climatic indicators (listed variables below) are the daily maximum, average and minimum temperatures as well as accumulated precipitation. ERA5-Land reanalysis was used to cover the present, while nine bias-adjusted CORDEX regional climate models were used for climate projections considering both RCP4.5 and RCP8.5 (Representative Concentration Pathway) climate scenarios. User-selectable parameters User-selectable parameters Variable: a climatic or agro-climatic variable amongst; Heat stress, Average temperature, Precipitation, Frost days, Days above 25°C, Date of last spring frost. Scenario: a climate projection scenario; RCP4.5 or RCP8.5. Threshold: a value in degrees Celsius (°C) which is the maximum daily temperature above which heat stress is considered. Start: a starting date ranging from the 15th of March to the 15th of September which defines the start of the period over which the heat stress days are counted. Stop: a stop date ranging from the 1st of April to the 1st of October which defines the end of the period over which the heat stress days are counted. Aggregation: A time aggregation over which the indicators (average temperature, precipitation, frost days, days above 25°C) are computed and presented. It can be Annual, Season or Month. Season or Month: A specific season or month depending on the selected time aggregation above. Seasons are based on three monthly aggregation with; Winter: December, January, February (DJF) Spring: March, April, May (MAM) Summer: June, July, August (JJA) Autumn: September, October, November (SON) Variable: a climatic or agro-climatic variable amongst; Heat stress, Average temperature, Precipitation, Frost days, Days above 25°C, Date of last spring frost. Heat stress Average temperature Precipitation Frost days Days above 25°C Date of last spring frost Scenario: a climate projection scenario; RCP4.5 or RCP8.5. RCP4.5 RCP8.5 Threshold: a value in degrees Celsius (°C) which is the maximum daily temperature above which heat stress is considered. Start: a starting date ranging from the 15th of March to the 15th of September which defines the start of the period over which the heat stress days are counted. Stop: a stop date ranging from the 1st of April to the 1st of October which defines the end of the period over which the heat stress days are counted. Aggregation: A time aggregation over which the indicators (average temperature, precipitation, frost days, days above 25°C) are computed and presented. It can be Annual, Season or Month. average temperature, precipitation, frost days, days above 25°C Annual Season Month Season or Month: A specific season or month depending on the selected time aggregation above. Seasons are based on three monthly aggregation with; Winter: December, January, February (DJF) Spring: March, April, May (MAM) Summer: June, July, August (JJA) Autumn: September, October, November (SON) Winter: December, January, February (DJF) Spring: March, April, May (MAM) Summer: June, July, August (JJA) Autumn: September, October, November (SON) Winter: December, January, February (DJF) Winter Spring: March, April, May (MAM) Spring Summer: June, July, August (JJA) Summer Autumn: September, October, November (SON) Autumn If the Date of last spring frost indicator is selected, the user has no further choice as the aggregation is annual by definition. Date of last spring frost INPUT VARIABLES Name Units Description Source 2m temperature K The ambient temperature of air at 2m above the surface of land, sea or inland waters and calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. 3 hourly data was used to calculate daily mean, maximum and minimum temperature. ERA5-Land Bias-adjusted CORDEX Total precipitation m An accumulated measure of liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above causing it to rise). ERA5-Land Bias-adjusted CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient temperature of air at 2m above the surface of land, sea or inland waters and calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. 3 hourly data was used to calculate daily mean, maximum and minimum temperature. ERA5-Land Bias-adjusted CORDEX 2m temperature K The ambient temperature of air at 2m above the surface of land, sea or inland waters and calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. 3 hourly data was used to calculate daily mean, maximum and minimum temperature. ERA5-Land Bias-adjusted CORDEX ERA5-Land ERA5-Land Bias-adjusted CORDEX Bias-adjusted CORDEX Total precipitation m An accumulated measure of liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above causing it to rise). ERA5-Land Bias-adjusted CORDEX Total precipitation m An accumulated measure of liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above causing it to rise). ERA5-Land Bias-adjusted CORDEX ERA5-Land ERA5-Land Bias-adjusted CORDEX Bias-adjusted CORDEX OUTPUT VARIABLES Name Units Description Average temperature °C Average temperature aggregated by year, season and month. Days above 25°C day Number of days with maximum temperatures exceeding 25°C aggregated by year, season and month. Frost days day Number of days with minimum temperatures 0°C aggregated by year, season and month. Heat stress day Number of days with maximum temperatures exceeding a given threshold temperature over a given period of the year. Heat stress on crops can lead to reduced yield and decrease in quality (e.g. fruits). This indidcator may be used to highlight the impact of extreme events on crops. Precipitation mm Accumulated rain water aggregated by year, season and months. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Average temperature °C Average temperature aggregated by year, season and month. Average temperature °C Average temperature aggregated by year, season and month. Days above 25°C day Number of days with maximum temperatures exceeding 25°C aggregated by year, season and month. Days above 25°C day Number of days with maximum temperatures exceeding 25°C aggregated by year, season and month. Frost days day Number of days with minimum temperatures 0°C aggregated by year, season and month. Frost days day Number of days with minimum temperatures 0°C aggregated by year, season and month. Heat stress day Number of days with maximum temperatures exceeding a given threshold temperature over a given period of the year. Heat stress on crops can lead to reduced yield and decrease in quality (e.g. fruits). This indidcator may be used to highlight the impact of extreme events on crops. Heat stress day Number of days with maximum temperatures exceeding a given threshold temperature over a given period of the year. Heat stress on crops can lead to reduced yield and decrease in quality (e.g. fruits). This indidcator may be used to highlight the impact of extreme events on crops. Precipitation mm Accumulated rain water aggregated by year, season and months. Precipitation mm Accumulated rain water aggregated by year, season and months. 156 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-classified-change-2015-2018-raster-20-m https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/change-maps/2015-2018/imperviousness-classified-change-2015-2018 Imperviousness Classified Change 2015-2018 (raster 20 m), Europe, 3-yearly, Aug. 2020 The High Resolution Layer: Imperviousness Change Classified (IMCC) 2015-2018 is a 20m raster dataset showing the classified change in imperviousness between 2015 and 2018 reference years. The dataset was produced in the frame of the EU Copernicus programme. The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. The dataset is provided as 20 meter rasters (fully conformant with EEA reference grid) in 100 x 100 km tiles grouped according to the EEA38 countries and the United Kingdom. 157 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cems-fire-historical https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical cems-fire-historical This data set provides complete historical reconstruction of meteorological conditions favourable to the start, spread and sustainability of fires. The fire danger metrics provided are part of a vast dataset produced by the Copernicus Emergency Management Service for the European Forest Fire Information System (EFFIS). The European Forest Fire Information System incorporates the fire danger indices for three different models developed in Canada, United States and Australia. In this dataset the fire danger indices are calculated using weather forecast from historical simulations provided by ECMWF ERA5 reanalysis. ERA5 by combining model data and a vast set of quality controlled observations provides a globally complete and consistent data-set and is regarded as a good proxy for observed atmospheric conditions. The selected data records in this data set are regularly extended with time as ERA5 forcing data become available. This dataset is produced by ECMWF in its role of the computational centre for fire danger forecast of the CEMS, on behalf of the Joint Research Centre which is the managing entity of the service. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Land Horizontal resolution Reanalysis: 0.25° x 0.25° Mean, spread and members: 0.5° x 0.5° Temporal coverage 1979 to 2022 Temporal resolution Daily File format NetCDF Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Land Horizontal coverage Global Land Horizontal resolution Reanalysis: 0.25° x 0.25° Mean, spread and members: 0.5° x 0.5° Horizontal resolution Reanalysis: 0.25° x 0.25° Mean, spread and members: 0.5° x 0.5° Reanalysis: 0.25° x 0.25° Mean, spread and members: 0.5° x 0.5° Temporal coverage 1979 to 2022 Temporal coverage 1979 to 2022 Temporal resolution Daily Temporal resolution Daily File format NetCDF File format NetCDF Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Build-up index Dimensionless The Build-Up Index is a weighted combination of the Duff moisture code and Drought code to indicate the total amount of fuel available for combustion by a moving flame front. The Duff moisture code has the most influence on the Build-up index value. For example, a Duff moisture code value of zero always results in a Build-up index value of zero regardless of what the Drought code value is. The Drought code has the strongest influence on the Build-up index when Duff moisture code values are high. The greatest effect that the Drought code can have is to make the Build-up index value equal to twice the Duff moisture code value. The Build-up index is often used for pre-suppression planning purposes. Burning index Dimensionless The Burning Index measures the difficulty of controlling a fire. It is derived from a combination of Spread component (how fast it will spread) and Energy release component (how much energy will be produced). In this way, it is related to flame length, which, in the Fire Behavior Prediction System, is based on rate of spread and heat per unit area. However, because of differences in the calculations for Burning index and flame length, they are not the same. Danger rating Dimensionless The Danger rating is equivalent to the FWI reduced to 6 classes of danger, accordingly to EFFIS danger class levels definition (very low, low, medium, high, very high and extreme). The fire danger classes are the same for all countries so maps show a harmonized picture of the spatial distribution of fire danger level. Drought code Dimensionless The Drought code is an indicator of the moisture content in deep compact organic layers. This code represents a fuel layer at approximately 10-20 cm deep. The Drought code fuels have a very slow drying rate, with a time lag of 52 days. The Drought code scale is open-ended, although the maximum value is about 800. Duff moisture code Dimensionless The Duff moisture code is an indicatore of the moisture content in loosely-compacted organic layers of moderate depth. It is representative of the duff layer that is 5-10 cm deep. Duff moisture code fuels are affected by rain, temperature and relative humidity. Because these fuels are below the forest floor surface, wind speed does not affect the fuel moisture content. The Duff moisture code fuels have a slower drying rate than the Fine fuel moisture code fuels, with a timelag of 12 days. Although the Duff moisture code has an open-ended scale, the highest probable value is in the range of 150. Energy release component J/m2 The Energy release component is a number related to the available energy (British Thermal Unit) per unit area (square foot) within the flaming front at the head of a fire. Daily variations in Energy release component are due to changes in moisture content of the various fuels present, both live and dead. Since this number represents the potential "heat release" per unit area in the flaming zone, it can provide guidance to several important fire activities. It may also be considered a composite fuel moisture value as it reflects the contribution that all live and dead fuels have to potential fire intensity. The Energy release component is a cumulative or "build-up" type of index. As live fuels cure and dead fuels dry, the Energy release component values get higher thus providing a good reflection of drought conditions. The scale is open-ended or unlimited and, as with other National Forest Danger Rating System components, is relative. Fine fuel moisture code Dimensionless The Fine fuel moisture code is an indicatore of the moisture content in litter and other cured fine fuels (needles, mosses, twigs less than 1 cm in diameter). The Fine fuel moisture code is representative of the top litter layer less than 1-2 cm deep. Fine fuel moisture code values change rapidly because of a high surface area to volume ratio, and direct exposure to changing environmental conditions. The Fine fuel moisture code scale ranges from 0-99 and is the only component of the Fire weather index system which does not have an open-ended scale. Generally, fires begin to ignite at Fine fuel moisture code values near 70, and the maximum probable value that will ever be achieved is 96. Fire daily severity index Dimensionless Numeric rating of the difficulty of controlling fires. It is an exponential transformation of the Fire weather index and more accurately reflects the expected efforts required for fire suppression as it increases exponentially as the Fire weather index is above a certain value. Fire danger index Dimensionless The Fire danger index is a metric related to the chances of a fire starting, its rate of spread, its intensity, and its difficulty of suppression. It is open ended however a value of 50 and above is considered extreme in most vegetation Fire weather index Dimensionless The Fire weather index is a combination of Initial spread index and Build-up index, and is a numerical rating of the potential frontal fire intensity. In effect, it indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed. Fire weather index values are not upper bounded however a value of 50 is considered as extreme in many places. The Fire weather index is used for general public information about fire danger conditions. Ignition component % The Ignition component measures the probability a firebrand will require suppression action. Since it is expressed as a probability, it ranges on a scale of 0 to 100. An Ignition component of 100 means that every firebrand will cause a fire requiring action if it contacts a receptive fuel. Likewise an Ignition component of 0 would mean that no firebrand would cause a fire requiring suppression action under those conditions. Initial spread index Dimensionless The Initial spread index combines the Fine fuel moisture code and wind speed to indicate the expected rate of fire spread. Generally, a 13 km h-1 increase in wind speed will double the Initial spread index value. The Initial spread index is accepted as a good indicator of fire spread in open light fuel stands with wind speeds up to 40 km h-1. Keetch-Byram drought index Dimensionless The Keetch-Byram drought index (KBDI) is a number representing the net effect of evapotranspiration and precipitation in producing cumulative moisture deficiency in deep duff and upper soil layers. It is a continuous index, relating to the flammability of organic material in the ground.The Keetch-Byram drought index attempts to measure the amount of precipitation necessary to return the soil to saturated conditions. It is a closed system ranging from 0 to 200 units and represents a moisture regime from 0 to 20 cm of water through the soil layer. At 20 cm of water, the Keetch-Byram drought index assumes saturation. Zero is the point of no moisture deficiency and 200 is the maximum drought that is possible. At any point along the scale, the index number indicates the amount of net rainfall that is required to reduce the index to zero, or saturation. The inputs for Keetch-Byram drought index are weather station latitude, mean annual precipitation, maximum dry bulb temperature, and the last 24 hours of rainfall. Reduction in drought occurs only when rainfall exceeds 5 mm (called net rainfall). The computational steps involve reducing the drought index by the net rain amount and increasing the drought index by a drought factor. KBDI = 0 - 50: Soil moisture and large class fuel moistures are high and do not contribute much to fire intensity. Typical of spring dormant season following winter precipitation. KBDI = 50 - 100: Typical of late spring, early growing season. Lower litter and duff layers are drying and beginning to contribute to fire intensity. KBDI = 100 - 150: Typical of late summer, early fall. Lower litter and duff layers actively contribute to fire intensity and will burn actively. KBDI = 150 - 200: Often associated with more severe drought with increased wildfire occurrence. Intense, deep burning fires with significant downwind spotting can be expected. Live fuels can also be expected to burn actively at these levels. Spread component Dimensionless The Spread component is a measure of the spead at which a headfire would spread. The spread component is numerically equal to the theoretical ideal rate of spread expressed in feet-per-minute however is considered as a dimensionless variable. The Spread component is expressed on an open-ended scale; thus it has no upper limit. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Build-up index Dimensionless The Build-Up Index is a weighted combination of the Duff moisture code and Drought code to indicate the total amount of fuel available for combustion by a moving flame front. The Duff moisture code has the most influence on the Build-up index value. For example, a Duff moisture code value of zero always results in a Build-up index value of zero regardless of what the Drought code value is. The Drought code has the strongest influence on the Build-up index when Duff moisture code values are high. The greatest effect that the Drought code can have is to make the Build-up index value equal to twice the Duff moisture code value. The Build-up index is often used for pre-suppression planning purposes. Build-up index Dimensionless The Build-Up Index is a weighted combination of the Duff moisture code and Drought code to indicate the total amount of fuel available for combustion by a moving flame front. The Duff moisture code has the most influence on the Build-up index value. For example, a Duff moisture code value of zero always results in a Build-up index value of zero regardless of what the Drought code value is. The Drought code has the strongest influence on the Build-up index when Duff moisture code values are high. The greatest effect that the Drought code can have is to make the Build-up index value equal to twice the Duff moisture code value. The Build-up index is often used for pre-suppression planning purposes. Burning index Dimensionless The Burning Index measures the difficulty of controlling a fire. It is derived from a combination of Spread component (how fast it will spread) and Energy release component (how much energy will be produced). In this way, it is related to flame length, which, in the Fire Behavior Prediction System, is based on rate of spread and heat per unit area. However, because of differences in the calculations for Burning index and flame length, they are not the same. Burning index Dimensionless The Burning Index measures the difficulty of controlling a fire. It is derived from a combination of Spread component (how fast it will spread) and Energy release component (how much energy will be produced). In this way, it is related to flame length, which, in the Fire Behavior Prediction System, is based on rate of spread and heat per unit area. However, because of differences in the calculations for Burning index and flame length, they are not the same. Danger rating Dimensionless The Danger rating is equivalent to the FWI reduced to 6 classes of danger, accordingly to EFFIS danger class levels definition (very low, low, medium, high, very high and extreme). The fire danger classes are the same for all countries so maps show a harmonized picture of the spatial distribution of fire danger level. Danger rating Dimensionless The Danger rating is equivalent to the FWI reduced to 6 classes of danger, accordingly to EFFIS danger class levels definition (very low, low, medium, high, very high and extreme). The fire danger classes are the same for all countries so maps show a harmonized picture of the spatial distribution of fire danger level. Drought code Dimensionless The Drought code is an indicator of the moisture content in deep compact organic layers. This code represents a fuel layer at approximately 10-20 cm deep. The Drought code fuels have a very slow drying rate, with a time lag of 52 days. The Drought code scale is open-ended, although the maximum value is about 800. Drought code Dimensionless The Drought code is an indicator of the moisture content in deep compact organic layers. This code represents a fuel layer at approximately 10-20 cm deep. The Drought code fuels have a very slow drying rate, with a time lag of 52 days. The Drought code scale is open-ended, although the maximum value is about 800. Duff moisture code Dimensionless The Duff moisture code is an indicatore of the moisture content in loosely-compacted organic layers of moderate depth. It is representative of the duff layer that is 5-10 cm deep. Duff moisture code fuels are affected by rain, temperature and relative humidity. Because these fuels are below the forest floor surface, wind speed does not affect the fuel moisture content. The Duff moisture code fuels have a slower drying rate than the Fine fuel moisture code fuels, with a timelag of 12 days. Although the Duff moisture code has an open-ended scale, the highest probable value is in the range of 150. Duff moisture code Dimensionless The Duff moisture code is an indicatore of the moisture content in loosely-compacted organic layers of moderate depth. It is representative of the duff layer that is 5-10 cm deep. Duff moisture code fuels are affected by rain, temperature and relative humidity. Because these fuels are below the forest floor surface, wind speed does not affect the fuel moisture content. The Duff moisture code fuels have a slower drying rate than the Fine fuel moisture code fuels, with a timelag of 12 days. Although the Duff moisture code has an open-ended scale, the highest probable value is in the range of 150. Energy release component J/m2 The Energy release component is a number related to the available energy (British Thermal Unit) per unit area (square foot) within the flaming front at the head of a fire. Daily variations in Energy release component are due to changes in moisture content of the various fuels present, both live and dead. Since this number represents the potential "heat release" per unit area in the flaming zone, it can provide guidance to several important fire activities. It may also be considered a composite fuel moisture value as it reflects the contribution that all live and dead fuels have to potential fire intensity. The Energy release component is a cumulative or "build-up" type of index. As live fuels cure and dead fuels dry, the Energy release component values get higher thus providing a good reflection of drought conditions. The scale is open-ended or unlimited and, as with other National Forest Danger Rating System components, is relative. Energy release component J/m2 The Energy release component is a number related to the available energy (British Thermal Unit) per unit area (square foot) within the flaming front at the head of a fire. Daily variations in Energy release component are due to changes in moisture content of the various fuels present, both live and dead. Since this number represents the potential "heat release" per unit area in the flaming zone, it can provide guidance to several important fire activities. It may also be considered a composite fuel moisture value as it reflects the contribution that all live and dead fuels have to potential fire intensity. The Energy release component is a cumulative or "build-up" type of index. As live fuels cure and dead fuels dry, the Energy release component values get higher thus providing a good reflection of drought conditions. The scale is open-ended or unlimited and, as with other National Forest Danger Rating System components, is relative. Fine fuel moisture code Dimensionless The Fine fuel moisture code is an indicatore of the moisture content in litter and other cured fine fuels (needles, mosses, twigs less than 1 cm in diameter). The Fine fuel moisture code is representative of the top litter layer less than 1-2 cm deep. Fine fuel moisture code values change rapidly because of a high surface area to volume ratio, and direct exposure to changing environmental conditions. The Fine fuel moisture code scale ranges from 0-99 and is the only component of the Fire weather index system which does not have an open-ended scale. Generally, fires begin to ignite at Fine fuel moisture code values near 70, and the maximum probable value that will ever be achieved is 96. Fine fuel moisture code Dimensionless The Fine fuel moisture code is an indicatore of the moisture content in litter and other cured fine fuels (needles, mosses, twigs less than 1 cm in diameter). The Fine fuel moisture code is representative of the top litter layer less than 1-2 cm deep. Fine fuel moisture code values change rapidly because of a high surface area to volume ratio, and direct exposure to changing environmental conditions. The Fine fuel moisture code scale ranges from 0-99 and is the only component of the Fire weather index system which does not have an open-ended scale. Generally, fires begin to ignite at Fine fuel moisture code values near 70, and the maximum probable value that will ever be achieved is 96. Fire daily severity index Dimensionless Numeric rating of the difficulty of controlling fires. It is an exponential transformation of the Fire weather index and more accurately reflects the expected efforts required for fire suppression as it increases exponentially as the Fire weather index is above a certain value. Fire daily severity index Dimensionless Numeric rating of the difficulty of controlling fires. It is an exponential transformation of the Fire weather index and more accurately reflects the expected efforts required for fire suppression as it increases exponentially as the Fire weather index is above a certain value. Fire danger index Dimensionless The Fire danger index is a metric related to the chances of a fire starting, its rate of spread, its intensity, and its difficulty of suppression. It is open ended however a value of 50 and above is considered extreme in most vegetation Fire danger index Dimensionless The Fire danger index is a metric related to the chances of a fire starting, its rate of spread, its intensity, and its difficulty of suppression. It is open ended however a value of 50 and above is considered extreme in most vegetation Fire weather index Dimensionless The Fire weather index is a combination of Initial spread index and Build-up index, and is a numerical rating of the potential frontal fire intensity. In effect, it indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed. Fire weather index values are not upper bounded however a value of 50 is considered as extreme in many places. The Fire weather index is used for general public information about fire danger conditions. Fire weather index Dimensionless The Fire weather index is a combination of Initial spread index and Build-up index, and is a numerical rating of the potential frontal fire intensity. In effect, it indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed. Fire weather index values are not upper bounded however a value of 50 is considered as extreme in many places. The Fire weather index is used for general public information about fire danger conditions. Ignition component % The Ignition component measures the probability a firebrand will require suppression action. Since it is expressed as a probability, it ranges on a scale of 0 to 100. An Ignition component of 100 means that every firebrand will cause a fire requiring action if it contacts a receptive fuel. Likewise an Ignition component of 0 would mean that no firebrand would cause a fire requiring suppression action under those conditions. Ignition component % The Ignition component measures the probability a firebrand will require suppression action. Since it is expressed as a probability, it ranges on a scale of 0 to 100. An Ignition component of 100 means that every firebrand will cause a fire requiring action if it contacts a receptive fuel. Likewise an Ignition component of 0 would mean that no firebrand would cause a fire requiring suppression action under those conditions. Initial spread index Dimensionless The Initial spread index combines the Fine fuel moisture code and wind speed to indicate the expected rate of fire spread. Generally, a 13 km h-1 increase in wind speed will double the Initial spread index value. The Initial spread index is accepted as a good indicator of fire spread in open light fuel stands with wind speeds up to 40 km h-1. Initial spread index Dimensionless The Initial spread index combines the Fine fuel moisture code and wind speed to indicate the expected rate of fire spread. Generally, a 13 km h-1 increase in wind speed will double the Initial spread index value. The Initial spread index is accepted as a good indicator of fire spread in open light fuel stands with wind speeds up to 40 km h-1. Keetch-Byram drought index Dimensionless The Keetch-Byram drought index (KBDI) is a number representing the net effect of evapotranspiration and precipitation in producing cumulative moisture deficiency in deep duff and upper soil layers. It is a continuous index, relating to the flammability of organic material in the ground.The Keetch-Byram drought index attempts to measure the amount of precipitation necessary to return the soil to saturated conditions. It is a closed system ranging from 0 to 200 units and represents a moisture regime from 0 to 20 cm of water through the soil layer. At 20 cm of water, the Keetch-Byram drought index assumes saturation. Zero is the point of no moisture deficiency and 200 is the maximum drought that is possible. At any point along the scale, the index number indicates the amount of net rainfall that is required to reduce the index to zero, or saturation. The inputs for Keetch-Byram drought index are weather station latitude, mean annual precipitation, maximum dry bulb temperature, and the last 24 hours of rainfall. Reduction in drought occurs only when rainfall exceeds 5 mm (called net rainfall). The computational steps involve reducing the drought index by the net rain amount and increasing the drought index by a drought factor. KBDI = 0 - 50: Soil moisture and large class fuel moistures are high and do not contribute much to fire intensity. Typical of spring dormant season following winter precipitation. KBDI = 50 - 100: Typical of late spring, early growing season. Lower litter and duff layers are drying and beginning to contribute to fire intensity. KBDI = 100 - 150: Typical of late summer, early fall. Lower litter and duff layers actively contribute to fire intensity and will burn actively. KBDI = 150 - 200: Often associated with more severe drought with increased wildfire occurrence. Intense, deep burning fires with significant downwind spotting can be expected. Live fuels can also be expected to burn actively at these levels. Keetch-Byram drought index Dimensionless The Keetch-Byram drought index (KBDI) is a number representing the net effect of evapotranspiration and precipitation in producing cumulative moisture deficiency in deep duff and upper soil layers. It is a continuous index, relating to the flammability of organic material in the ground.The Keetch-Byram drought index attempts to measure the amount of precipitation necessary to return the soil to saturated conditions. It is a closed system ranging from 0 to 200 units and represents a moisture regime from 0 to 20 cm of water through the soil layer. At 20 cm of water, the Keetch-Byram drought index assumes saturation. Zero is the point of no moisture deficiency and 200 is the maximum drought that is possible. At any point along the scale, the index number indicates the amount of net rainfall that is required to reduce the index to zero, or saturation. The inputs for Keetch-Byram drought index are weather station latitude, mean annual precipitation, maximum dry bulb temperature, and the last 24 hours of rainfall. Reduction in drought occurs only when rainfall exceeds 5 mm (called net rainfall). The computational steps involve reducing the drought index by the net rain amount and increasing the drought index by a drought factor. KBDI = 0 - 50: Soil moisture and large class fuel moistures are high and do not contribute much to fire intensity. Typical of spring dormant season following winter precipitation. KBDI = 50 - 100: Typical of late spring, early growing season. Lower litter and duff layers are drying and beginning to contribute to fire intensity. KBDI = 100 - 150: Typical of late summer, early fall. Lower litter and duff layers actively contribute to fire intensity and will burn actively. KBDI = 150 - 200: Often associated with more severe drought with increased wildfire occurrence. Intense, deep burning fires with significant downwind spotting can be expected. Live fuels can also be expected to burn actively at these levels. The Keetch-Byram drought index (KBDI) is a number representing the net effect of evapotranspiration and precipitation in producing cumulative moisture deficiency in deep duff and upper soil layers. It is a continuous index, relating to the flammability of organic material in the ground.The Keetch-Byram drought index attempts to measure the amount of precipitation necessary to return the soil to saturated conditions. It is a closed system ranging from 0 to 200 units and represents a moisture regime from 0 to 20 cm of water through the soil layer. At 20 cm of water, the Keetch-Byram drought index assumes saturation. Zero is the point of no moisture deficiency and 200 is the maximum drought that is possible. At any point along the scale, the index number indicates the amount of net rainfall that is required to reduce the index to zero, or saturation. The inputs for Keetch-Byram drought index are weather station latitude, mean annual precipitation, maximum dry bulb temperature, and the last 24 hours of rainfall. Reduction in drought occurs only when rainfall exceeds 5 mm (called net rainfall). The computational steps involve reducing the drought index by the net rain amount and increasing the drought index by a drought factor. KBDI = 0 - 50: Soil moisture and large class fuel moistures are high and do not contribute much to fire intensity. Typical of spring dormant season following winter precipitation. KBDI = 50 - 100: Typical of late spring, early growing season. Lower litter and duff layers are drying and beginning to contribute to fire intensity. KBDI = 100 - 150: Typical of late summer, early fall. Lower litter and duff layers actively contribute to fire intensity and will burn actively. KBDI = 150 - 200: Often associated with more severe drought with increased wildfire occurrence. Intense, deep burning fires with significant downwind spotting can be expected. Live fuels can also be expected to burn actively at these levels. Spread component Dimensionless The Spread component is a measure of the spead at which a headfire would spread. The spread component is numerically equal to the theoretical ideal rate of spread expressed in feet-per-minute however is considered as a dimensionless variable. The Spread component is expressed on an open-ended scale; thus it has no upper limit. Spread component Dimensionless The Spread component is a measure of the spead at which a headfire would spread. The spread component is numerically equal to the theoretical ideal rate of spread expressed in feet-per-minute however is considered as a dimensionless variable. The Spread component is expressed on an open-ended scale; thus it has no upper limit. 158 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/iberia-biscay-ireland-sea-level-extreme-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_VAR_EXTREME_SL_IBI_slev_mean_and_anomaly_obs Iberia Biscay Ireland Sea Level extreme from Observations Reprocessing DEFINITION The OMI_VAR_EXTREME_SL_IBI_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_var_extreme_sl_ibi_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). CONTEXT Sea level is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990’s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one metre by the end of the century (Vousdoukas et al., 2020). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves. CMEMS KEY FINDINGS The completeness index criteria is fulfilled by 52 stations, a significant increase with respect to those available in 2019 (17). Most of these new stations belong to UK, Ireland and France, and their reprocessed timeseries are now provided in product INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053. The mean 99th percentiles reflect the great tide spatial variability around the UK and the north of France. Minimum values are obseved in the Irish coast (e.g.: 0.66 m above mean sea level in Arklow Harbour), South of England (e.g.: 0.70 m above mean sea level in Bournemouth), and the Canary Islands (e.g.: 0.96 m above mean sea level in Hierro). Maximum values are observed in the Bristol and English Channels (e.g.: 6.25 m and 5.16 m above mean sea level in Newport and St. Helier, respectively). The standard deviation reflects the south-north increase of storminess, ranging between 2 cm in the Canary Islands to 12 cm in Newport. Positive anomalies of 2020 99th percentile are observed for most of the stations, increasing northwards from 1-2 cm in the Canary Islands to 16 cm in Workington (Irish Sea). A negative anomaly of -3 cm is observed in Bonanza (Gulf of Cadiz, Guadalquivir river mouth). DOI (product):https://doi.org/10.48670/moi-00253 https://doi.org/10.48670/moi-00253 159 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-2012-raster-100-m-europe-6-yearly https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012 CORINE Land Cover 2012 (raster 100 m), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover 2012 (CLC2012) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2012. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 160 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-2012-vector-europe-6-yearly-version https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012 CORINE Land Cover 2012 (vector), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover 2012 (CLC2012) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2012. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-co…. https://land.copernicus.eu/user-corner/technical-library/ https://land.copernicus.eu/user-corner/technical-library/corine-land-co… 161 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-classified-change-2006-2009-raster-20-m https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/change-maps/2006-2009/classified-change/view Imperviousness Classified Change 2006-2009 (raster 20 m), Europe, 3-yearly, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012 and 2015 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 162 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-classified-change-2009-2012-raster-20-m https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/change-maps/2009-2012/classified-change/view Imperviousness Classified Change 2009-2012 (raster 20 m), Europe, 3-yearly, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012 and 2015 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 163 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-classified-change-2012-2015-raster-20-m https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/change-maps/2012-2015/classified-change/view Imperviousness Classified Change 2012-2015 (raster 20 m), Europe, 3-yearly, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012 and 2015 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 164 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-classified-change-2006-2012-raster-20-m https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/change-maps/2006-2012-clc-synchronous/classified-change/view Imperviousness Classified Change 2006-2012 (raster 20 m), Europe, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012 and 2015 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 165 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-sea-ice-edge-type https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-ice-edge-type satellite-sea-ice-edge-type This dataset provides daily gridded data of sea ice edge and sea ice type derived from brightness temperatures measured by satellite passive microwave radiometers. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its presence or its retreat has a strong impact on air-sea interactions, the Earth’s energy budget as well as marine ecosystems. It is recognized by the Global Climate Observing System as an Essential Climate Variable. Sea ice edge and type are some of the parameters used to characterise sea ice. Other parameters include sea ice concentration and sea ice thickness, also available in the Climate Data Store. Sea ice edge and type are defined as follows: Sea ice edge classifies the sea surface into open water, open ice, and closed ice depending on the amount of sea ice present in each grid cell. This variable is provided for both the Northern and Southern Hemispheres. Note that a sea ice concentration threshold of 30% is used to distinguish between open water and open ice, which differs from the 15% threshold commonly used for other sea ice products such as sea ice extent. Sea ice type classifies ice-covered areas into two categories based on the age of the sea ice: multiyear ice versus seasonal first-year ice. This variable is currently only available for the Northern Hemisphere and limited to the extended boreal winter months (mid-October through April). Sea ice type classification during summer is difficult due to the effect of melting at the ice surface which disturbs the passive microwave signature. Sea ice edge classifies the sea surface into open water, open ice, and closed ice depending on the amount of sea ice present in each grid cell. This variable is provided for both the Northern and Southern Hemispheres. Note that a sea ice concentration threshold of 30% is used to distinguish between open water and open ice, which differs from the 15% threshold commonly used for other sea ice products such as sea ice extent. Sea ice type classifies ice-covered areas into two categories based on the age of the sea ice: multiyear ice versus seasonal first-year ice. This variable is currently only available for the Northern Hemisphere and limited to the extended boreal winter months (mid-October through April). Sea ice type classification during summer is difficult due to the effect of melting at the ice surface which disturbs the passive microwave signature. Both sea ice products are based on measurements from the series of Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSMIS) sensors and share the same algorithm baseline. However, sea ice edge makes use of two lower frequencies near 19 GHz and 37 GHz and a higher frequency near 90 GHz whereas sea ice type only uses the two lower frequencies. This dataset combines Climate Data Records (CDRs), which are intended to have sufficient length, consistency, and continuity to assess climate variability and change, and Interim Climate Data Records (ICDRs), which provide regular temporal extensions to the CDRs and where consistency with the CDRs is expected but not extensively checked. For this dataset, both the CDR and ICDR parts of each product were generated using the same software and algorithms. The CDRs of sea ice edge and type currently extend from 25 October 1978 to 31 December 2020 whereas the corresponding ICDRs extend from January 2021 to present (with a 16-day latency behind real time). All data from the current release of the datasets (version 2.0) are Level-4 products, in which data gaps are filled by temporal and spatial interpolation. For product limitations and known issues, please consult the Product User Guide. This dataset is produced on behalf of Copernicus Climate Change Service (C3S), with heritage from the operational products generated by EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF). DATA DESCRIPTION Data type Gridded Projection Lambert Azimuthal Equal Area (EASE-Grid version 2.0) centred over the poles Horizontal coverage Sea ice edge: Northern and Southern Hemispheres Sea ice type: Northern Hemisphere only Horizontal resolution Sea ice edge: 12.5 km grid resolution (true spatial resolution as resolved by sensor: ~15 km) Sea ice type: 25 km grid resolution (true spatial resolution as resolved by sensor: 30-60 km) Vertical coverage Surface Vertical resolution Single level Temporal coverage 25 October 1978 to present (sea ice type available only from mid-October to April) Temporal resolution Daily (every second day prior to 9 July 1987) File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.7, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions v2.0: Level 4 product; based on updated input data; includes temperature-based correction scheme for sea ice type and upgraded processing flags. v1.0: Level 3 product; covers the period from 1 January 1979 to September 2021; no longer updated. Update frequency Daily (with a 16-day latency) DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert Azimuthal Equal Area (EASE-Grid version 2.0) centred over the poles Projection Lambert Azimuthal Equal Area (EASE-Grid version 2.0) centred over the poles Horizontal coverage Sea ice edge: Northern and Southern Hemispheres Sea ice type: Northern Hemisphere only Horizontal coverage Sea ice edge: Northern and Southern Hemispheres Sea ice type: Northern Hemisphere only Horizontal resolution Sea ice edge: 12.5 km grid resolution (true spatial resolution as resolved by sensor: ~15 km) Sea ice type: 25 km grid resolution (true spatial resolution as resolved by sensor: 30-60 km) Horizontal resolution Sea ice edge: 12.5 km grid resolution (true spatial resolution as resolved by sensor: ~15 km) Sea ice type: 25 km grid resolution (true spatial resolution as resolved by sensor: 30-60 km) Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage 25 October 1978 to present (sea ice type available only from mid-October to April) Temporal coverage 25 October 1978 to present (sea ice type available only from mid-October to April) Temporal resolution Daily (every second day prior to 9 July 1987) Temporal resolution Daily (every second day prior to 9 July 1987) File format NetCDF 4 File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.7, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.7, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions v2.0: Level 4 product; based on updated input data; includes temperature-based correction scheme for sea ice type and upgraded processing flags. v1.0: Level 3 product; covers the period from 1 January 1979 to September 2021; no longer updated. Versions v2.0: Level 4 product; based on updated input data; includes temperature-based correction scheme for sea ice type and upgraded processing flags. v1.0: Level 3 product; covers the period from 1 January 1979 to September 2021; no longer updated. Update frequency Daily (with a 16-day latency) Update frequency Daily (with a 16-day latency) MAIN VARIABLES Name Units Description Sea ice edge Dimensionless Classification of the sea surface based on sea ice concentration (SIC) values. Possible values are: 1: open-water (SIC < 30%) 2: open ice (30% < SIC < 70%) 3: closed ice (SIC > 70%) Sea ice type Dimensionless Classification of the sea ice type. Possible values are: 1: no ice 2: first-year ice 3: multi-year ice 4: ambiguous ice type First-year ice corresponds to seasonal ice that has formed since the previous melting season, whereas multi-year ice corresponds to older ice that has survived at least one melting season. Ambiguous ice is ice with undetermined classification. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Sea ice edge Dimensionless Classification of the sea surface based on sea ice concentration (SIC) values. Possible values are: 1: open-water (SIC < 30%) 2: open ice (30% < SIC < 70%) 3: closed ice (SIC > 70%) Sea ice edge Dimensionless Classification of the sea surface based on sea ice concentration (SIC) values. Possible values are: 1: open-water (SIC < 30%) 2: open ice (30% < SIC < 70%) 3: closed ice (SIC > 70%) Sea ice type Dimensionless Classification of the sea ice type. Possible values are: 1: no ice 2: first-year ice 3: multi-year ice 4: ambiguous ice type First-year ice corresponds to seasonal ice that has formed since the previous melting season, whereas multi-year ice corresponds to older ice that has survived at least one melting season. Ambiguous ice is ice with undetermined classification. Sea ice type Dimensionless Classification of the sea ice type. Possible values are: 1: no ice 2: first-year ice 3: multi-year ice 4: ambiguous ice type First-year ice corresponds to seasonal ice that has formed since the previous melting season, whereas multi-year ice corresponds to older ice that has survived at least one melting season. Ambiguous ice is ice with undetermined classification. RELATED VARIABLES Name Units Description Status flag Dimensionless Status flag detailing what filters or processing steps were applied to each pixel in the classification retrievals. See Product User Guide for details. Uncertainty Dimensionless Statistical uncertainty of the sea ice classification. Values range from 0 to 1. See Product User Guide for details. RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Status flag Dimensionless Status flag detailing what filters or processing steps were applied to each pixel in the classification retrievals. See Product User Guide for details. Status flag Dimensionless Status flag detailing what filters or processing steps were applied to each pixel in the classification retrievals. See Product User Guide for details. Uncertainty Dimensionless Statistical uncertainty of the sea ice classification. Values range from 0 to 1. See Product User Guide for details. Uncertainty Dimensionless Statistical uncertainty of the sea ice classification. Values range from 0 to 1. See Product User Guide for details. 166 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-low-and-mid-trophic-levels-biomass-content http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_MULTIYEAR_BGC_001_033 Global ocean low and mid trophic levels biomass content hindcast Short description: The Low and Mid-Trophic Levels (LMTL) reanalysis for global ocean is produced at [https://www.cls.fr CLS] on behalf of Global Ocean Marine Forecasting Center. It provides 2D fields of biomass content of zooplankton and six functional groups of micronekton. It uses the LMTL component of SEAPODYM dynamical population model (http://www.seapodym.eu). No data assimilation has been done. This product also contains forcing data: net primary production, euphotic depth, depth of each pelagic layers zooplankton and micronekton inhabit, average temperature and currents over pelagic layers. https://www.cls.fr http://www.seapodym.eu Forcings sources: * Ocean currents and temperature (CMEMS multiyear product) * Net Primary Production computed from chlorophyll a, Sea Surface Temperature and Photosynthetically Active Radiation observations (chlorophyll from CMEMS multiyear product, SST from NOAA NCEI AVHRR-only Reynolds, PAR from INTERIM) and relaxed by model outputs at high latitudes (CMEMS biogeochemistry multiyear product) Vertical coverage: * Epipelagic layer * Upper mesopelagic layer * Lower mesopelagic layer (max. 1000m) DOI (product) :https://doi.org/10.48670/moi-00020 https://doi.org/10.48670/moi-00020 167 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-carra-height-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-carra-height-levels reanalysis-carra-height-levels The C3S Arctic Regional Reanalysis (CARRA) dataset contains 3-hourly analyses and hourly short term forecasts of atmospheric and surface meteorological variables (temperature, humidity, wind, cloud and pressure) at 2.5 km resolution. Additionally, forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC are available. The dataset includes two domains. The West domain covers Greenland, the Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, the Greenland Sea, and parts of Svalbard. The East domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia. The dataset has been produced with the use of the HARMONIE-AROME state-of-the-art non-hydrostatic regional numerical weather prediction model. High resolution reanalysis for the Arctic region is particularly important because the climate change is more pronounced in the Arctic region than elsewhere in the Earth. This fact calls for a better description of this region providing additional details with respect to the global reanalyses (ERA5 for instance). The additional information is provided by the higher horizontal resolution, more local observations (from the Nordic countries and Greenland), better description of surface characteristics (high resolution satellite and physiographic data), high resolution non-hydrostatic dynamics and improved physical parameterisation of clouds and precipitation in particular. The inputs to CARRA reanalysis are the observations, the ERA5 global reanalysis as lateral boundary conditions and the physiographic datasets describing the surface characteristics of the model. The observation values and information about their quality are used together to constrain the reanalysis where observations are available and provide information for the data assimilation system in areas in where less observations are available. More details about the reanalysis dataset and the extensive input data are given in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Horizontal coverage West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia Horizontal resolution 2.5km x 2.5km Vertical coverage From a height of 15 m to a height of 500 m above the surface Vertical resolution 11 specific height levels are included: 15, 30, 50, 75, 100, 150, 200, 250, 300, 400 and 500 m above the surface Temporal coverage From 1991 to present Temporal resolution 3-hourly analysis data. Hourly forecast data File format GRIB2 Update frequency Monthly. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Projection Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Horizontal coverage West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia Horizontal coverage West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia Horizontal resolution 2.5km x 2.5km Horizontal resolution 2.5km x 2.5km Vertical coverage From a height of 15 m to a height of 500 m above the surface Vertical coverage From a height of 15 m to a height of 500 m above the surface Vertical resolution 11 specific height levels are included: 15, 30, 50, 75, 100, 150, 200, 250, 300, 400 and 500 m above the surface Vertical resolution 11 specific height levels are included: 15, 30, 50, 75, 100, 150, 200, 250, 300, 400 and 500 m above the surface Temporal coverage From 1991 to present Temporal coverage From 1991 to present Temporal resolution 3-hourly analysis data. Hourly forecast data Temporal resolution 3-hourly analysis data. Hourly forecast data File format GRIB2 File format GRIB2 Update frequency Monthly. Update frequency Monthly. MAIN VARIABLES Name Units Description Pressure Pa The atmospheric pressure interpolated to the specific height level. Relative humidity % Relation between actual humidity and saturation humidity. Values are in the interval [0, 100]. 0% means that the air in the height level is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. Specific cloud ice water content kg kg-1 Mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud liquid water content kg kg-1 Mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Temperature K Air temperature at the height level. Wind direction Degrees Average wind direction interpolated to the specific height level. The wind direction is the direction from which the wind comes. Values are in the interval [0, 360]. A value of 0° means a northerly wind and 90° indicates an easterly wind. Wind speed m s-1 Average wind speed interpolated to the specific height level. It is computed from the zonal (u) and the meridional (v) wind components by sqrt( u2 + v2 ). MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Pressure Pa The atmospheric pressure interpolated to the specific height level. Pressure Pa The atmospheric pressure interpolated to the specific height level. Relative humidity % Relation between actual humidity and saturation humidity. Values are in the interval [0, 100]. 0% means that the air in the height level is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. Relative humidity % Relation between actual humidity and saturation humidity. Values are in the interval [0, 100]. 0% means that the air in the height level is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. Specific cloud ice water content kg kg-1 Mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud ice water content kg kg-1 Mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud liquid water content kg kg-1 Mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud liquid water content kg kg-1 Mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Temperature K Air temperature at the height level. Temperature K Air temperature at the height level. Wind direction Degrees Average wind direction interpolated to the specific height level. The wind direction is the direction from which the wind comes. Values are in the interval [0, 360]. A value of 0° means a northerly wind and 90° indicates an easterly wind. Wind direction Degrees Average wind direction interpolated to the specific height level. The wind direction is the direction from which the wind comes. Values are in the interval [0, 360]. A value of 0° means a northerly wind and 90° indicates an easterly wind. Wind speed m s-1 Average wind speed interpolated to the specific height level. It is computed from the zonal (u) and the meridional (v) wind components by sqrt( u2 + v2 ). Wind speed m s-1 Average wind speed interpolated to the specific height level. It is computed from the zonal (u) and the meridional (v) wind components by sqrt( u2 + v2 ). 168 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-change-2006-2012-raster-100-m-europe-apr https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/change-maps/2006-2012-clc-synchronous/degree-change/view Imperviousness Change 2006-2012 (raster 100 m), Europe, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 169 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-change-2012-2015-raster-20-m-europe-3 https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/change-maps/2012-2015/degree-change/view Imperviousness Change 2012-2015 (raster 20 m), Europe, 3-yearly, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 170 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-change-2006-2009-raster-20-m-europe-3 https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/change-maps/2006-2009/degree-change/view Imperviousness Change 2006-2009 (raster 20 m), Europe, 3-yearly, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 171 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-density-2012-raster-20-m-europe-3-yearly https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/2012/view Imperviousness Density 2012 (raster 20 m), Europe, 3-yearly, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 172 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-density-2006-raster-100-m-europe-3-yearly https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/2006/view Imperviousness Density 2006 (raster 100 m), Europe, 3-yearly, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 173 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-density-2015-raster-100-m-europe-3-yearly https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/2015/view Imperviousness Density 2015 (raster 100 m), Europe, 3-yearly, Marc. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 174 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-change-2009-2012-raster-100-m-europe-3 https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/change-maps/2009-2012/degree-change/view Imperviousness Change 2009-2012 (raster 100 m), Europe, 3-yearly, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 175 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-change-2009-2012-raster-20-m-europe-3 https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/change-maps/2009-2012/degree-change/view Imperviousness Change 2009-2012 (raster 20 m), Europe, 3-yearly, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 176 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-density-2015-raster-20-m-europe-3-yearly https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/2015/view Imperviousness Density 2015 (raster 20 m), Europe, 3-yearly, Marc. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 177 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-density-2012-raster-100-m-europe-3-yearly https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/2012/view Imperviousness Density 2012 (raster 100 m), Europe, 3-yearly, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 178 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-density-2009-raster-20-m-europe-3-yearly https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/2009/view Imperviousness Density 2009 (raster 20 m), Europe, 3-yearly, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 179 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-density-2009-raster-100-m-europe-3-yearly https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/2009/view Imperviousness Density 2009 (raster 100 m), Europe, 3-yearly, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 180 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-change-2012-2015-raster-100-m-europe-3 https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/change-maps/2012-2015/degree-change/view Imperviousness Change 2012-2015 (raster 100 m), Europe, 3-yearly, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 181 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-change-2006-2012-raster-20-m-europe-apr https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/change-maps/2006-2012-clc-synchronous/degree-change/view Imperviousness Change 2006-2012 (raster 20 m), Europe, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 182 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-change-2006-2009-raster-100-m-europe-3 https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/change-maps/2006-2009/degree-change/view Imperviousness Change 2006-2009 (raster 100 m), Europe, 3-yearly, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 183 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-density-2006-raster-20-m-europe-3-yearly https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/2006/view Imperviousness Density 2006 (raster 20 m), Europe, 3-yearly, Apr. 2018 The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. 184 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/water-bodies-2020-present-raster-100-m-global-monthly https://land.copernicus.eu/global/products/wb Water Bodies 2020-present (raster 100 m), global, monthly - version 1 The Water Bodies product detects the areas covered by inland water along the year providing the maximum and the minimum extent of the water surface as well as the seasonal dynamics. The area of water bodies is identified as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). 185 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/water-bodies-2020-present-raster-300-m-global-monthly https://land.copernicus.eu/global/products/wb Water Bodies 2020-present (raster 300 m), global, monthly - version 2 The Water Bodies product detects the areas covered by inland water along the year providing the maximum and the minimum extent of the water surface as well as the seasonal dynamics. The area of water bodies is identified as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). 186 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/burnt-area-2014-2022-raster-300-m-global-10-daily-version http://land.copernicus.eu/global/access Burnt Area 2014-2022 (raster 300 m), global, 10-daily - version 1 BA or Burnt Area products provide temporal pattern information of the fire activity over decades and over seasons. In addition, a seasonality metric provides estimates of the start, peak and end of the fire season within a 1 degree grid. The BA product is generated every 10 days over the entire globe and provided in 10 degree tiles and continents. 187 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-2000-vector-europe-6-yearly-version https://land.copernicus.eu/pan-european/corine-land-cover/clc-2000/view CORINE Land Cover 2000 (vector), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover 2000 (CLC2000) is one of the datasets produced within the frame the Corine Land Cover programme referring to land cover / land use status of year 2000. The Corine Land Cover (CLC) is a European programme, coordinated by the European Environment Agency (EEA), providing consistent and thematically detailed information on land cover and land cover changes across Europe. CLC products are based on the classification of satellite images by the national teams of the participating countries - the EEA member and cooperating countries (EEA39). National CLC inventories are further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC programme provides important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Community as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture and implementing the EU Water Framework Directive. The CLC programme is a part of the Copernicus Land Monitoring Service (https://land.copernicus.eu/) run by the European Commission and the European Environment Agency, which provides environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC (product description, mapping guides and class descriptions) can be found here: https://land.copernicus.eu/user-corner/technical-library/. https://land.copernicus.eu/user-corner/technical-library/ 188 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cems-fire-historical-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical-v1 cems-fire-historical-v1 This data set provides complete historical reconstruction of meteorological conditions favourable to the start, spread and sustainability of fires. The fire danger metrics provided are part of a vast dataset produced by the Copernicus Emergency Management Service for the European Forest Fire Information System (EFFIS). The European Forest Fire Information System incorporates the fire danger indices for three different models developed in Canada, United States and Australia. In this dataset the fire danger indices are calculated using weather forecast from historical simulations provided by ECMWF ERA5 reanalysis. ERA5 by combining model data and a vast set of quality controlled observations provides a globally complete and consistent data-set and is regarded as a good proxy for observed atmospheric conditions. The selected data records in this data set are regularly extended with time as ERA5 forcing data become available. This dataset is produced by ECMWF in its role of the computational centre for fire danger forecast of the CEMS, on behalf of the Joint Research Centre which is the managing entity of the service. DATA DESCRIPTION Data type Gridded Projection Original grid: reduced Gaussian grid of N320 (about 31km) for Reanalysis and N160 (about 63km) for Ensemble members Variables are also provided on a regular latitude x longitude grid. Horizontal coverage Global Land Horizontal resolution Interpolated variables are provided at two grid resolutions: Reanalysis: 0.25° x 0.25° Ensemble members: 0.5° x 0.5° Temporal coverage 1940 to present Temporal resolution Daily File format NetCDF (interpolated grid only) and GRIB2 (all grids) Update frequency Daily DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Original grid: reduced Gaussian grid of N320 (about 31km) for Reanalysis and N160 (about 63km) for Ensemble members Variables are also provided on a regular latitude x longitude grid. Projection Original grid: reduced Gaussian grid of N320 (about 31km) for Reanalysis and N160 (about 63km) for Ensemble members Variables are also provided on a regular latitude x longitude grid. Original grid: reduced Gaussian grid of N320 (about 31km) for Reanalysis and N160 (about 63km) for Ensemble members Variables are also provided on a regular latitude x longitude grid. Horizontal coverage Global Land Horizontal coverage Global Land Horizontal resolution Interpolated variables are provided at two grid resolutions: Reanalysis: 0.25° x 0.25° Ensemble members: 0.5° x 0.5° Horizontal resolution Interpolated variables are provided at two grid resolutions: Reanalysis: 0.25° x 0.25° Ensemble members: 0.5° x 0.5° Interpolated variables are provided at two grid resolutions: Reanalysis: 0.25° x 0.25° Ensemble members: 0.5° x 0.5° Temporal coverage 1940 to present Temporal coverage 1940 to present Temporal resolution Daily Temporal resolution Daily File format NetCDF (interpolated grid only) and GRIB2 (all grids) File format NetCDF (interpolated grid only) and GRIB2 (all grids) Update frequency Daily Update frequency Daily MAIN VARIABLES Name Units Description Build-up index Dimensionless The Build-Up Index is a weighted combination of the Duff moisture code and Drought code to indicate the total amount of fuel available for combustion by a moving flame front. The Duff moisture code has the most influence on the Build-up index value. For example, a Duff moisture code value of zero always results in a Build-up index value of zero regardless of what the Drought code value is. The Drought code has the strongest influence on the Build-up index when Duff moisture code values are high. The greatest effect that the Drought code can have is to make the Build-up index value equal to twice the Duff moisture code value. The Build-up index is often used for pre-suppression planning purposes. Burning index Dimensionless The Burning Index measures the difficulty of controlling a fire. It is derived from a combination of Spread component (how fast it will spread) and Energy release component (how much energy will be produced). In this way, it is related to flame length, which, in the Fire Behavior Prediction System, is based on rate of spread and heat per unit area. However, because of differences in the calculations for Burning index and flame length, they are not the same. Danger rating Dimensionless The Danger rating is equivalent to the FWI reduced to 6 classes of danger, accordingly to EFFIS danger class levels definition (very low, low, medium, high, very high and extreme). The fire danger classes are the same for all countries so maps show a harmonized picture of the spatial distribution of fire danger level. Drought code Dimensionless The Drought code is an indicator of the moisture content in deep compact organic layers. This code represents a fuel layer at approximately 10-20 cm deep. The Drought code fuels have a very slow drying rate, with a time lag of 52 days. The Drought code scale is open-ended, although the maximum value is about 800. Drought factor The drought factor is a component representing fuel availability. It is is given as a number between 0 and 10 and represents the influence of recent temperatures and rainfall events on fuel availability (see Griffiths 1998 for details). The Drought Factor is partly based on the soil moisture deficit which is commonly calculated in Australia as the Keetch-Byram Drought Index (KBDI) (also available). The KBDI estimates the soil moisture below saturation up to a maximum Duff moisture code Dimensionless The Duff moisture code is an indicatore of the moisture content in loosely-compacted organic layers of moderate depth. It is representative of the duff layer that is 5-10 cm deep. Duff moisture code fuels are affected by rain, temperature and relative humidity. Because these fuels are below the forest floor surface, wind speed does not affect the fuel moisture content. The Duff moisture code fuels have a slower drying rate than the Fine fuel moisture code fuels, with a timelag of 12 days. Although the Duff moisture code has an open-ended scale, the highest probable value is in the range of 150. Energy release component J/m2 The Energy release component is a number related to the available energy (British Thermal Unit) per unit area (square foot) within the flaming front at the head of a fire. Daily variations in Energy release component are due to changes in moisture content of the various fuels present, both live and dead. Since this number represents the potential "heat release" per unit area in the flaming zone, it can provide guidance to several important fire activities. It may also be considered a composite fuel moisture value as it reflects the contribution that all live and dead fuels have to potential fire intensity. The Energy release component is a cumulative or "build-up" type of index. As live fuels cure and dead fuels dry, the Energy release component values get higher thus providing a good reflection of drought conditions. The scale is open-ended or unlimited and, as with other National Forest Danger Rating System components, is relative. Fine fuel moisture code Dimensionless The Fine fuel moisture code is an indicatore of the moisture content in litter and other cured fine fuels (needles, mosses, twigs less than 1 cm in diameter). The Fine fuel moisture code is representative of the top litter layer less than 1-2 cm deep. Fine fuel moisture code values change rapidly because of a high surface area to volume ratio, and direct exposure to changing environmental conditions. The Fine fuel moisture code scale ranges from 0-99 and is the only component of the Fire weather index system which does not have an open-ended scale. Generally, fires begin to ignite at Fine fuel moisture code values near 70, and the maximum probable value that will ever be achieved is 96. Fire daily severity index Dimensionless Numeric rating of the difficulty of controlling fires. It is an exponential transformation of the Fire weather index and more accurately reflects the expected efforts required for fire suppression as it increases exponentially as the Fire weather index is above a certain value. Fire danger index Dimensionless The Fire danger index is a metric related to the chances of a fire starting, its rate of spread, its intensity, and its difficulty of suppression. It is open ended however a value of 50 and above is considered extreme in most vegetation Fire weather index Dimensionless The Fire weather index is a combination of Initial spread index and Build-up index, and is a numerical rating of the potential frontal fire intensity. In effect, it indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed. Fire weather index values are not upper bounded however a value of 50 is considered as extreme in many places. The Fire weather index is used for general public information about fire danger conditions. Ignition component % The Ignition component measures the probability a firebrand will require suppression action. Since it is expressed as a probability, it ranges on a scale of 0 to 100. An Ignition component of 100 means that every firebrand will cause a fire requiring action if it contacts a receptive fuel. Likewise an Ignition component of 0 would mean that no firebrand would cause a fire requiring suppression action under those conditions. Initial spread index Dimensionless The Initial spread index combines the Fine fuel moisture code and wind speed to indicate the expected rate of fire spread. Generally, a 13 km h-1 increase in wind speed will double the Initial spread index value. The Initial spread index is accepted as a good indicator of fire spread in open light fuel stands with wind speeds up to 40 km h-1. Keetch-Byram drought index Dimensionless The Keetch-Byram drought index (KBDI) is a number representing the net effect of evapotranspiration and precipitation in producing cumulative moisture deficiency in deep duff and upper soil layers. It is a continuous index, relating to the flammability of organic material in the ground.The Keetch-Byram drought index attempts to measure the amount of precipitation necessary to return the soil to saturated conditions. It is a closed system ranging from 0 to 200 units and represents a moisture regime from 0 to 20 cm of water through the soil layer. At 20 cm of water, the Keetch-Byram drought index assumes saturation. Zero is the point of no moisture deficiency and 200 is the maximum drought that is possible. At any point along the scale, the index number indicates the amount of net rainfall that is required to reduce the index to zero, or saturation. The inputs for Keetch-Byram drought index are weather station latitude, mean annual precipitation, maximum dry bulb temperature, and the last 24 hours of rainfall. Reduction in drought occurs only when rainfall exceeds 5 mm (called net rainfall). The computational steps involve reducing the drought index by the net rain amount and increasing the drought index by a drought factor. KBDI = 0 - 50: Soil moisture and large class fuel moistures are high and do not contribute much to fire intensity. Typical of spring dormant season following winter precipitation. KBDI = 50 - 100: Typical of late spring, early growing season. Lower litter and duff layers are drying and beginning to contribute to fire intensity. KBDI = 100 - 150: Typical of late summer, early fall. Lower litter and duff layers actively contribute to fire intensity and will burn actively. KBDI = 150 - 200: Often associated with more severe drought with increased wildfire occurrence. Intense, deep burning fires with significant downwind spotting can be expected. Live fuels can also be expected to burn actively at these levels. Spread component Dimensionless The Spread component is a measure of the spead at which a headfire would spread. The spread component is numerically equal to the theoretical ideal rate of spread expressed in feet-per-minute however is considered as a dimensionless variable. The Spread component is expressed on an open-ended scale; thus it has no upper limit. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Build-up index Dimensionless The Build-Up Index is a weighted combination of the Duff moisture code and Drought code to indicate the total amount of fuel available for combustion by a moving flame front. The Duff moisture code has the most influence on the Build-up index value. For example, a Duff moisture code value of zero always results in a Build-up index value of zero regardless of what the Drought code value is. The Drought code has the strongest influence on the Build-up index when Duff moisture code values are high. The greatest effect that the Drought code can have is to make the Build-up index value equal to twice the Duff moisture code value. The Build-up index is often used for pre-suppression planning purposes. Build-up index Dimensionless The Build-Up Index is a weighted combination of the Duff moisture code and Drought code to indicate the total amount of fuel available for combustion by a moving flame front. The Duff moisture code has the most influence on the Build-up index value. For example, a Duff moisture code value of zero always results in a Build-up index value of zero regardless of what the Drought code value is. The Drought code has the strongest influence on the Build-up index when Duff moisture code values are high. The greatest effect that the Drought code can have is to make the Build-up index value equal to twice the Duff moisture code value. The Build-up index is often used for pre-suppression planning purposes. Burning index Dimensionless The Burning Index measures the difficulty of controlling a fire. It is derived from a combination of Spread component (how fast it will spread) and Energy release component (how much energy will be produced). In this way, it is related to flame length, which, in the Fire Behavior Prediction System, is based on rate of spread and heat per unit area. However, because of differences in the calculations for Burning index and flame length, they are not the same. Burning index Dimensionless The Burning Index measures the difficulty of controlling a fire. It is derived from a combination of Spread component (how fast it will spread) and Energy release component (how much energy will be produced). In this way, it is related to flame length, which, in the Fire Behavior Prediction System, is based on rate of spread and heat per unit area. However, because of differences in the calculations for Burning index and flame length, they are not the same. Danger rating Dimensionless The Danger rating is equivalent to the FWI reduced to 6 classes of danger, accordingly to EFFIS danger class levels definition (very low, low, medium, high, very high and extreme). The fire danger classes are the same for all countries so maps show a harmonized picture of the spatial distribution of fire danger level. Danger rating Dimensionless The Danger rating is equivalent to the FWI reduced to 6 classes of danger, accordingly to EFFIS danger class levels definition (very low, low, medium, high, very high and extreme). The fire danger classes are the same for all countries so maps show a harmonized picture of the spatial distribution of fire danger level. Drought code Dimensionless The Drought code is an indicator of the moisture content in deep compact organic layers. This code represents a fuel layer at approximately 10-20 cm deep. The Drought code fuels have a very slow drying rate, with a time lag of 52 days. The Drought code scale is open-ended, although the maximum value is about 800. Drought code Dimensionless The Drought code is an indicator of the moisture content in deep compact organic layers. This code represents a fuel layer at approximately 10-20 cm deep. The Drought code fuels have a very slow drying rate, with a time lag of 52 days. The Drought code scale is open-ended, although the maximum value is about 800. Drought factor The drought factor is a component representing fuel availability. It is is given as a number between 0 and 10 and represents the influence of recent temperatures and rainfall events on fuel availability (see Griffiths 1998 for details). The Drought Factor is partly based on the soil moisture deficit which is commonly calculated in Australia as the Keetch-Byram Drought Index (KBDI) (also available). The KBDI estimates the soil moisture below saturation up to a maximum Drought factor The drought factor is a component representing fuel availability. It is is given as a number between 0 and 10 and represents the influence of recent temperatures and rainfall events on fuel availability (see Griffiths 1998 for details). The Drought Factor is partly based on the soil moisture deficit which is commonly calculated in Australia as the Keetch-Byram Drought Index (KBDI) (also available). The KBDI estimates the soil moisture below saturation up to a maximum Duff moisture code Dimensionless The Duff moisture code is an indicatore of the moisture content in loosely-compacted organic layers of moderate depth. It is representative of the duff layer that is 5-10 cm deep. Duff moisture code fuels are affected by rain, temperature and relative humidity. Because these fuels are below the forest floor surface, wind speed does not affect the fuel moisture content. The Duff moisture code fuels have a slower drying rate than the Fine fuel moisture code fuels, with a timelag of 12 days. Although the Duff moisture code has an open-ended scale, the highest probable value is in the range of 150. Duff moisture code Dimensionless The Duff moisture code is an indicatore of the moisture content in loosely-compacted organic layers of moderate depth. It is representative of the duff layer that is 5-10 cm deep. Duff moisture code fuels are affected by rain, temperature and relative humidity. Because these fuels are below the forest floor surface, wind speed does not affect the fuel moisture content. The Duff moisture code fuels have a slower drying rate than the Fine fuel moisture code fuels, with a timelag of 12 days. Although the Duff moisture code has an open-ended scale, the highest probable value is in the range of 150. Energy release component J/m2 The Energy release component is a number related to the available energy (British Thermal Unit) per unit area (square foot) within the flaming front at the head of a fire. Daily variations in Energy release component are due to changes in moisture content of the various fuels present, both live and dead. Since this number represents the potential "heat release" per unit area in the flaming zone, it can provide guidance to several important fire activities. It may also be considered a composite fuel moisture value as it reflects the contribution that all live and dead fuels have to potential fire intensity. The Energy release component is a cumulative or "build-up" type of index. As live fuels cure and dead fuels dry, the Energy release component values get higher thus providing a good reflection of drought conditions. The scale is open-ended or unlimited and, as with other National Forest Danger Rating System components, is relative. Energy release component J/m2 The Energy release component is a number related to the available energy (British Thermal Unit) per unit area (square foot) within the flaming front at the head of a fire. Daily variations in Energy release component are due to changes in moisture content of the various fuels present, both live and dead. Since this number represents the potential "heat release" per unit area in the flaming zone, it can provide guidance to several important fire activities. It may also be considered a composite fuel moisture value as it reflects the contribution that all live and dead fuels have to potential fire intensity. The Energy release component is a cumulative or "build-up" type of index. As live fuels cure and dead fuels dry, the Energy release component values get higher thus providing a good reflection of drought conditions. The scale is open-ended or unlimited and, as with other National Forest Danger Rating System components, is relative. Fine fuel moisture code Dimensionless The Fine fuel moisture code is an indicatore of the moisture content in litter and other cured fine fuels (needles, mosses, twigs less than 1 cm in diameter). The Fine fuel moisture code is representative of the top litter layer less than 1-2 cm deep. Fine fuel moisture code values change rapidly because of a high surface area to volume ratio, and direct exposure to changing environmental conditions. The Fine fuel moisture code scale ranges from 0-99 and is the only component of the Fire weather index system which does not have an open-ended scale. Generally, fires begin to ignite at Fine fuel moisture code values near 70, and the maximum probable value that will ever be achieved is 96. Fine fuel moisture code Dimensionless The Fine fuel moisture code is an indicatore of the moisture content in litter and other cured fine fuels (needles, mosses, twigs less than 1 cm in diameter). The Fine fuel moisture code is representative of the top litter layer less than 1-2 cm deep. Fine fuel moisture code values change rapidly because of a high surface area to volume ratio, and direct exposure to changing environmental conditions. The Fine fuel moisture code scale ranges from 0-99 and is the only component of the Fire weather index system which does not have an open-ended scale. Generally, fires begin to ignite at Fine fuel moisture code values near 70, and the maximum probable value that will ever be achieved is 96. Fire daily severity index Dimensionless Numeric rating of the difficulty of controlling fires. It is an exponential transformation of the Fire weather index and more accurately reflects the expected efforts required for fire suppression as it increases exponentially as the Fire weather index is above a certain value. Fire daily severity index Dimensionless Numeric rating of the difficulty of controlling fires. It is an exponential transformation of the Fire weather index and more accurately reflects the expected efforts required for fire suppression as it increases exponentially as the Fire weather index is above a certain value. Fire danger index Dimensionless The Fire danger index is a metric related to the chances of a fire starting, its rate of spread, its intensity, and its difficulty of suppression. It is open ended however a value of 50 and above is considered extreme in most vegetation Fire danger index Dimensionless The Fire danger index is a metric related to the chances of a fire starting, its rate of spread, its intensity, and its difficulty of suppression. It is open ended however a value of 50 and above is considered extreme in most vegetation Fire weather index Dimensionless The Fire weather index is a combination of Initial spread index and Build-up index, and is a numerical rating of the potential frontal fire intensity. In effect, it indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed. Fire weather index values are not upper bounded however a value of 50 is considered as extreme in many places. The Fire weather index is used for general public information about fire danger conditions. Fire weather index Dimensionless The Fire weather index is a combination of Initial spread index and Build-up index, and is a numerical rating of the potential frontal fire intensity. In effect, it indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed. Fire weather index values are not upper bounded however a value of 50 is considered as extreme in many places. The Fire weather index is used for general public information about fire danger conditions. Ignition component % The Ignition component measures the probability a firebrand will require suppression action. Since it is expressed as a probability, it ranges on a scale of 0 to 100. An Ignition component of 100 means that every firebrand will cause a fire requiring action if it contacts a receptive fuel. Likewise an Ignition component of 0 would mean that no firebrand would cause a fire requiring suppression action under those conditions. Ignition component % The Ignition component measures the probability a firebrand will require suppression action. Since it is expressed as a probability, it ranges on a scale of 0 to 100. An Ignition component of 100 means that every firebrand will cause a fire requiring action if it contacts a receptive fuel. Likewise an Ignition component of 0 would mean that no firebrand would cause a fire requiring suppression action under those conditions. Initial spread index Dimensionless The Initial spread index combines the Fine fuel moisture code and wind speed to indicate the expected rate of fire spread. Generally, a 13 km h-1 increase in wind speed will double the Initial spread index value. The Initial spread index is accepted as a good indicator of fire spread in open light fuel stands with wind speeds up to 40 km h-1. Initial spread index Dimensionless The Initial spread index combines the Fine fuel moisture code and wind speed to indicate the expected rate of fire spread. Generally, a 13 km h-1 increase in wind speed will double the Initial spread index value. The Initial spread index is accepted as a good indicator of fire spread in open light fuel stands with wind speeds up to 40 km h-1. Keetch-Byram drought index Dimensionless The Keetch-Byram drought index (KBDI) is a number representing the net effect of evapotranspiration and precipitation in producing cumulative moisture deficiency in deep duff and upper soil layers. It is a continuous index, relating to the flammability of organic material in the ground.The Keetch-Byram drought index attempts to measure the amount of precipitation necessary to return the soil to saturated conditions. It is a closed system ranging from 0 to 200 units and represents a moisture regime from 0 to 20 cm of water through the soil layer. At 20 cm of water, the Keetch-Byram drought index assumes saturation. Zero is the point of no moisture deficiency and 200 is the maximum drought that is possible. At any point along the scale, the index number indicates the amount of net rainfall that is required to reduce the index to zero, or saturation. The inputs for Keetch-Byram drought index are weather station latitude, mean annual precipitation, maximum dry bulb temperature, and the last 24 hours of rainfall. Reduction in drought occurs only when rainfall exceeds 5 mm (called net rainfall). The computational steps involve reducing the drought index by the net rain amount and increasing the drought index by a drought factor. KBDI = 0 - 50: Soil moisture and large class fuel moistures are high and do not contribute much to fire intensity. Typical of spring dormant season following winter precipitation. KBDI = 50 - 100: Typical of late spring, early growing season. Lower litter and duff layers are drying and beginning to contribute to fire intensity. KBDI = 100 - 150: Typical of late summer, early fall. Lower litter and duff layers actively contribute to fire intensity and will burn actively. KBDI = 150 - 200: Often associated with more severe drought with increased wildfire occurrence. Intense, deep burning fires with significant downwind spotting can be expected. Live fuels can also be expected to burn actively at these levels. Keetch-Byram drought index Dimensionless The Keetch-Byram drought index (KBDI) is a number representing the net effect of evapotranspiration and precipitation in producing cumulative moisture deficiency in deep duff and upper soil layers. It is a continuous index, relating to the flammability of organic material in the ground.The Keetch-Byram drought index attempts to measure the amount of precipitation necessary to return the soil to saturated conditions. It is a closed system ranging from 0 to 200 units and represents a moisture regime from 0 to 20 cm of water through the soil layer. At 20 cm of water, the Keetch-Byram drought index assumes saturation. Zero is the point of no moisture deficiency and 200 is the maximum drought that is possible. At any point along the scale, the index number indicates the amount of net rainfall that is required to reduce the index to zero, or saturation. The inputs for Keetch-Byram drought index are weather station latitude, mean annual precipitation, maximum dry bulb temperature, and the last 24 hours of rainfall. Reduction in drought occurs only when rainfall exceeds 5 mm (called net rainfall). The computational steps involve reducing the drought index by the net rain amount and increasing the drought index by a drought factor. KBDI = 0 - 50: Soil moisture and large class fuel moistures are high and do not contribute much to fire intensity. Typical of spring dormant season following winter precipitation. KBDI = 50 - 100: Typical of late spring, early growing season. Lower litter and duff layers are drying and beginning to contribute to fire intensity. KBDI = 100 - 150: Typical of late summer, early fall. Lower litter and duff layers actively contribute to fire intensity and will burn actively. KBDI = 150 - 200: Often associated with more severe drought with increased wildfire occurrence. Intense, deep burning fires with significant downwind spotting can be expected. Live fuels can also be expected to burn actively at these levels. The Keetch-Byram drought index (KBDI) is a number representing the net effect of evapotranspiration and precipitation in producing cumulative moisture deficiency in deep duff and upper soil layers. It is a continuous index, relating to the flammability of organic material in the ground.The Keetch-Byram drought index attempts to measure the amount of precipitation necessary to return the soil to saturated conditions. It is a closed system ranging from 0 to 200 units and represents a moisture regime from 0 to 20 cm of water through the soil layer. At 20 cm of water, the Keetch-Byram drought index assumes saturation. Zero is the point of no moisture deficiency and 200 is the maximum drought that is possible. At any point along the scale, the index number indicates the amount of net rainfall that is required to reduce the index to zero, or saturation. The inputs for Keetch-Byram drought index are weather station latitude, mean annual precipitation, maximum dry bulb temperature, and the last 24 hours of rainfall. Reduction in drought occurs only when rainfall exceeds 5 mm (called net rainfall). The computational steps involve reducing the drought index by the net rain amount and increasing the drought index by a drought factor. KBDI = 0 - 50: Soil moisture and large class fuel moistures are high and do not contribute much to fire intensity. Typical of spring dormant season following winter precipitation. KBDI = 50 - 100: Typical of late spring, early growing season. Lower litter and duff layers are drying and beginning to contribute to fire intensity. KBDI = 100 - 150: Typical of late summer, early fall. Lower litter and duff layers actively contribute to fire intensity and will burn actively. KBDI = 150 - 200: Often associated with more severe drought with increased wildfire occurrence. Intense, deep burning fires with significant downwind spotting can be expected. Live fuels can also be expected to burn actively at these levels. Spread component Dimensionless The Spread component is a measure of the spead at which a headfire would spread. The spread component is numerically equal to the theoretical ideal rate of spread expressed in feet-per-minute however is considered as a dimensionless variable. The Spread component is expressed on an open-ended scale; thus it has no upper limit. Spread component Dimensionless The Spread component is a measure of the spead at which a headfire would spread. The spread component is numerically equal to the theoretical ideal rate of spread expressed in feet-per-minute however is considered as a dimensionless variable. The Spread component is expressed on an open-ended scale; thus it has no upper limit. 189 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-cooling-degree-days-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-cooling-degree-days-v1 ecde-app-cooling-degree-days-v1 This application has been published in a hidden state for contractual purposes. INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 190 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-extreme-wind-speed-days-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-extreme-wind-speed-days-v1 ecde-app-extreme-wind-speed-days-v1 This application has been published in a hidden state for contractual purposes. INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 191 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-warmest-three-day-period-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-warmest-three-day-period-v1 ecde-app-warmest-three-day-period-v1 This application has been published in a hidden state for contractual purposes. INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 192 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-meteorological-droughts-duration-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-meteorological-droughts-duration-v1 ecde-app-meteorological-droughts-duration-v1 This application has been published in a hidden state for contractual purposes. INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 193 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-extreme-precipitation-total-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-extreme-precipitation-total-v1 ecde-app-extreme-precipitation-total-v1 This application has been published in a hidden state for contractual purposes. INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 194 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-mean-temperature-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-mean-temperature-v1 ecde-app-mean-temperature-v1 This application has been published in a hidden state for contractual purposes. INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 195 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-extreme-precipitation-days-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-extreme-precipitation-days-v1 ecde-app-extreme-precipitation-days-v1 This application has been published in a hidden state for contractual purposes. INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 196 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-mean-soil-moisture-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-mean-soil-moisture-v1 ecde-app-mean-soil-moisture-v1 This application has been published in a hidden state for contractual purposes. INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 197 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-heating-degree-days-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-heating-degree-days-v1 ecde-app-heating-degree-days-v1 This application has been published in a hidden state for contractual purposes. INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 198 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-tropical-nights-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-tropical-nights-v1 ecde-app-tropical-nights-v1 This application has been published in a hidden state for contractual purposes. INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 199 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-consecutive-dry-days-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-consecutive-dry-days-v1 ecde-app-consecutive-dry-days-v1 This application has been published in a hidden state for contractual purposes. INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 200 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-hot-days-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-hot-days-v1 ecde-app-hot-days-v1 This application has been published in a hidden state for contractual purposes. INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 201 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-mean-wind-speed-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-mean-wind-speed-v1 ecde-app-mean-wind-speed-v1 This application has been published in a hidden state for contractual purposes. INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 202 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-climatological-heatwave-days-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-climatological-heatwave-days-v1 ecde-app-climatological-heatwave-days-v1 This application has been published in a hidden state for contractual purposes. INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 203 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-meteorological-droughts-magnitude-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-meteorological-droughts-magnitude-v1 ecde-app-meteorological-droughts-magnitude-v1 This application has been published in a hidden state for contractual purposes. INPUT VARIABLES Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX 2m temperature K The ambient air temperature near to the surface, typically at height of 2m. 3 hourly data was used to calculate monthly, seasonal and annual mean temperature. Bias-corrected CORDEX Bias-corrected CORDEX OUTPUT VARIABLES Name Units Description Mean temperature °C Mean temperature over the selected region and time span. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Mean temperature °C Mean temperature over the selected region and time span. Mean temperature °C Mean temperature over the selected region and time span. 204 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/medium-resolution-vegetation-phenology-and-productivity-2 https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum_d1-0.pdf Medium Resolution Vegetation Phenology and Productivity: Small integral (raster 500m), Oct. 2022 The small integral (SINT), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European Medium Resolution Vegetation Phenology and Productivity (MR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The small integral (SINT) expresses the difference between the function describing the season and the base level from season start to season end. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The small integral for the fitted function during the season is one of the 13 parameters. The full list is available in the Product User Manual: https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… The small integral time series dataset is made available as raster files with 500x 500m resolution, in ETRS89-LAEA projection corresponding to the MCD43 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2000 onwards. It is updated in the first quarter of each year. The full on-line access to open and free data for this resource will be made available by the end of 2022. Until then the data will be made available 'on-demand' by filling in the form at: https://land.copernicus.eu/contact-form https://land.copernicus.eu/contact-form 205 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-surface-temperature-extreme http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_VAR_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs Mediterranean Sea Surface Temperature extreme from Observations Reprocessing DEFINITION The OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). CONTEXT Sea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years. An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial. The Mediterranean Sea has showed a constant increase of the SST in the last three decades across the whole basin. Deep analyses of the variations have displayed a non-uniform rate in space, being the warming trend more evident in the eastern Mediterranean Sea with respect to the western side. This variation rate is also changing in time over the three decades with differences between the seasons (e.g. Pastor et al. 2018; Pisano et al. 2020), being higher in Spring and Summer, which would affect the extreme values. CMEMS KEY FINDINGS The mean 99th percentiles showed in the area present values from 26º in the Alboran sea, around 27ºC in the West of Iberian Peninsula to 28ºC in the Coast of Slovenia. The standard deviation ranges from 0.4 to 0.9ºC in the area. Results for this year show a slight positive anomaly in most of stations in the Spanish Coast (+0.4/+0.8ºC), negative, but close to zero, in the Coast of Slovenia (-0.13ºC) and only in one station in the North West of Spain (Barcelona) the anomaly is positive, slightly above the standard deviation (+1.4ºC). DOI (product):https://doi.org/10.48670/moi-00267 https://doi.org/10.48670/moi-00267 206 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-agroclimatic-indicators https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agroclimatic-indicators sis-agroclimatic-indicators This dataset provides agroclimatic indicators used to characterise plant-climate interactions for global agriculture. Agroclimatic indicators are useful in conveying climate variability and change in the terms that are meaningful to the agricultural sector. The objective of this dataset is to provide these indicators at a global scale in an easily accessible and usable format for further downstream analysis and the forcing of agricultural impact models. ERA-interim reanalysis and bias-corrected climate datasets have been used to generate the agroclimatic indicators for historical and future time periods. The input data was provided through the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), of which the ISIMIP Fast Track product was used. This product contains daily, biased-corrected, climate data from 5 CMIP5 General Circulation Models covering the period 1951-2099 (historical run up to 2005). The agroclimatic indicators were also generated using the WFDEI (Watch Forcing Data methodology applied to ERA-Interim) for the 1981-2010 climatological period. A total of 26 indicators are provided in this dataset at a spatial resolution of 0.5°x0.5° on a lat-lon grid. The temporal resolution of the variables differs depending on the indicator - they are available at 10 consecutive days (10-day), seasonal or annual resolution. Agroclimatic indicators are often used in species distribution modelling to study phenological developments of plants under varying climate conditions. For many users in the agricultural community, assessments of crop development for the current or future cropping seasons are particularly important. This is especially true for the agro-policy and the agro-business communities, as early indications of production anomalies are of paramount importance for tax/subsidies and price volatility. The provision of pre-computed agroclimatic indicators make them readily available to the user and will facilitate the use of climate data by the agricultural community. The data was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 0.5° x 0.5° Vertical coverage Surface Vertical resolution Single level Temporal coverage 1951 to 2099 Temporal resolution Variable dependent: 10-day, seasonal or annual File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Versions Version 1.1 is an extension of 1.0 where the BEDD and SD variables are provided for a range of temperature thresholds Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.5° x 0.5° Horizontal resolution 0.5° x 0.5° Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage 1951 to 2099 Temporal coverage 1951 to 2099 Temporal resolution Variable dependent: 10-day, seasonal or annual Temporal resolution Variable dependent: 10-day, seasonal or annual File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Versions Version 1.1 is an extension of 1.0 where the BEDD and SD variables are provided for a range of temperature thresholds Versions Version 1.1 is an extension of 1.0 where the BEDD and SD variables are provided for a range of temperature thresholds Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Biologically effective degree days °C Sum of daily mean temperatures (TG) above 10°C and less than 30°C, over 10 days. Cold spell duration index day Number of days per season with at least 6 consecutive days when TN < TN10th, where TN is the daily minimum temperature and TN10th is the calendar day 10th percentile. To calculate TN10th for each day, a 5-day window centered on each equivalent day in the 1981-2010 period is used to improve the statistical robustness from which the 10th percentile of the data is selected (e.g. for all 3rd of July days between 1981-2010, the 1st, 2nd, 3rd, 4th, 5th July are selected in the 5-day window). This indicator provides information on reduced blossom formation or reduced growth. Frost days day Number of days per 10 days when TN < 0°C, where TN is the daily minimum temperature. This indicator provides information on frost damage. Growing season length day Number of days between the first occurrence after 1st January (1st July in southern hemisphere) of at least 6 consecutive days with TG > 5°C and the first occurrence after 1st July (1st January in southern hemisphere) of at least 6 consecutive days with TG < 5°C, where TG is the daily mean temperature. This indicator provides an indication whether or not a crop, or a combination of crops, can be sown and subsequently reach maturity within a certain time frame. Heavy precipitation days day Number of days per 10 days when RR > 10mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Ice days day Number of days per 10 days when TX < 0°C, where TX is the daily maximum temperature. This indicator provides information on frost damage. Maximum number of consecutive dry days day Longest period of consecutive days when RR < 1mm, where RR is the daily precipitation sum. This indicator is used for drought monitoring. Maximum number of consecutive frost days day Longest period of consecutive days when TN < 0°C, where TN is the daily minimum temperature. This indicator is used as a general frost damage indicator. Maximum number of consecutive summer days day Longest period of consecutive days when TX > 25°C, where TX is the daily maximum temperature. This indicator provides information on drought stress or on optimal growth for C4 crops (crops that use the C4 carbon fixation pathway, e.g. maize). Maximum number of consecutive wet days day Longest period of consecutive days when RR > 1mm, where RR is the daily precipitation sum. This indicator provides information on drought, oxygen stress and crop growth (i.e. less radiation interception during rainy days). Maximum of daily maximum temperature K Maximum value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Maximum of daily minimum temperature K Maximum value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Mean of daily maximum temperature K Mean value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Mean of daily mean temperature K Mean value of TG over 10 days, where TG is the daily mean temperature. This indicator provides information on long-term climate variability and change. Mean of daily minimum temperature K Mean value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Mean of diurnal temperature range °C Mean value of the daily difference between TX and TN (TX-TN) over 10 days, where TX and TN are daily maximum and minimum temperature respectively. This indicator provides information on climate variability and change. It also serves as a proxy for information on the clarity (transmittance) of the atmosphere. Minimum of daily maximum temperature K Minimum value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Minimum of daily minimum temperature K Minimum value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Precipitation sum mm Sum of RR over 10 days, where RR is the daily precipitation sum. This indicator provides information on possible water shortage or excess. Simple daily intensity index mm Mean of RR over 10 days in which RR > 1mm (wet days), where RR is the daily precipitation sum. This indicator provides information on possible runoff losses. Summer days day Number of days per 10 days when TX > 25°C, where TX is the daily maximum temperature. This indicator provides an indication of the occurrence of heat stress. Tropical nights day Number of days per 10 days when TN > 20°C, where TN is the daily minimum temperature. This indicator provides an indication of occurrence of various pests. Very heavy precipitation days day Number of days per 10 days when RR > 20mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Warm and wet days day Number of days per 10 days when TG > TG75th and RR > RR75th; where TG is the daily mean temperature, TG75th is the calendar day 75th percentile, RR is the daily precipitation sum and RR75th is the 75th percentile of precipitation on wet days. To calculate TG75th and RR75 for each day, a 5-day window centered on each equivalent day in the 1981-2010 period is used to improve the statistical robustness from which the 75th percentile of the data is selected (e.g. for all the 3rd of July days between 1981-2010, the 1st, 2nd, 3rd, 4th, 5th July are selected in the 5-day window). This indicator provides an indication of occurrence of various pests and the crop development, especially leaf formation. Warm spell duration index day Number of days per season with at least 6 consecutive days when TX > TX90th, where TX is the daily maximum temperature and TX90th is the calendar day 90th percentile. To calculate TX90th for each day, a 5-day window centered on each equivalent day in the 1981-2010 period is used to improve the statistical robustness from which the 90th percentile of the data is selected (e.g. for all the 3rd of July days between 1981-2010, the 1st, 2nd, 3rd, 4th, 5th July are selected in the 5-day window). This indicator provides an indication of the occurrence of heat stress. Wet days day Number of days per 10 days when RR > 1mm, where RR is the daily precipitation sum. This indicator provides information on intercepted reduction. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Biologically effective degree days °C Sum of daily mean temperatures (TG) above 10°C and less than 30°C, over 10 days. Biologically effective degree days °C Sum of daily mean temperatures (TG) above 10°C and less than 30°C, over 10 days. Cold spell duration index day Number of days per season with at least 6 consecutive days when TN < TN10th, where TN is the daily minimum temperature and TN10th is the calendar day 10th percentile. To calculate TN10th for each day, a 5-day window centered on each equivalent day in the 1981-2010 period is used to improve the statistical robustness from which the 10th percentile of the data is selected (e.g. for all 3rd of July days between 1981-2010, the 1st, 2nd, 3rd, 4th, 5th July are selected in the 5-day window). This indicator provides information on reduced blossom formation or reduced growth. Cold spell duration index day Number of days per season with at least 6 consecutive days when TN < TN10th, where TN is the daily minimum temperature and TN10th is the calendar day 10th percentile. To calculate TN10th for each day, a 5-day window centered on each equivalent day in the 1981-2010 period is used to improve the statistical robustness from which the 10th percentile of the data is selected (e.g. for all 3rd of July days between 1981-2010, the 1st, 2nd, 3rd, 4th, 5th July are selected in the 5-day window). This indicator provides information on reduced blossom formation or reduced growth. Frost days day Number of days per 10 days when TN < 0°C, where TN is the daily minimum temperature. This indicator provides information on frost damage. Frost days day Number of days per 10 days when TN < 0°C, where TN is the daily minimum temperature. This indicator provides information on frost damage. Growing season length day Number of days between the first occurrence after 1st January (1st July in southern hemisphere) of at least 6 consecutive days with TG > 5°C and the first occurrence after 1st July (1st January in southern hemisphere) of at least 6 consecutive days with TG < 5°C, where TG is the daily mean temperature. This indicator provides an indication whether or not a crop, or a combination of crops, can be sown and subsequently reach maturity within a certain time frame. Growing season length day Number of days between the first occurrence after 1st January (1st July in southern hemisphere) of at least 6 consecutive days with TG > 5°C and the first occurrence after 1st July (1st January in southern hemisphere) of at least 6 consecutive days with TG < 5°C, where TG is the daily mean temperature. This indicator provides an indication whether or not a crop, or a combination of crops, can be sown and subsequently reach maturity within a certain time frame. Heavy precipitation days day Number of days per 10 days when RR > 10mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Heavy precipitation days day Number of days per 10 days when RR > 10mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Ice days day Number of days per 10 days when TX < 0°C, where TX is the daily maximum temperature. This indicator provides information on frost damage. Ice days day Number of days per 10 days when TX < 0°C, where TX is the daily maximum temperature. This indicator provides information on frost damage. Maximum number of consecutive dry days day Longest period of consecutive days when RR < 1mm, where RR is the daily precipitation sum. This indicator is used for drought monitoring. Maximum number of consecutive dry days day Longest period of consecutive days when RR < 1mm, where RR is the daily precipitation sum. This indicator is used for drought monitoring. Maximum number of consecutive frost days day Longest period of consecutive days when TN < 0°C, where TN is the daily minimum temperature. This indicator is used as a general frost damage indicator. Maximum number of consecutive frost days day Longest period of consecutive days when TN < 0°C, where TN is the daily minimum temperature. This indicator is used as a general frost damage indicator. Maximum number of consecutive summer days day Longest period of consecutive days when TX > 25°C, where TX is the daily maximum temperature. This indicator provides information on drought stress or on optimal growth for C4 crops (crops that use the C4 carbon fixation pathway, e.g. maize). Maximum number of consecutive summer days day Longest period of consecutive days when TX > 25°C, where TX is the daily maximum temperature. This indicator provides information on drought stress or on optimal growth for C4 crops (crops that use the C4 carbon fixation pathway, e.g. maize). Maximum number of consecutive wet days day Longest period of consecutive days when RR > 1mm, where RR is the daily precipitation sum. This indicator provides information on drought, oxygen stress and crop growth (i.e. less radiation interception during rainy days). Maximum number of consecutive wet days day Longest period of consecutive days when RR > 1mm, where RR is the daily precipitation sum. This indicator provides information on drought, oxygen stress and crop growth (i.e. less radiation interception during rainy days). Maximum of daily maximum temperature K Maximum value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Maximum of daily maximum temperature K Maximum value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Maximum of daily minimum temperature K Maximum value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Maximum of daily minimum temperature K Maximum value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Mean of daily maximum temperature K Mean value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Mean of daily maximum temperature K Mean value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Mean of daily mean temperature K Mean value of TG over 10 days, where TG is the daily mean temperature. This indicator provides information on long-term climate variability and change. Mean of daily mean temperature K Mean value of TG over 10 days, where TG is the daily mean temperature. This indicator provides information on long-term climate variability and change. Mean of daily minimum temperature K Mean value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Mean of daily minimum temperature K Mean value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Mean of diurnal temperature range °C Mean value of the daily difference between TX and TN (TX-TN) over 10 days, where TX and TN are daily maximum and minimum temperature respectively. This indicator provides information on climate variability and change. It also serves as a proxy for information on the clarity (transmittance) of the atmosphere. Mean of diurnal temperature range °C Mean value of the daily difference between TX and TN (TX-TN) over 10 days, where TX and TN are daily maximum and minimum temperature respectively. This indicator provides information on climate variability and change. It also serves as a proxy for information on the clarity (transmittance) of the atmosphere. Minimum of daily maximum temperature K Minimum value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Minimum of daily maximum temperature K Minimum value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Minimum of daily minimum temperature K Minimum value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Minimum of daily minimum temperature K Minimum value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Precipitation sum mm Sum of RR over 10 days, where RR is the daily precipitation sum. This indicator provides information on possible water shortage or excess. Precipitation sum mm Sum of RR over 10 days, where RR is the daily precipitation sum. This indicator provides information on possible water shortage or excess. Simple daily intensity index mm Mean of RR over 10 days in which RR > 1mm (wet days), where RR is the daily precipitation sum. This indicator provides information on possible runoff losses. Simple daily intensity index mm Mean of RR over 10 days in which RR > 1mm (wet days), where RR is the daily precipitation sum. This indicator provides information on possible runoff losses. Summer days day Number of days per 10 days when TX > 25°C, where TX is the daily maximum temperature. This indicator provides an indication of the occurrence of heat stress. Summer days day Number of days per 10 days when TX > 25°C, where TX is the daily maximum temperature. This indicator provides an indication of the occurrence of heat stress. Tropical nights day Number of days per 10 days when TN > 20°C, where TN is the daily minimum temperature. This indicator provides an indication of occurrence of various pests. Tropical nights day Number of days per 10 days when TN > 20°C, where TN is the daily minimum temperature. This indicator provides an indication of occurrence of various pests. Very heavy precipitation days day Number of days per 10 days when RR > 20mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Very heavy precipitation days day Number of days per 10 days when RR > 20mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Warm and wet days day Number of days per 10 days when TG > TG75th and RR > RR75th; where TG is the daily mean temperature, TG75th is the calendar day 75th percentile, RR is the daily precipitation sum and RR75th is the 75th percentile of precipitation on wet days. To calculate TG75th and RR75 for each day, a 5-day window centered on each equivalent day in the 1981-2010 period is used to improve the statistical robustness from which the 75th percentile of the data is selected (e.g. for all the 3rd of July days between 1981-2010, the 1st, 2nd, 3rd, 4th, 5th July are selected in the 5-day window). This indicator provides an indication of occurrence of various pests and the crop development, especially leaf formation. Warm and wet days day Number of days per 10 days when TG > TG75th and RR > RR75th; where TG is the daily mean temperature, TG75th is the calendar day 75th percentile, RR is the daily precipitation sum and RR75th is the 75th percentile of precipitation on wet days. To calculate TG75th and RR75 for each day, a 5-day window centered on each equivalent day in the 1981-2010 period is used to improve the statistical robustness from which the 75th percentile of the data is selected (e.g. for all the 3rd of July days between 1981-2010, the 1st, 2nd, 3rd, 4th, 5th July are selected in the 5-day window). This indicator provides an indication of occurrence of various pests and the crop development, especially leaf formation. Warm spell duration index day Number of days per season with at least 6 consecutive days when TX > TX90th, where TX is the daily maximum temperature and TX90th is the calendar day 90th percentile. To calculate TX90th for each day, a 5-day window centered on each equivalent day in the 1981-2010 period is used to improve the statistical robustness from which the 90th percentile of the data is selected (e.g. for all the 3rd of July days between 1981-2010, the 1st, 2nd, 3rd, 4th, 5th July are selected in the 5-day window). This indicator provides an indication of the occurrence of heat stress. Warm spell duration index day Number of days per season with at least 6 consecutive days when TX > TX90th, where TX is the daily maximum temperature and TX90th is the calendar day 90th percentile. To calculate TX90th for each day, a 5-day window centered on each equivalent day in the 1981-2010 period is used to improve the statistical robustness from which the 90th percentile of the data is selected (e.g. for all the 3rd of July days between 1981-2010, the 1st, 2nd, 3rd, 4th, 5th July are selected in the 5-day window). This indicator provides an indication of the occurrence of heat stress. Wet days day Number of days per 10 days when RR > 1mm, where RR is the daily precipitation sum. This indicator provides information on intercepted reduction. Wet days day Number of days per 10 days when RR > 1mm, where RR is the daily precipitation sum. This indicator provides information on intercepted reduction. 207 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/corine-land-cover-2000-raster-100-m-europe-6-yearly https://land.copernicus.eu/pan-european/corine-land-cover/clc-2000/view CORINE Land Cover 2000 (raster 100 m), Europe, 6-yearly - version 2020_20u1, May 2020 Corine Land Cover 2000 (CLC2000) is one of the datasets produced within the frame the Corine Land Cover programme referring to land cover / land use status of year 2000. The Corine Land Cover (CLC) is a European programme, coordinated by the European Environment Agency (EEA), providing consistent and thematically detailed information on land cover and land cover changes across Europe. CLC products are based on the classification of satellite images by the national teams of the participating countries - the EEA member and cooperating countries (EEA39). National CLC inventories are further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC programme provides important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Community as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture and implementing the EU Water Framework Directive. The CLC programme is a part of the Copernicus Land Monitoring Service (https://land.copernicus.eu/) run by the European Commission and the European Environment Agency, which provides environmental information from a combination of air- and space-based observation systems and in-situ monitoring. https://land.copernicus.eu/ Additional information about CLC (product description, mapping guides and class descriptions) can be found here: https://land.copernicus.eu/user-corner/technical-library/. https://land.copernicus.eu/user-corner/technical-library/ 208 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-cerra-model-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-cerra-model-levels reanalysis-cerra-model-levels The Copernicus European Regional ReAnalysis (CERRA) datasets provide spatially and temporally consistent historical reconstructions of meteorological variables in the atmosphere and at the surface. There are four subsets: single levels (atmospheric and surface quantities), height levels (upper-air fields up to 500m), pressure levels (upper-air fields up to 1hPa) and model levels (native levels of the model). This entry provides reanalysis data on model levels for Europe from 1984 to present. Reanalysis combines model data with observations into a complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved, reprocessed versions of the original observations, which all benefit the quality of the reanalysis product. The CERRA dataset was produced using the HARMONIE-ALADIN limited-area numerical weather prediction and data assimilation system, hereafter referred to as the CERRA system. The CERRA system employs a 3-dimensional variational data assimilation scheme of the atmospheric state at every assimilation time. The reanalysis dataset is convenient owing to its provision of atmospheric estimates at each model domain grid point over Europe for each regular output time, over a long period, and always using the same data format. The inputs to CERRA reanalysis are the observational data, lateral boundary conditions from ERA5 global reanalysis as prior estimates of the atmospheric state and physiographic datasets describing the surface characteristics of the model. The observing system has evolved over time, and although the data assimilation system can resolve data holes, the much sparser observational networks in the past periods (for example a reduced amount of satellite data in the 1980s) can impact the quality of analyses leading to less accurate estimates. The uncertainty estimates for reanalysis variables are provided by the CERRA-EDA, a 10-member ensemble of data assimilation system. The added value of the CERRA data with respect to the global reanalysis products is expected to come, for example, with the higher horizontal resolution that permits the usage of a better description of the model topography and physiographic data, and the assimilation of more surface observations. More information about the CERRA dataset can be found in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Lambert conformal conic Horizontal coverage Europe. The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Horizontal resolution 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members Vertical coverage From approximately 10m (model level 106) above the surface to a height of 1 hPa (model level 1) Vertical resolution 106 hybrid atmospheric model levels (106, 105, 104 ... 3, 2, 1) Temporal coverage September 1984 - June 2021 Temporal resolution Analysis data: 3-hourly for high-resolution, 6-hourly for ensemble members File format GRIB2 Update frequency New data will be added towards the end of 2023 DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert conformal conic Projection Lambert conformal conic Horizontal coverage Europe. The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Horizontal coverage Europe. The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Horizontal resolution 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members Horizontal resolution 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members Vertical coverage From approximately 10m (model level 106) above the surface to a height of 1 hPa (model level 1) Vertical coverage From approximately 10m (model level 106) above the surface to a height of 1 hPa (model level 1) Vertical resolution 106 hybrid atmospheric model levels (106, 105, 104 ... 3, 2, 1) Vertical resolution 106 hybrid atmospheric model levels (106, 105, 104 ... 3, 2, 1) Temporal coverage September 1984 - June 2021 Temporal coverage September 1984 - June 2021 Temporal resolution Analysis data: 3-hourly for high-resolution, 6-hourly for ensemble members Temporal resolution Analysis data: 3-hourly for high-resolution, 6-hourly for ensemble members File format GRIB2 File format GRIB2 Update frequency New data will be added towards the end of 2023 Update frequency New data will be added towards the end of 2023 MAIN VARIABLES Name Units Description Specific humidity kg kg-1 The specific humidity is the mass of water vapour per unit mass of air valid for the grid area at the corresponding model level. Only analyses are stored for parameters on model levels. Temperature K The temperature is the model temperature valid for the grid area at the corresponding model level. Only analyses are stored for parameters on model levels. Temperature given in Kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. U-component of wind m s-1 The U-component of wind is the zonal component of the wind valid for the grid area at the corresponding pressure level. By model convention, a negative(positive) value indicates air moving towards the west (east). This variable can be combined with the V-component of wind to give the speed and direction of the horizontal wind. Only analyses are stored for parameters on model levels. V-component of wind m s-1 The V-component of wind is the meridional component of the wind valid for the grid area at the corresponding model level. By model convention, a negative(positive) value indicates air moving towards the south (north). This variable can be combined with the U-component of wind to give the speed and direction of the horizontal wind. Only analyses are stored for parameters on model levels. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Specific humidity kg kg-1 The specific humidity is the mass of water vapour per unit mass of air valid for the grid area at the corresponding model level. Only analyses are stored for parameters on model levels. Specific humidity kg kg-1 The specific humidity is the mass of water vapour per unit mass of air valid for the grid area at the corresponding model level. Only analyses are stored for parameters on model levels. Temperature K The temperature is the model temperature valid for the grid area at the corresponding model level. Only analyses are stored for parameters on model levels. Temperature given in Kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Temperature K The temperature is the model temperature valid for the grid area at the corresponding model level. Only analyses are stored for parameters on model levels. Temperature given in Kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. U-component of wind m s-1 The U-component of wind is the zonal component of the wind valid for the grid area at the corresponding pressure level. By model convention, a negative(positive) value indicates air moving towards the west (east). This variable can be combined with the V-component of wind to give the speed and direction of the horizontal wind. Only analyses are stored for parameters on model levels. U-component of wind m s-1 The U-component of wind is the zonal component of the wind valid for the grid area at the corresponding pressure level. By model convention, a negative(positive) value indicates air moving towards the west (east). This variable can be combined with the V-component of wind to give the speed and direction of the horizontal wind. Only analyses are stored for parameters on model levels. V-component of wind m s-1 The V-component of wind is the meridional component of the wind valid for the grid area at the corresponding model level. By model convention, a negative(positive) value indicates air moving towards the south (north). This variable can be combined with the U-component of wind to give the speed and direction of the horizontal wind. Only analyses are stored for parameters on model levels. V-component of wind m s-1 The V-component of wind is the meridional component of the wind valid for the grid area at the corresponding model level. By model convention, a negative(positive) value indicates air moving towards the south (north). This variable can be combined with the U-component of wind to give the speed and direction of the horizontal wind. Only analyses are stored for parameters on model levels. 209 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-delayed-mode-sea-level-product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053 Global Ocean - Delayed Mode Sea level product Short description: This product integrates sea level observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from the Global telecommunication system (GTS) used by the Met Offices. DOI (product) :https://doi.org/10.17882/93670 https://doi.org/10.17882/93670 210 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/insitu-gridded-observations-alpine-precipitation https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-alpine-precipitation insitu-gridded-observations-alpine-precipitation This dataset, also known as the Long-term Alpine Precipitation Reconstruction (LAPrec), provides gridded fields of monthly precipitation for the Alpine region (eight countries). The dataset is derived from station observations and is provided in two issues: LAPrec1871 starts in 1871 and is based on data from 85 input series; LAPrec1901 starts in 1901 and is based on data from 165 input series. LAPrec1871 starts in 1871 and is based on data from 85 input series; LAPrec1901 starts in 1901 and is based on data from 165 input series. This allows user flexibility in terms of requirements defined by temporal extent or spatial accuracy. LAPrec was constructed to satisfy high climatological standards, such as temporal consistency and the realistic reproduction of spatial patterns in complex terrain. As the dataset covers over one-hundred years in temporal extent, it is a qualified basis for historical climate analysis in a mountain region that is highly affected by climate change. The production of LAPrec combines two data sources: HISTALP (Historical Instrumental Climatological Surface Time Series of the Greater Alpine Region) offers homogenised station series of monthly precipitation reaching back into the 19th century. APGD (Alpine Precipitation Grid Dataset) provides daily precipitation gridded data for the period 1971–2008 built from more than 8500 rain gauges. HISTALP (Historical Instrumental Climatological Surface Time Series of the Greater Alpine Region) offers homogenised station series of monthly precipitation reaching back into the 19th century. HISTALP (Historical Instrumental Climatological Surface Time Series of the Greater Alpine Region) offers homogenised station series of monthly precipitation reaching back into the 19th century. APGD (Alpine Precipitation Grid Dataset) provides daily precipitation gridded data for the period 1971–2008 built from more than 8500 rain gauges. APGD (Alpine Precipitation Grid Dataset) provides daily precipitation gridded data for the period 1971–2008 built from more than 8500 rain gauges. The adopted reconstruction method, Reduced Space Optimal Interpolation (RSOI), establishes a linear model between station and grid data, calibrated over the period when both are available. RSOI involves a Principal Component Analysis (PCA) of the high-resolution grid data, followed by an Optimal Interpolation (OI) using the long-term station data. The LAPrec dataset is updated on a two-year basis, by no later than the end of February each second year. The latest version of the dataset will extend until the end of the year before its release date. LAPrec has been developed in the framework of the Copernicus Climate Change Service in a collaboration between the national meteorological services of Switzerland (MeteoSwiss, Federal Office of Meteorology and Climatology) and Austria (ZAMG, Zentralanstalt für Meteorologie und Geodynamik). For more information on input data, methodical construction, applicability, versioning and data access, see the product user guide in the Documentation tab. The latest version of the dataset will temporally extend until the end of the year before its release date. DATA DESCRIPTION Data type Gridded Projection Lambert Azimuthal Equal-Area projection (ETRS89) Horizontal coverage Alpine region (approximately 43–49°N, 4–17.5°E, land area only) Horizontal resolution 5 km Vertical coverage Surface Vertical resolution Single layer Temporal coverage 1871 to 2020 Temporal resolution Monthly File format NetCDF4 Conventions Climate and Forecast Metadata Convention v1.6 (CF-1.6) Versions 1.1, 1.2 Update frequency Every second year DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert Azimuthal Equal-Area projection (ETRS89) Projection Lambert Azimuthal Equal-Area projection (ETRS89) Horizontal coverage Alpine region (approximately 43–49°N, 4–17.5°E, land area only) Horizontal coverage Alpine region (approximately 43–49°N, 4–17.5°E, land area only) Horizontal resolution 5 km Horizontal resolution 5 km Vertical coverage Surface Vertical coverage Surface Vertical resolution Single layer Vertical resolution Single layer Temporal coverage 1871 to 2020 Temporal coverage 1871 to 2020 Temporal resolution Monthly Temporal resolution Monthly File format NetCDF4 File format NetCDF4 Conventions Climate and Forecast Metadata Convention v1.6 (CF-1.6) Conventions Climate and Forecast Metadata Convention v1.6 (CF-1.6) Versions 1.1, 1.2 Versions 1.1, 1.2 Update frequency Every second year Update frequency Every second year MAIN VARIABLES Name Units Description Precipitation kg m-2 Precipitation in the Earth's atmosphere means precipitation of water in all phases. The data represents the accumulated monthly precipitation calculated as the sum of daily precipitation measured between 07:00 CET of the respective day and 07:00 CET of the following day. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Precipitation kg m-2 Precipitation in the Earth's atmosphere means precipitation of water in all phases. The data represents the accumulated monthly precipitation calculated as the sum of daily precipitation measured between 07:00 CET of the respective day and 07:00 CET of the following day. Precipitation kg m-2 Precipitation in the Earth's atmosphere means precipitation of water in all phases. The data represents the accumulated monthly precipitation calculated as the sum of daily precipitation measured between 07:00 CET of the respective day and 07:00 CET of the following day. 211 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/projections-cordex-single-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-single-levels projections-cordex-single-levels This deprecated catalogue entry provides daily and monthly Regional Climate Model (RCM) data on single levels from a number of experiments, models, members and time periods computed over Europe and in the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX). The term "single levels" is used to express that the variables are computed at one vertical level which can be surface (or a level close to the surface) or a dedicated pressure level in the atmosphere. Multiple vertical levels are excluded from this catalogue entry. deprecated High-resolution Regional Climate Models (RCMs) can provide climate change information on regional and local scales in relatively fine detail, which cannot be obtained from coarse scale Global Climate Models (GCMs). This is manifested in better description of small-scale regional climate characteristics and also in more accurate representation of extreme events. Consequently, outputs of such RCMs are indispensable in supporting regional and local climate impact studies and adaptation decisions. RCMs are not independent from the GCMs, since the GCMs provide lateral and lower boundary conditions to the regional models. In that sense RCMs can be viewed as magnifying glasses of the GCMs. The CORDEX experiments consist of RCM simulations representing different future socio-economic scenarios (forcings), different combinations of GCMs and RCMs and different ensemble members of the same GCM-RCM combinations. This experiment design through the ensemble members allows for studies addressing questions related to the key uncertainties in future climate change. These uncertainties come from differences in the scenarios of future socio-economic development, the imperfection of regional and global models used and the internal (natural) variability of the climate system. This experiment design allows for studies addressing questions related to the key uncertainties in future climate change: what will future climate forcing be? what will be the response of the climate system to changes in forcing? what is the uncertainty related to natural variability of the climate system? what will future climate forcing be? what will be the response of the climate system to changes in forcing? what is the uncertainty related to natural variability of the climate system? The term "experiment" in the CDS form refers to three main categories: Evaluation: CORDEX experiment driven by ECMWF ERA-Interim reanalysis for a past period. These experiments can be used to evaluate the quality of the RCMs using perfect boundary conditions as provided by a reanalysis system. The period covered is typically 1980-2010; Historical: CORDEX experiment which covers a period for which modern climate observations exist. Boundary conditions are provided by GCMs. These experiments, that follow the observed changes in climate forcing, show how the RCMs perform for the past climate when forced by GCMs and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1950-2005; Scenario: Ensemble of CORDEX climate projection experiments using RCP (Representative Concentration Pathways) forcing scenarios. These scenarios are the RCP 2.6, 4.5 and 8.5 scenarios providing different pathways of the future climate forcing. Boundary conditions are provided by GCMs. The period covered is typically 2006-2100. Evaluation: CORDEX experiment driven by ECMWF ERA-Interim reanalysis for a past period. These experiments can be used to evaluate the quality of the RCMs using perfect boundary conditions as provided by a reanalysis system. The period covered is typically 1980-2010; Evaluation Historical: CORDEX experiment which covers a period for which modern climate observations exist. Boundary conditions are provided by GCMs. These experiments, that follow the observed changes in climate forcing, show how the RCMs perform for the past climate when forced by GCMs and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1950-2005; Historical Scenario: Ensemble of CORDEX climate projection experiments using RCP (Representative Concentration Pathways) forcing scenarios. These scenarios are the RCP 2.6, 4.5 and 8.5 scenarios providing different pathways of the future climate forcing. Boundary conditions are provided by GCMs. The period covered is typically 2006-2100. Scenario In CORDEX, the same experiments were done using different RCMs (labelled as “Regional Climate Model” in the CDS form). In addition, for each RCM, there is a variety of GCMs, which can be used as lateral boundary conditions. The GCMs used are coming from the CMIP5 (5th phase of the Coupled Model Intercomparison Project) archive. These GCM boundary conditions are labelled as “Global Climate Model” in the form and are also available in the CDS. Additionally, the uncertainty related to internal variability of the climate system is sampled by running several simulations with the same RCM-GCM combination. On the forms, these are indexed as separate ensemble members (the naming convention for ensemble members is available in the documentation). For each GCM, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. More details behind these sequential ensemble numbers will be available in the detailed documentation. On a general level in the CDS form for the RCM simulations “v” enumerates runs and not model versions. Runs numbers different from “v1” means new simulations relative to the first “v1” one. It might not mean a new version: For the EC-EARTH and HadGEM2-ES forced HIRHAM RCM simulation “v2” is a new simulation where proper GHG concentrations changing with time are used as a contrast to “v1” that erroneously used the constant control level throughout the simulation. Therefore users should use "v2". For NorESM forced HIRAM RCM “v2” run includes also an error in the vertical interpolation when preparing the boundary files also exists. Therefore users should use "v3". For the MOHC-HadGEM2-ES forced RACMO simulation "v2" is a new simulation where a big error in SST-remapping from the HadGEM-grid to the RCM-grid in "v1" was corrected. The erroneous v1-simulation has been unpublished from the ESGF. For the CNRM-CM5 forced runs "v2" is a new simulation replacing the old now with input data taken from pressure levels instead of model levels. The originally provided model level fields from CNRM were wrong. Two MPI-driven scenario runs were rerun in 2016 as there had been problems with a restart file and as there was an error in the snow diagnostics in the original run. The reruns were labelled "v1a". For the EC-EARTH and HadGEM2-ES forced HIRHAM RCM simulation “v2” is a new simulation where proper GHG concentrations changing with time are used as a contrast to “v1” that erroneously used the constant control level throughout the simulation. Therefore users should use "v2". For NorESM forced HIRAM RCM “v2” run includes also an error in the vertical interpolation when preparing the boundary files also exists. Therefore users should use "v3". For the MOHC-HadGEM2-ES forced RACMO simulation "v2" is a new simulation where a big error in SST-remapping from the HadGEM-grid to the RCM-grid in "v1" was corrected. The erroneous v1-simulation has been unpublished from the ESGF. For the CNRM-CM5 forced runs "v2" is a new simulation replacing the old now with input data taken from pressure levels instead of model levels. The originally provided model level fields from CNRM were wrong. Two MPI-driven scenario runs were rerun in 2016 as there had been problems with a restart file and as there was an error in the snow diagnostics in the original run. The reruns were labelled "v1a". The data are produced by the participating institutes of the EURO-CORDEX and Med-CORDEX projects. DATA DESCRIPTION Data type Gridded Projection Projection differs across the RCMs Horizontal coverage From 27°N to 72°N and from 22°W to 45°E Horizontal resolution 0.11°x0.11° Vertical resolution Variables are provided in one single level (which may differ among variables) Temporal coverage 1950-2100 (shorter for some experiments) Temporal resolution 3h, 6h, daily, monthly and seasonal File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions Latest version of the data is provided. Update frequency Regular quarterly updates DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Projection differs across the RCMs Projection Projection differs across the RCMs Horizontal coverage From 27°N to 72°N and from 22°W to 45°E Horizontal coverage From 27°N to 72°N and from 22°W to 45°E Horizontal resolution 0.11°x0.11° Horizontal resolution 0.11°x0.11° Vertical resolution Variables are provided in one single level (which may differ among variables) Vertical resolution Variables are provided in one single level (which may differ among variables) Temporal coverage 1950-2100 (shorter for some experiments) Temporal coverage 1950-2100 (shorter for some experiments) Temporal resolution 3h, 6h, daily, monthly and seasonal Temporal resolution 3h, 6h, daily, monthly and seasonal File format NetCDF4 File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions Latest version of the data is provided. Versions Latest version of the data is provided. Update frequency Regular quarterly updates Update frequency Regular quarterly updates MAIN VARIABLES Name Units Description 10m Wind Speed m s-1 The magnitude of the two-dimensional horizontal air velocity. The data represents the mean over the aggregation period at 10m above the surface. 10m u-component of the wind m s-1 The magnitude of the eastward component of the wind at 10m above the surface. 10m v-component of the wind m s-1 The magnitude of the northward component of the wind at 10m above the surface. 200hPa u-component of the wind m s-1 The magnitude of the eastward component of the wind at 10m 200hPa. 200hPa v-component of the wind m s-1 The magnitude of the northward component of the wind at 10m 200hPa. 2m relative humidity % Relative humidity is the percentage ratio of the water vapour mass to the water vapour mass at the saturation point given the temperature at that location. The data represents the mean over the aggregation period at 2m above the surface. 2m specific humidity Dimensionless Amount of moisture in the air at 2m above the surface divided by the amount of air plus moisture at that location. 2m temperature K The ambient air temperature. The data represents the mean over the aggregation period at to 2m above the surface. 500hPa geopotential m Gravitational potential energy per unit mass normalised by the standard gravity at 500hPa at the same latitude. 850hPa U-component of the wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity at 850hPa. 850hPa V-component of the wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity at 850hPa. Mean precipitation flux kg m-2 s-1 The deposition of water to the Earth s surface in the form of rain, snow, ice or hail. The precipitation flux is the mass of water per unit area and time. The data represents the mean over the aggregation period. Mean sea level pressure Pa The air pressure at sea level. In regions where the Earth s surface is above sea level the surface pressure is used to compute the air pressure that would exist at sea level directly below given a constant air temperature from the surface to the sea level point. The data represents the mean over the aggregation period. Surface pressure Pa Pressure of air at the lower boundary of the atmosphere. Surface solar radiation downwards W m-2 The downward shortwave radiative flux of energy per unit area. The data represents the mean over the aggregation period at the surface. Surface thermal radiation downward W m-2 Radiative longwave flux of energy incinding on the surface from the above per unit area. Surface upwelling shortwave radiation W m-2 Short wave radiative flux of energy from the surface per unit area. Total cloud cover Dimensionless Total refers to the whole atmosphere column, as seen from the surface or the top of the atmosphere. Cloud cover refers to fraction of horizontal area occupied by clouds. Total run-off flux kg m-2 s-1 The mass of surface and sub-surface liquid water per unit area and time, which drains from land. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 10m Wind Speed m s-1 The magnitude of the two-dimensional horizontal air velocity. The data represents the mean over the aggregation period at 10m above the surface. 10m Wind Speed m s-1 The magnitude of the two-dimensional horizontal air velocity. The data represents the mean over the aggregation period at 10m above the surface. 10m u-component of the wind m s-1 The magnitude of the eastward component of the wind at 10m above the surface. 10m u-component of the wind m s-1 The magnitude of the eastward component of the wind at 10m above the surface. 10m v-component of the wind m s-1 The magnitude of the northward component of the wind at 10m above the surface. 10m v-component of the wind m s-1 The magnitude of the northward component of the wind at 10m above the surface. 200hPa u-component of the wind m s-1 The magnitude of the eastward component of the wind at 10m 200hPa. 200hPa u-component of the wind m s-1 The magnitude of the eastward component of the wind at 10m 200hPa. 200hPa v-component of the wind m s-1 The magnitude of the northward component of the wind at 10m 200hPa. 200hPa v-component of the wind m s-1 The magnitude of the northward component of the wind at 10m 200hPa. 2m relative humidity % Relative humidity is the percentage ratio of the water vapour mass to the water vapour mass at the saturation point given the temperature at that location. The data represents the mean over the aggregation period at 2m above the surface. 2m relative humidity % Relative humidity is the percentage ratio of the water vapour mass to the water vapour mass at the saturation point given the temperature at that location. The data represents the mean over the aggregation period at 2m above the surface. 2m specific humidity Dimensionless Amount of moisture in the air at 2m above the surface divided by the amount of air plus moisture at that location. 2m specific humidity Dimensionless Amount of moisture in the air at 2m above the surface divided by the amount of air plus moisture at that location. 2m temperature K The ambient air temperature. The data represents the mean over the aggregation period at to 2m above the surface. 2m temperature K The ambient air temperature. The data represents the mean over the aggregation period at to 2m above the surface. 500hPa geopotential m Gravitational potential energy per unit mass normalised by the standard gravity at 500hPa at the same latitude. 500hPa geopotential m Gravitational potential energy per unit mass normalised by the standard gravity at 500hPa at the same latitude. 850hPa U-component of the wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity at 850hPa. 850hPa U-component of the wind m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity at 850hPa. 850hPa V-component of the wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity at 850hPa. 850hPa V-component of the wind m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity at 850hPa. Mean precipitation flux kg m-2 s-1 The deposition of water to the Earth s surface in the form of rain, snow, ice or hail. The precipitation flux is the mass of water per unit area and time. The data represents the mean over the aggregation period. Mean precipitation flux kg m-2 s-1 The deposition of water to the Earth s surface in the form of rain, snow, ice or hail. The precipitation flux is the mass of water per unit area and time. The data represents the mean over the aggregation period. Mean sea level pressure Pa The air pressure at sea level. In regions where the Earth s surface is above sea level the surface pressure is used to compute the air pressure that would exist at sea level directly below given a constant air temperature from the surface to the sea level point. The data represents the mean over the aggregation period. Mean sea level pressure Pa The air pressure at sea level. In regions where the Earth s surface is above sea level the surface pressure is used to compute the air pressure that would exist at sea level directly below given a constant air temperature from the surface to the sea level point. The data represents the mean over the aggregation period. Surface pressure Pa Pressure of air at the lower boundary of the atmosphere. Surface pressure Pa Pressure of air at the lower boundary of the atmosphere. Surface solar radiation downwards W m-2 The downward shortwave radiative flux of energy per unit area. The data represents the mean over the aggregation period at the surface. Surface solar radiation downwards W m-2 The downward shortwave radiative flux of energy per unit area. The data represents the mean over the aggregation period at the surface. Surface thermal radiation downward W m-2 Radiative longwave flux of energy incinding on the surface from the above per unit area. Surface thermal radiation downward W m-2 Radiative longwave flux of energy incinding on the surface from the above per unit area. Surface upwelling shortwave radiation W m-2 Short wave radiative flux of energy from the surface per unit area. Surface upwelling shortwave radiation W m-2 Short wave radiative flux of energy from the surface per unit area. Total cloud cover Dimensionless Total refers to the whole atmosphere column, as seen from the surface or the top of the atmosphere. Cloud cover refers to fraction of horizontal area occupied by clouds. Total cloud cover Dimensionless Total refers to the whole atmosphere column, as seen from the surface or the top of the atmosphere. Cloud cover refers to fraction of horizontal area occupied by clouds. Total run-off flux kg m-2 s-1 The mass of surface and sub-surface liquid water per unit area and time, which drains from land. Total run-off flux kg m-2 s-1 The mass of surface and sub-surface liquid water per unit area and time, which drains from land. 212 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-west-shelf-region-bio-geo-chemical-l3-daily http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_NWS_BGC_HR_L3_NRT_009_203 North West Shelf Region, Bio-Geo-Chemical, L3, daily observation Short description: The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in µg/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km². RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel). Processing information: The HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of: * Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone. * Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression. * Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area. * invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. * Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. * Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month. * Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day. Description of observation methods/instruments: Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton. Quality / Accuracy / Calibration information: A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212. Suitability, Expected type of users / uses: This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. Dataset names: *cmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-v01 *cmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-v01 *cmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-v01 Files format: *netCDF-4, CF-1.7 *INSPIRE compliant. DOI (product) :https://doi.org/10.48670/moi-00118 https://doi.org/10.48670/moi-00118 213 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-surface-temperature-extreme-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BLKSEA_OMI_TEMPSAL_extreme_var_temp_mean_and_anomaly Black Sea Surface Temperature extreme from Reanalysis DEFINITION The CMEMS BLKSEA_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (BLKSEA_MULTIYEAR_PHY_007_004) and the Analysis product (BLKSEA_ANALYSIS_FORECAST_PHYS_007_001). Two parameters have been considered for this OMI: * Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2019). * Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile. This indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019). CONTEXT The Sea Surface Temperature is one of the Essential Ocean Variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. Particularly in the Black Sea, ocean-atmospheric processes together with its general cyclonic circulation (Rim Current) play an important role on the sea surface temperature variability (Capet et al. 2012). As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. The 99th mean percentile of sea surface temperature provides a worth information about the variability of the sea surface temperature and warming trends but has not been investigated with details in the Black Sea. While the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013). Recent studies indicated a warming trend of the sea surface temperature in the Black Sea in the latest years (Mulet et al., 2018; Sakali and Başusta, 2018). A specific analysis on the interannual variability of the basin-averaged sea surface temperature revealed a higher positive trend in its eastern region (Ginzburg et al., 2004). For the past three decades, Sakali and Başusta (2018) presented an increase in sea surface temperature that varied along both east–west and south–north directions in the Black Sea. CMEMS KEY FINDINGS The mean annual 99th percentile in the period 1993–2019 exhibits values ranging from 25.50 to 26.50 oC in the western and central regions of the Black Sea. The values increase towards the east, exceeding 27.5 oC. This contrasting west-east pattern may be linked to the basin wide cyclonic circulation. There are regions showing lower values, below 25.75 oC, such as a small area west of Crimean Peninsula in the vicinity of the Sevastopol anticyclone, the Northern Ukraine region, in particular close to the Odessa and the Karkinytska Gulf due to the freshwaters from the land and a narrow area along the Turkish coastline in the south. Results for 2020 show negative anomalies in the area of influence of the Bosporus and the Bulgarian offshore region up to the Crimean peninsula, while the North West shelf exhibits a positive anomaly as in the Eastern basin. The highest positive value is occurring in the Eastern Tukish coastline nearest the Batumi gyre area. This may be related to the variously increase of sea surface temperature in such a way the southern regions have experienced a higher warming. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00216 https://doi.org/10.48670/moi-00216 214 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-bio-geo-chemical-l3-daily-observation http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_BAL_BGC_HR_L3_NRT_009_202 Baltic Sea, Bio-Geo-Chemical, L3, daily observation Short description: The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in µg/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km². RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel). Processing information: The HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of: * Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone. * Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression. * Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area. * invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. * Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. * Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month. * Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day. Description of observation methods/instruments: Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton. Quality / Accuracy / Calibration information: A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212. Suitability, Expected type of users / uses: This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. Dataset names: *cmems_obs_oc_bal_bgc_geophy_nrt_l3-hr_P1D-v01 *cmems_obs_oc_bal_bgc_transp_nrt_l3-hr_P1D-v01 *cmems_obs_oc_bal_bgc_optics_nrt_l3-hr_P1D-v01 Files format: *netCDF-4, CF-1.7 *INSPIRE compliant. DOI (product) :https://doi.org/10.48670/moi-00079 https://doi.org/10.48670/moi-00079 215 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-bio-geo-chemical-l3-daily-observation http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_BLK_BGC_HR_L3_NRT_009_206 Black Sea, Bio-Geo-Chemical, L3, daily observation Short description: The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in µg/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km². RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel). Processing information: The HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of: * Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone. * Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression. * Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area. * invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. * Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. * Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month. * Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day. Description of observation methods/instruments: Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton. Quality / Accuracy / Calibration information: A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212. Suitability, Expected type of users / uses: This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. Dataset names: *cmems_obs_oc_blk_bgc_geophy_nrt_l3-hr_P1D-v01 *cmems_obs_oc_blk_bgc_transp_nrt_l3-hr_P1D-v01 *cmems_obs_oc_blk_bgc_optics_nrt_l3-hr_P1D-v01 Files format: *netCDF-4, CF-1.7 *INSPIRE compliant. DOI (product) :https://doi.org/10.48670/moi-00086 https://doi.org/10.48670/moi-00086 216 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-water-level-change-timeseries https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-water-level-change-timeseries sis-water-level-change-timeseries The dataset presents water level time series resulting from tides, surges and sea level rise computed for a European-wide domain. The dataset provides an understanding of European coastal hydrodynamics under the impact of climate change (e.g. sea level rise) and can be used to provide added value to various coastal sectors and studies such as coastal flooding, coastal erosion, harbours and ports. To compute these time series, the Deltares Global Tide and Surge Model (GTSM) version 3.0 is used together with regional climate forcing and sea level rise initial conditions. The regional climate forcing employed is the HIRHAM5 model from the Danish Meteorological Institute (DMI), a member of the EURO-CORDEX climate model ensemble, which is downscaled from the global climate model EC-EARTH. By using a regional climate model and a high-resolution forcing field, GTSM is able to produce a more consistent and high quality dataset. In order to assess the impact of climate change, the GTSM model is run for three different climate scenarios: the present climate (labelled 'historical'), and two Representative Concentration Pathway (RCP) scenarios that correspond to an optimistic emission scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century often called the business-as-usual scenario (RCP8.5). Given that the projections of these climate scenarios are based on a single combination of the regional and global climate models, users of these data should take in consideration that there is an inevitable underestimation of the uncertainty associated with this dataset. In addition to the climate scenarios, a reanalysis dataset is computed by forcing GTSM with ERA5 reanalysis. This provides recent historical water-levels that can be used to look at specific (extreme) events in the past. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage Europe Horizontal resolution Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Vertical coverage Surface Vertical resolution Single level Temporal coverage ERA5 reanalysis: from 1979 to 2017 Historical: from 1977 to 2005 RCP8.5: from 2041 to 2070 RCP4.5: from 2071 to 2100 Temporal resolution 10 min File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.5 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Europe Horizontal coverage Europe Horizontal resolution Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Horizontal resolution Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage ERA5 reanalysis: from 1979 to 2017 Historical: from 1977 to 2005 RCP8.5: from 2041 to 2070 RCP4.5: from 2071 to 2100 Temporal coverage ERA5 reanalysis: from 1979 to 2017 Historical: from 1977 to 2005 RCP8.5: from 2041 to 2070 RCP4.5: from 2071 to 2100 ERA5 reanalysis: from 1979 to 2017 Historical: from 1977 to 2005 RCP8.5: from 2041 to 2070 RCP4.5: from 2071 to 2100 Temporal resolution 10 min Temporal resolution 10 min File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.5 Conventions Climate and Forecast (CF) Metadata Convention v1.5 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Mean sea level m Mean sea level height relative to the historical mean sea level height (1985-2005). Storm surge residual m The storm surge residual is calculated as the difference between the total water level and the tide-only derived water level. Sea level rise forcing is included in both total water level and tidal elevation in the simulations of the future cases. Tidal elevation m Barotropic tidal signal containing astronomic tide, self-attraction and loading, radiational tides and mean sea level. Sea level rise forcing is included in both total water level and tidal elevation in the simulations of the future cases. Total water level m Total water level (resulting from the full simulations including pure tides, storm surges and mean sea level). Sea level rise forcing is included in both total water level and tidal elevation in the simulations of the future cases. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Mean sea level m Mean sea level height relative to the historical mean sea level height (1985-2005). Mean sea level m Mean sea level height relative to the historical mean sea level height (1985-2005). Storm surge residual m The storm surge residual is calculated as the difference between the total water level and the tide-only derived water level. Sea level rise forcing is included in both total water level and tidal elevation in the simulations of the future cases. Storm surge residual m The storm surge residual is calculated as the difference between the total water level and the tide-only derived water level. Sea level rise forcing is included in both total water level and tidal elevation in the simulations of the future cases. Tidal elevation m Barotropic tidal signal containing astronomic tide, self-attraction and loading, radiational tides and mean sea level. Sea level rise forcing is included in both total water level and tidal elevation in the simulations of the future cases. Tidal elevation m Barotropic tidal signal containing astronomic tide, self-attraction and loading, radiational tides and mean sea level. Sea level rise forcing is included in both total water level and tidal elevation in the simulations of the future cases. Total water level m Total water level (resulting from the full simulations including pure tides, storm surges and mean sea level). Sea level rise forcing is included in both total water level and tidal elevation in the simulations of the future cases. Total water level m Total water level (resulting from the full simulations including pure tides, storm surges and mean sea level). Sea level rise forcing is included in both total water level and tidal elevation in the simulations of the future cases. 217 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-bio-geo-chemical-l3-daily-observation http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_MED_BGC_HR_L3_NRT_009_205 Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation Short description: The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in µg/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km². RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel). Processing information: The HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of: * Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone. * Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression. * Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area. * invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. * Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. * Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month. * Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day. Description of observation methods/instruments: Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton. Quality / Accuracy / Calibration information: A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212. Suitability, Expected type of users / uses: This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. Dataset names: *cmems_obs_oc_ibi_bgc_geophy_nrt_l3-hr_P1D-v01 *cmems_obs_oc_ibi_bgc_transp_nrt_l3-hr_P1D-v01 *cmems_obs_oc_ibi_bgc_optics_nrt_l3-hr_P1D-v01 Files format: *netCDF-4, CF-1.7 *INSPIRE compliant. DOI (product) :https://doi.org/10.48670/moi-00109 https://doi.org/10.48670/moi-00109 218 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-era5-explorer https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-era5-explorer app-era5-explorer This application provides visualisations of historical climate statistics for any location around the world. Click anywhere on the interactive map or search for a city to explore the typical monthly climate and discover how the climate has changed over the past forty years. This application is driven by ERA5, the fifth generation ECMWF atmospheric reanalysis of the global climate. ERA5 describes the global history of the atmosphere for the period 1979-2020, using a combination of forecast models and data assimilation systems to 'reanalyse' past observations. As such, the information presented by this application for specific locations are not site-specific observations but rather based on the nearest grid point (nearest 1 degree) to the given location in the ERA5 reanalysis. User-selectable parameters User-selectable parameters City: the city for which to generate location-specific climate statistics. The performance of the application is maximised for the most populous cities in Europe, along with all European capitals. For lower population and/or non-European cities, you might need to wait up to a minute for the data to be retrieved and processed. Variable: global average fields (for the 1981-2010 period) to visualise in the interactive map. The available options are global average temperatures, wind speeds and precipitation totals. Statistics: by searching for a city or clicking on the interactive map, you can view a range of climate statistics based on the nearest ERA5 grid point to the selected location: Climatologies (typical monthly averages) of temperature, precipitation, wind speed and wind direction at the given location, averaged over the period 1981-2010. Temperature and precipitations anomalies comparing each year from 1979-2020 to the long-term average period 1981-2010. Monthly and annual percentages of frost days, summer days and tropical nights, averaged over the period 1981-2010. City: the city for which to generate location-specific climate statistics. The performance of the application is maximised for the most populous cities in Europe, along with all European capitals. For lower population and/or non-European cities, you might need to wait up to a minute for the data to be retrieved and processed. Variable: global average fields (for the 1981-2010 period) to visualise in the interactive map. The available options are global average temperatures, wind speeds and precipitation totals. Statistics: by searching for a city or clicking on the interactive map, you can view a range of climate statistics based on the nearest ERA5 grid point to the selected location: Climatologies (typical monthly averages) of temperature, precipitation, wind speed and wind direction at the given location, averaged over the period 1981-2010. Temperature and precipitations anomalies comparing each year from 1979-2020 to the long-term average period 1981-2010. Monthly and annual percentages of frost days, summer days and tropical nights, averaged over the period 1981-2010. Climatologies (typical monthly averages) of temperature, precipitation, wind speed and wind direction at the given location, averaged over the period 1981-2010. Temperature and precipitations anomalies comparing each year from 1979-2020 to the long-term average period 1981-2010. Monthly and annual percentages of frost days, summer days and tropical nights, averaged over the period 1981-2010. Climatologies (typical monthly averages) of temperature, precipitation, wind speed and wind direction at the given location, averaged over the period 1981-2010. Temperature and precipitations anomalies comparing each year from 1979-2020 to the long-term average period 1981-2010. Monthly and annual percentages of frost days, summer days and tropical nights, averaged over the period 1981-2010. More details about the products are given in the Documentation section. INPUT VARIABLES Name Units Description Source 10m u-component of wind m s-1 ERA5 monthly averaged eastward component of wind at a height of 10m. ERA5 10m v-component of wind m s-1 ERA5 monthly averaged northward component of wind at a height of 10m. ERA5 Air temperature K ERA5 3-hourly temperature of air at 2m above the surface. ERA5 Instantaneous 10m wind gust m s-1 ERA5 monthly averaged wind gust speed at a height of 10m. ERA5 Mean total precipitation rate kg m-2 day-1 ERA5 monthly averaged total precipitation rate. ERA5 INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 10m u-component of wind m s-1 ERA5 monthly averaged eastward component of wind at a height of 10m. ERA5 10m u-component of wind m s-1 ERA5 monthly averaged eastward component of wind at a height of 10m. ERA5 ERA5 10m v-component of wind m s-1 ERA5 monthly averaged northward component of wind at a height of 10m. ERA5 10m v-component of wind m s-1 ERA5 monthly averaged northward component of wind at a height of 10m. ERA5 ERA5 Air temperature K ERA5 3-hourly temperature of air at 2m above the surface. ERA5 Air temperature K ERA5 3-hourly temperature of air at 2m above the surface. ERA5 ERA5 Instantaneous 10m wind gust m s-1 ERA5 monthly averaged wind gust speed at a height of 10m. ERA5 Instantaneous 10m wind gust m s-1 ERA5 monthly averaged wind gust speed at a height of 10m. ERA5 ERA5 Mean total precipitation rate kg m-2 day-1 ERA5 monthly averaged total precipitation rate. ERA5 Mean total precipitation rate kg m-2 day-1 ERA5 monthly averaged total precipitation rate. ERA5 ERA5 OUTPUT VARIABLES Name Units Description Monthly average 10m wind speed and gust speed m s-1 The typical monthly average 10m wind speed and gust speed for each calendar month at the given location, averaged over the long-term reference period of 1981-2010. Monthly average direction cardinal direction The typical monthly average 10m wind direction, derived from the direction of the monthly average u- and v-components of 10m wind. Monthly average mean, maximum and minimum temperatures °C The typical monthly mean, maximum and minimum temperatures for each calendar month at the selected location. Monthly average total precipitation amount mm The typical monthly total precipitation amount for each calendar month at the given location, averaged over the long-term reference period of 1981-2010. Monthly temperature indices % The typical monthly percentage of days which are categorised as frost days (daily minimum temperature < 0°C), summer days (daily maximum temperature > 25°C) and tropical nights (daily minimum temperature > 20°C). Yearly precipitation anomaly % The difference (as a percentage) between each yearly precipitation total (in mm) for the 1979-2018 period, and the average over the long-term reference period of 1981-2010. Yearly temperature anomaly stripes °C A visualisation of yearly temperature anomalies, or how much warmer or colder each year in the 1979-2018 period was compared to the long-term reference period of 1981-2010. Yearly temperature indices % The yearly percentage of days which are categorised as frost days (daily minimum temperature < 0°C), summer days (daily maximum temperature > 25°C) and tropical nights (daily minimum temperature > 20°C). OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Monthly average 10m wind speed and gust speed m s-1 The typical monthly average 10m wind speed and gust speed for each calendar month at the given location, averaged over the long-term reference period of 1981-2010. Monthly average 10m wind speed and gust speed m s-1 The typical monthly average 10m wind speed and gust speed for each calendar month at the given location, averaged over the long-term reference period of 1981-2010. Monthly average direction cardinal direction The typical monthly average 10m wind direction, derived from the direction of the monthly average u- and v-components of 10m wind. Monthly average direction cardinal direction The typical monthly average 10m wind direction, derived from the direction of the monthly average u- and v-components of 10m wind. Monthly average mean, maximum and minimum temperatures °C The typical monthly mean, maximum and minimum temperatures for each calendar month at the selected location. Monthly average mean, maximum and minimum temperatures °C The typical monthly mean, maximum and minimum temperatures for each calendar month at the selected location. Monthly average total precipitation amount mm The typical monthly total precipitation amount for each calendar month at the given location, averaged over the long-term reference period of 1981-2010. Monthly average total precipitation amount mm The typical monthly total precipitation amount for each calendar month at the given location, averaged over the long-term reference period of 1981-2010. Monthly temperature indices % The typical monthly percentage of days which are categorised as frost days (daily minimum temperature < 0°C), summer days (daily maximum temperature > 25°C) and tropical nights (daily minimum temperature > 20°C). Monthly temperature indices % The typical monthly percentage of days which are categorised as frost days (daily minimum temperature < 0°C), summer days (daily maximum temperature > 25°C) and tropical nights (daily minimum temperature > 20°C). Yearly precipitation anomaly % The difference (as a percentage) between each yearly precipitation total (in mm) for the 1979-2018 period, and the average over the long-term reference period of 1981-2010. Yearly precipitation anomaly % The difference (as a percentage) between each yearly precipitation total (in mm) for the 1979-2018 period, and the average over the long-term reference period of 1981-2010. Yearly temperature anomaly stripes °C A visualisation of yearly temperature anomalies, or how much warmer or colder each year in the 1979-2018 period was compared to the long-term reference period of 1981-2010. Yearly temperature anomaly stripes °C A visualisation of yearly temperature anomalies, or how much warmer or colder each year in the 1979-2018 period was compared to the long-term reference period of 1981-2010. Yearly temperature indices % The yearly percentage of days which are categorised as frost days (daily minimum temperature < 0°C), summer days (daily maximum temperature > 25°C) and tropical nights (daily minimum temperature > 20°C). Yearly temperature indices % The yearly percentage of days which are categorised as frost days (daily minimum temperature < 0°C), summer days (daily maximum temperature > 25°C) and tropical nights (daily minimum temperature > 20°C). 219 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/iberic-sea-bio-geo-chemical-l3-daily-observation http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_IBI_BGC_HR_L3_NRT_009_204 Iberic Sea, Bio-Geo-Chemical, L3, daily observation Short description: The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in µg/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km². RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel). Processing information: The HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of: * Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone. * Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression. * Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area. * invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. * Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. * Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month. * Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day. Description of observation methods/instruments: Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton. Quality / Accuracy / Calibration information: A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212. Suitability, Expected type of users / uses: This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. Dataset names: *cmems_obs_oc_nws_bgc_geophy_nrt_l3-hr_P1D-v01 *cmems_obs_oc_nws_bgc_transp_nrt_l3-hr_P1D-v01 *cmems_obs_oc_nws_bgc_optics_nrt_l3-hr_P1D-v01 Files format: *netCDF-4, CF-1.7 *INSPIRE compliant DOI (product) :https://doi.org/10.48670/moi-00107 https://doi.org/10.48670/moi-00107 220 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cems-fire-seasonal https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-seasonal cems-fire-seasonal This dataset offers modeled daily fire danger time series, driven by seasonal weather forecasts. It provides long-range predictions of meteorological conditions conducive to the initiation, spread, and persistence of fires. The fire danger metrics included in this dataset are part of an extensive dataset produced by the Copernicus Emergency Management Service (CEMS) for the European Forest Fire Information System (EFFIS) and the Global Wildfire Information System (GWIS). EFFIS and GWIS are used for monitoring and forecasting fire danger at both European and global scales. The dataset incorporates fire danger indices from the U.S. Forest Service National Fire-Danger Rating System (NFDRS), the Canadian Forest Service Fire Weather Index Rating System (FWI), and the Australian McArthur (Mark 5) rating systems. This dataset was generated by driving the Global ECMWF Fire Forecast (GEFF) model with seasonal weather ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) System 5 (SEAS5) prediction system.These forecasts initially consist of 25 ensemble members until December 2016, referred to as re-forecasts. After that period, they consist of seasonal forecasts with 51 members. It is important to note that the re-forecast dataset was initialized using ERA-Interim analysis data, while forecast simulations from 2016 onward are initialized using ECMWF operational analysis. Therefore, it is suggested that the period 1981-2016 be used as a reference period, while the period 2017-to present as a real time forecast. For both the re-forecast (1981-2016) and forecast periods (2017-present), the temporal resolution is daily forecasts at 12:00 local time, available once a month, with a prediction horizon of 216 days (equivalent to 7 months). The data records in this dataset will be extended over time as seasonal forcing data becomes available. Once the SEAS5 operation ceases, the dataset will be updated with the next ECMWF seasonal system (SEAS6). It is essential to note that this is not a real-time service, as real-time forecasts are accessible through the EFFIS web services. These seasonal forecasts can be used to assess the performance of the forecasting system or to develop tools for statistically correcting forecast errors. ECMWF produces this dataset as the computational center for fire danger forecasting within the Copernicus Emergency Management Service (CEMS) on behalf of the Joint Research Centre, which serves as the managing entity for this service. DATA DESCRIPTION Data type Gridded ensemble Projection Regular latitude-longitude grid Horizontal coverage Global Land Horizontal resolution 1° x 1° Temporal coverage 1981 to present Temporal resolution Daily File format Grib and NetCDF Versions System version of the seasonal forecast Update frequency Monthly on the 5th of each month, one month behind real time. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded ensemble Data type Gridded ensemble Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Land Horizontal coverage Global Land Horizontal resolution 1° x 1° Horizontal resolution 1° x 1° Temporal coverage 1981 to present Temporal coverage 1981 to present Temporal resolution Daily Temporal resolution Daily File format Grib and NetCDF File format Grib and NetCDF Versions System version of the seasonal forecast Versions System version of the seasonal forecast Update frequency Monthly on the 5th of each month, one month behind real time. Update frequency Monthly on the 5th of each month, one month behind real time. MAIN VARIABLES Name Units Description Build-up index Dimensionless The Build-Up Index is a weighted combination of the Duff moisture code and Drought code to indicate the total amount of fuel available for combustion by a moving flame front. The Duff moisture code has the most influence on the Build-up index value. For example, a Duff moisture code value of zero always results in a Build-up index value of zero regardless of what the Drought code value is. The Drought code has the strongest influence on the Build-up index when Duff moisture code values are high. The greatest effect that the Drought code can have is to make the Build-up index value equal to twice the Duff moisture code value. The Build-up index is often used for pre-suppression planning purposes. Burning index Dimensionless The Burning Index measures the difficulty of controlling a fire. It is derived from a combination of Spread component (how fast it will spread) and Energy release component (how much energy will be produced). In this way, it is related to flame length, which, in the Fire Behavior Prediction System, is based on rate of spread and heat per unit area. However, because of differences in the calculations for Burning index and flame length, they are not the same. Drought code Dimensionless The Drought code is an indicator of the moisture content in deep compact organic layers. This code represents a fuel layer at approximately 10-20 cm deep. The Drought code fuels have a very slow drying rate, with a time lag of 52 days. The Drought code scale is open-ended, although the maximum value is about 800. Drought factor Dimensionless The drought factor is a component representing fuel availability. It is is given as a number between 0 and 10 and represents the influence of recent temperatures and rainfall events on fuel availability (see Griffiths 1998 for details). The Drought Factor is partly based on the soil moisture deficit which is commonly calculated in Australia as the Keetch-Byram Drought Index (KBDI) (also available). The KBDI estimates the soil moisture below saturation up to a maximum field capacity of 203.2 mm (i.e. 8 inches) and a minimum of 0 mm. Duff moisture code Dimensionless The Duff moisture code is an indicatore of the moisture content in loosely-compacted organic layers of moderate depth. It is representative of the duff layer that is 5-10 cm deep. Duff moisture code fuels are affected by rain, temperature and relative humidity. Because these fuels are below the forest floor surface, wind speed does not affect the fuel moisture content. The Duff moisture code fuels have a slower drying rate than the Fine fuel moisture code fuels, with a timelag of 12 days. Although the Duff moisture code has an open-ended scale, the highest probable value is in the range of 150. Energy release component J/m2 The Energy release component is a number related to the available energy (British Thermal Unit) per unit area (square foot) within the flaming front at the head of a fire. Daily variations in Energy release component are due to changes in moisture content of the various fuels present, both live and dead. Since this number represents the potential "heat release" per unit area in the flaming zone, it can provide guidance to several important fire activities. It may also be considered a composite fuel moisture value as it reflects the contribution that all live and dead fuels have to potential fire intensity. The Energy release component is a cumulative or "build-up" type of index. As live fuels cure and dead fuels dry, the Energy release component values get higher thus providing a good reflection of drought conditions. The scale is open-ended or unlimited and, as with other National Forest Danger Rating System components, is relative. Fine fuel moisture code Dimensionless The Fine fuel moisture code is an indicatore of the moisture content in litter and other cured fine fuels (needles, mosses, twigs less than 1 cm in diameter). The Fine fuel moisture code is representative of the top litter layer less than 1-2 cm deep. Fine fuel moisture code values change rapidly because of a high surface area to volume ratio, and direct exposure to changing environmental conditions. The Fine fuel moisture code scale ranges from 0-99 and is the only component of the Fire weather index system which does not have an open-ended scale. Generally, fires begin to ignite at Fine fuel moisture code values near 70, and the maximum probable value that will ever be achieved is 96. Fire daily severity rating Dimensionless Numeric rating of the difficulty of controlling fires. It is an exponential transformation of the Fire weather index and more accurately reflects the expected efforts required for fire suppression as it increases exponentially as the Fire weather index is above a certain value. Fire danger index Dimensionless The Fire danger index is a metric related to the chances of a fire starting, its rate of spread, its intensity, and its difficulty of suppression. It is open ended however a value of 50 and above is considered extreme in most vegetation Fire weather index Dimensionless The Fire weather index is a combination of Initial spread index and Build-up index, and is a numerical rating of the potential frontal fire intensity. In effect, it indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed. Fire weather index values are not upper bounded however a value of 50 is considered as extreme in many places. The Fire weather index is used for general public information about fire danger conditions. Ignition component % The Ignition component measures the probability a firebrand will require suppression action. Since it is expressed as a probability, it ranges on a scale of 0 to 100. An Ignition component of 100 means that every firebrand will cause a fire requiring action if it contacts a receptive fuel. Likewise an Ignition component of 0 would mean that no firebrand would cause a fire requiring suppression action under those conditions. Initial fire spread index Dimensionless The Initial fire spread index combines the Fine fuel moisture code and wind speed to indicate the expected rate of fire spread. Generally, a 13 km h-1 increase in wind speed will double the Initial spread index value. The Initial spread index is accepted as a good indicator of fire spread in open light fuel stands with wind speeds up to 40 km h-1. Keetch-Byram drought index Dimensionless The Keetch-Byram drought index (KBDI) is a number representing the net effect of evapotranspiration and precipitation in producing cumulative moisture deficiency in deep duff and upper soil layers. It is a continuous index, relating to the flammability of organic material in the ground.The Keetch-Byram drought index attempts to measure the amount of precipitation necessary to return the soil to saturated conditions. It is a closed system ranging from 0 to 200 units and represents a moisture regime from 0 to 20 cm of water through the soil layer. At 20 cm of water, the Keetch-Byram drought index assumes saturation. Zero is the point of no moisture deficiency and 200 is the maximum drought that is possible. At any point along the scale, the index number indicates the amount of net rainfall that is required to reduce the index to zero, or saturation. KBDI = 0 - 50: Soil moisture and large class fuel moistures are high and do not contribute much to fire intensity. Typical of spring dormant season following winter precipitation. KBDI = 50 - 100: Typical of late spring, early growing season. Lower litter and duff layers are drying and beginning to contribute to fire intensity. KBDI = 100 - 150: Typical of late summer, early fall. Lower litter and duff layers actively contribute to fire intensity and will burn actively. KBDI = 150 - 200: Often associated with more severe drought with increased wildfire occurrence. Intense, deep burning fires with significant downwind spotting can be expected. Live fuels can also be expected to burn actively at these levels. Spread component Dimensionless The Spread component is a measure of the spead at which a headfire would spread. The spread component is numerically equal to the theoretical ideal rate of spread expressed in feet-per-minute however is considered as a dimensionless variable. The Spread component is expressed on an open-ended scale; thus it has no upper limit. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Build-up index Dimensionless The Build-Up Index is a weighted combination of the Duff moisture code and Drought code to indicate the total amount of fuel available for combustion by a moving flame front. The Duff moisture code has the most influence on the Build-up index value. For example, a Duff moisture code value of zero always results in a Build-up index value of zero regardless of what the Drought code value is. The Drought code has the strongest influence on the Build-up index when Duff moisture code values are high. The greatest effect that the Drought code can have is to make the Build-up index value equal to twice the Duff moisture code value. The Build-up index is often used for pre-suppression planning purposes. Build-up index Dimensionless The Build-Up Index is a weighted combination of the Duff moisture code and Drought code to indicate the total amount of fuel available for combustion by a moving flame front. The Duff moisture code has the most influence on the Build-up index value. For example, a Duff moisture code value of zero always results in a Build-up index value of zero regardless of what the Drought code value is. The Drought code has the strongest influence on the Build-up index when Duff moisture code values are high. The greatest effect that the Drought code can have is to make the Build-up index value equal to twice the Duff moisture code value. The Build-up index is often used for pre-suppression planning purposes. Burning index Dimensionless The Burning Index measures the difficulty of controlling a fire. It is derived from a combination of Spread component (how fast it will spread) and Energy release component (how much energy will be produced). In this way, it is related to flame length, which, in the Fire Behavior Prediction System, is based on rate of spread and heat per unit area. However, because of differences in the calculations for Burning index and flame length, they are not the same. Burning index Dimensionless The Burning Index measures the difficulty of controlling a fire. It is derived from a combination of Spread component (how fast it will spread) and Energy release component (how much energy will be produced). In this way, it is related to flame length, which, in the Fire Behavior Prediction System, is based on rate of spread and heat per unit area. However, because of differences in the calculations for Burning index and flame length, they are not the same. Drought code Dimensionless The Drought code is an indicator of the moisture content in deep compact organic layers. This code represents a fuel layer at approximately 10-20 cm deep. The Drought code fuels have a very slow drying rate, with a time lag of 52 days. The Drought code scale is open-ended, although the maximum value is about 800. Drought code Dimensionless The Drought code is an indicator of the moisture content in deep compact organic layers. This code represents a fuel layer at approximately 10-20 cm deep. The Drought code fuels have a very slow drying rate, with a time lag of 52 days. The Drought code scale is open-ended, although the maximum value is about 800. Drought factor Dimensionless The drought factor is a component representing fuel availability. It is is given as a number between 0 and 10 and represents the influence of recent temperatures and rainfall events on fuel availability (see Griffiths 1998 for details). The Drought Factor is partly based on the soil moisture deficit which is commonly calculated in Australia as the Keetch-Byram Drought Index (KBDI) (also available). The KBDI estimates the soil moisture below saturation up to a maximum field capacity of 203.2 mm (i.e. 8 inches) and a minimum of 0 mm. Drought factor Dimensionless The drought factor is a component representing fuel availability. It is is given as a number between 0 and 10 and represents the influence of recent temperatures and rainfall events on fuel availability (see Griffiths 1998 for details). The Drought Factor is partly based on the soil moisture deficit which is commonly calculated in Australia as the Keetch-Byram Drought Index (KBDI) (also available). The KBDI estimates the soil moisture below saturation up to a maximum field capacity of 203.2 mm (i.e. 8 inches) and a minimum of 0 mm. Duff moisture code Dimensionless The Duff moisture code is an indicatore of the moisture content in loosely-compacted organic layers of moderate depth. It is representative of the duff layer that is 5-10 cm deep. Duff moisture code fuels are affected by rain, temperature and relative humidity. Because these fuels are below the forest floor surface, wind speed does not affect the fuel moisture content. The Duff moisture code fuels have a slower drying rate than the Fine fuel moisture code fuels, with a timelag of 12 days. Although the Duff moisture code has an open-ended scale, the highest probable value is in the range of 150. Duff moisture code Dimensionless The Duff moisture code is an indicatore of the moisture content in loosely-compacted organic layers of moderate depth. It is representative of the duff layer that is 5-10 cm deep. Duff moisture code fuels are affected by rain, temperature and relative humidity. Because these fuels are below the forest floor surface, wind speed does not affect the fuel moisture content. The Duff moisture code fuels have a slower drying rate than the Fine fuel moisture code fuels, with a timelag of 12 days. Although the Duff moisture code has an open-ended scale, the highest probable value is in the range of 150. Energy release component J/m2 The Energy release component is a number related to the available energy (British Thermal Unit) per unit area (square foot) within the flaming front at the head of a fire. Daily variations in Energy release component are due to changes in moisture content of the various fuels present, both live and dead. Since this number represents the potential "heat release" per unit area in the flaming zone, it can provide guidance to several important fire activities. It may also be considered a composite fuel moisture value as it reflects the contribution that all live and dead fuels have to potential fire intensity. The Energy release component is a cumulative or "build-up" type of index. As live fuels cure and dead fuels dry, the Energy release component values get higher thus providing a good reflection of drought conditions. The scale is open-ended or unlimited and, as with other National Forest Danger Rating System components, is relative. Energy release component J/m2 The Energy release component is a number related to the available energy (British Thermal Unit) per unit area (square foot) within the flaming front at the head of a fire. Daily variations in Energy release component are due to changes in moisture content of the various fuels present, both live and dead. Since this number represents the potential "heat release" per unit area in the flaming zone, it can provide guidance to several important fire activities. It may also be considered a composite fuel moisture value as it reflects the contribution that all live and dead fuels have to potential fire intensity. The Energy release component is a cumulative or "build-up" type of index. As live fuels cure and dead fuels dry, the Energy release component values get higher thus providing a good reflection of drought conditions. The scale is open-ended or unlimited and, as with other National Forest Danger Rating System components, is relative. Fine fuel moisture code Dimensionless The Fine fuel moisture code is an indicatore of the moisture content in litter and other cured fine fuels (needles, mosses, twigs less than 1 cm in diameter). The Fine fuel moisture code is representative of the top litter layer less than 1-2 cm deep. Fine fuel moisture code values change rapidly because of a high surface area to volume ratio, and direct exposure to changing environmental conditions. The Fine fuel moisture code scale ranges from 0-99 and is the only component of the Fire weather index system which does not have an open-ended scale. Generally, fires begin to ignite at Fine fuel moisture code values near 70, and the maximum probable value that will ever be achieved is 96. Fine fuel moisture code Dimensionless The Fine fuel moisture code is an indicatore of the moisture content in litter and other cured fine fuels (needles, mosses, twigs less than 1 cm in diameter). The Fine fuel moisture code is representative of the top litter layer less than 1-2 cm deep. Fine fuel moisture code values change rapidly because of a high surface area to volume ratio, and direct exposure to changing environmental conditions. The Fine fuel moisture code scale ranges from 0-99 and is the only component of the Fire weather index system which does not have an open-ended scale. Generally, fires begin to ignite at Fine fuel moisture code values near 70, and the maximum probable value that will ever be achieved is 96. Fire daily severity rating Dimensionless Numeric rating of the difficulty of controlling fires. It is an exponential transformation of the Fire weather index and more accurately reflects the expected efforts required for fire suppression as it increases exponentially as the Fire weather index is above a certain value. Fire daily severity rating Dimensionless Numeric rating of the difficulty of controlling fires. It is an exponential transformation of the Fire weather index and more accurately reflects the expected efforts required for fire suppression as it increases exponentially as the Fire weather index is above a certain value. Fire danger index Dimensionless The Fire danger index is a metric related to the chances of a fire starting, its rate of spread, its intensity, and its difficulty of suppression. It is open ended however a value of 50 and above is considered extreme in most vegetation Fire danger index Dimensionless The Fire danger index is a metric related to the chances of a fire starting, its rate of spread, its intensity, and its difficulty of suppression. It is open ended however a value of 50 and above is considered extreme in most vegetation Fire weather index Dimensionless The Fire weather index is a combination of Initial spread index and Build-up index, and is a numerical rating of the potential frontal fire intensity. In effect, it indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed. Fire weather index values are not upper bounded however a value of 50 is considered as extreme in many places. The Fire weather index is used for general public information about fire danger conditions. Fire weather index Dimensionless The Fire weather index is a combination of Initial spread index and Build-up index, and is a numerical rating of the potential frontal fire intensity. In effect, it indicates fire intensity by combining the rate of fire spread with the amount of fuel being consumed. Fire weather index values are not upper bounded however a value of 50 is considered as extreme in many places. The Fire weather index is used for general public information about fire danger conditions. Ignition component % The Ignition component measures the probability a firebrand will require suppression action. Since it is expressed as a probability, it ranges on a scale of 0 to 100. An Ignition component of 100 means that every firebrand will cause a fire requiring action if it contacts a receptive fuel. Likewise an Ignition component of 0 would mean that no firebrand would cause a fire requiring suppression action under those conditions. Ignition component % The Ignition component measures the probability a firebrand will require suppression action. Since it is expressed as a probability, it ranges on a scale of 0 to 100. An Ignition component of 100 means that every firebrand will cause a fire requiring action if it contacts a receptive fuel. Likewise an Ignition component of 0 would mean that no firebrand would cause a fire requiring suppression action under those conditions. Initial fire spread index Dimensionless The Initial fire spread index combines the Fine fuel moisture code and wind speed to indicate the expected rate of fire spread. Generally, a 13 km h-1 increase in wind speed will double the Initial spread index value. The Initial spread index is accepted as a good indicator of fire spread in open light fuel stands with wind speeds up to 40 km h-1. Initial fire spread index Dimensionless The Initial fire spread index combines the Fine fuel moisture code and wind speed to indicate the expected rate of fire spread. Generally, a 13 km h-1 increase in wind speed will double the Initial spread index value. The Initial spread index is accepted as a good indicator of fire spread in open light fuel stands with wind speeds up to 40 km h-1. Keetch-Byram drought index Dimensionless The Keetch-Byram drought index (KBDI) is a number representing the net effect of evapotranspiration and precipitation in producing cumulative moisture deficiency in deep duff and upper soil layers. It is a continuous index, relating to the flammability of organic material in the ground.The Keetch-Byram drought index attempts to measure the amount of precipitation necessary to return the soil to saturated conditions. It is a closed system ranging from 0 to 200 units and represents a moisture regime from 0 to 20 cm of water through the soil layer. At 20 cm of water, the Keetch-Byram drought index assumes saturation. Zero is the point of no moisture deficiency and 200 is the maximum drought that is possible. At any point along the scale, the index number indicates the amount of net rainfall that is required to reduce the index to zero, or saturation. KBDI = 0 - 50: Soil moisture and large class fuel moistures are high and do not contribute much to fire intensity. Typical of spring dormant season following winter precipitation. KBDI = 50 - 100: Typical of late spring, early growing season. Lower litter and duff layers are drying and beginning to contribute to fire intensity. KBDI = 100 - 150: Typical of late summer, early fall. Lower litter and duff layers actively contribute to fire intensity and will burn actively. KBDI = 150 - 200: Often associated with more severe drought with increased wildfire occurrence. Intense, deep burning fires with significant downwind spotting can be expected. Live fuels can also be expected to burn actively at these levels. Keetch-Byram drought index Dimensionless The Keetch-Byram drought index (KBDI) is a number representing the net effect of evapotranspiration and precipitation in producing cumulative moisture deficiency in deep duff and upper soil layers. It is a continuous index, relating to the flammability of organic material in the ground.The Keetch-Byram drought index attempts to measure the amount of precipitation necessary to return the soil to saturated conditions. It is a closed system ranging from 0 to 200 units and represents a moisture regime from 0 to 20 cm of water through the soil layer. At 20 cm of water, the Keetch-Byram drought index assumes saturation. Zero is the point of no moisture deficiency and 200 is the maximum drought that is possible. At any point along the scale, the index number indicates the amount of net rainfall that is required to reduce the index to zero, or saturation. KBDI = 0 - 50: Soil moisture and large class fuel moistures are high and do not contribute much to fire intensity. Typical of spring dormant season following winter precipitation. KBDI = 50 - 100: Typical of late spring, early growing season. Lower litter and duff layers are drying and beginning to contribute to fire intensity. KBDI = 100 - 150: Typical of late summer, early fall. Lower litter and duff layers actively contribute to fire intensity and will burn actively. KBDI = 150 - 200: Often associated with more severe drought with increased wildfire occurrence. Intense, deep burning fires with significant downwind spotting can be expected. Live fuels can also be expected to burn actively at these levels. Spread component Dimensionless The Spread component is a measure of the spead at which a headfire would spread. The spread component is numerically equal to the theoretical ideal rate of spread expressed in feet-per-minute however is considered as a dimensionless variable. The Spread component is expressed on an open-ended scale; thus it has no upper limit. Spread component Dimensionless The Spread component is a measure of the spead at which a headfire would spread. The spread component is numerically equal to the theoretical ideal rate of spread expressed in feet-per-minute however is considered as a dimensionless variable. The Spread component is expressed on an open-ended scale; thus it has no upper limit. 221 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-cerra-land https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-cerra-land reanalysis-cerra-land The Copernicus European Regional ReAnalysis Land (CERRA-Land) dataset provides spatially and temporally consistent historical reconstructions of surface and soil variables at the same horizontal resolution as the CERRA high-resolution reanalysis. The need for precipitation and surface variables at an ever-increasing spatial and temporal resolution is a recurrent demand. These variables allow, among other things, to address water resource management issues and to carry out climate change impact studies. Regional surface reanalyses are a way to reconstruct these variables for past periods covering several decades using state-of-the-art models. Reanalysis combines model data with observations into a complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but usually at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved, reprocessed versions of the original observations, which all benefit the quality of the reanalysis product. The dataset was produced using the CERRA-Land system which consists of a land surface modelling platform SURFEX (V8.1) and a daily (24-h) total accumulated precipitation assimilation system. Most of the data are forecasts generated based on the open-loop integration of the SURFEX. The observations are not directly used in their production but have an indirect influence through the atmospheric forcing (e.g. 2m temperature) from the CERRA high-resolution reanalysis and precipitation reanalysis system used to integrate in time the SURFEX model. No downscaling method was used to build up the input forcing data because the CERRA-Land system has the same integration domain (e.g. grid spacing, orography) as the CERRA high-resolution atmospheric reanalysis. SURFEX was run offline, that is without feedback to the atmospheric analysis performed in the CERRA data assimilation cycles. To solve both heat and water transfer equations in the soil, a discretisation of the soil into 14 layers was used. The surface precipitation analysis and the 12 snow layers model included in the CERRA-Land system significantly improve the representation of the snowpack over Europe in comparison with the CERRA dataset. This dataset describes the evolution of soil moisture, soil temperature and snowpack in a consistent view over several decades at an enhanced resolution compared to ERA5 and ERA5-Land. The temporal and spatial resolutions of CERRA-Land data recommend this dataset, for example, for water resource management and climate change studies. The added value of the CERRA-Land data with respect to the global reanalysis products is expected to come, for example, with the higher horizontal resolution that permits the usage of a better description of the model topography and physiographic data. More information about the CERRA-Land dataset can be found in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Lambert conformal conical grid Horizontal coverage Europe. The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Horizontal resolution 5.5 km x 5.5 km Vertical coverage From surface to a soil depth of 12m Vertical resolution Single level for surface parameters 14 layers for soil parameters. The vertical discretisation (bottom depth of each layer in meters) is 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12m Temporal coverage September 1984 to April 2021 Temporal resolution Analysis: daily (24-h) for total precipitation Forecasts: hourly File format GRIB2 Update frequency New data are expected to be added towards the end of 2023 DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert conformal conical grid Projection Lambert conformal conical grid Horizontal coverage Europe. The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Horizontal coverage Europe. The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Horizontal resolution 5.5 km x 5.5 km Horizontal resolution 5.5 km x 5.5 km Vertical coverage From surface to a soil depth of 12m Vertical coverage From surface to a soil depth of 12m Vertical resolution Single level for surface parameters 14 layers for soil parameters. The vertical discretisation (bottom depth of each layer in meters) is 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12m Vertical resolution Single level for surface parameters 14 layers for soil parameters. The vertical discretisation (bottom depth of each layer in meters) is 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12m Single level for surface parameters 14 layers for soil parameters. The vertical discretisation (bottom depth of each layer in meters) is 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12m Temporal coverage September 1984 to April 2021 Temporal coverage September 1984 to April 2021 Temporal resolution Analysis: daily (24-h) for total precipitation Forecasts: hourly Temporal resolution Analysis: daily (24-h) for total precipitation Forecasts: hourly Analysis: daily (24-h) for total precipitation Forecasts: hourly File format GRIB2 File format GRIB2 Update frequency New data are expected to be added towards the end of 2023 Update frequency New data are expected to be added towards the end of 2023 MAIN VARIABLES Name Units Description Albedo % The albedo [0-100%] is the total reflectance of downward solar radiation at the surface over the grid box. The albedo is the ratio of one hour time-integrated surface solar radiation upward by the one hour time-integrated surface solar radiation downward. Multiplying the albedo with the one hour accumulated downward solar radiation gives the one hour accumulated upward solar radiation. Evaporation kg m-2 Evaporation is the amount of water that has evaporated from the earth’s surface from the initial time of the forecast to the forecast time step. It is given as a mean for the grid area between the three surface types in the grid - inland water, natural land and urban. Hence, evaporation is represented by negative values and positive values represent condensation. By model convention downward fluxes are positive. It is an accumulated field. Fraction of snow cover dimensionless It represents the fraction of natural land covered by snow. It is an instantaneous variable and takes values between 0 and 1. Lake bottom temperature K Temperature of water at the bottom of inland water bodies (lakes). The model keeps lake depth and surface area (or fractional cover) constant in time. It is an instantaneous field. Lake depth m Depth of inland water. It is defined for positive fractions only. It is a static field. Lake ice depth m The thickness of ice on inland water bodies (lakes). A single ice layer is represented. This parameter is the thickness of that ice layer. It is an instantaneous field. Lake ice temperature K The temperature of the uppermost surface of ice on inland water bodies (lakes). A single ice layer is represented. It is an instantaneous field. Lake mix-layer depth m The thickness of the upper most layer of an inland water body (lake) that is well mixed and has a near constant temperature with depth (uniform distribution of temperature). The Flake model  represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. It is an instantaneous field. Lake mix-layer temperature K The temperature of the upper most layer of inland water bodies (lakes) that is well mixed. The Flake model represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. It is an instantaneous field. Lake shape factor dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes). It is used to calculate the lake bottom temperature and other lake-related parameters. The Flake model represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. It is an instantaneous field. Lake total layer temperature K The mean temperature of total water column in inland water bodies (lakes). The Flake model represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. This parameter is the mean over the two layers. It is an instantaneous field. Land-sea mask dimensionless The land-sea mask is a field that contains, for every grid point, the proportion of land (including inland water) in the grid box. It is the sum of the three fractions - natural land, urban and inland water. The parameter is dimensionless and the values are between 0 (sea) and 1 (land). It is a static field. Liquid volumetric soil moisture (non-frozen) m3 m-3 The volume concentration of liquid water only. The vertical discretisation (bottom depth of each layer in metres) is as follows - 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. A liquid volumetric soil moisture is available for each soil layer. It is an instantaneous field. Orography m The height above sea level of the land surface. This variable does not change with snow cover. It is a static field. Percolation kg m-2 The mass per unit area of water that drains below the deepest soil level in the model. The drainage is accumulated from the initial time of the forecast to the forecast time step. This variable is calculated for the natural land, including soil, vegetation and snow (not for urban and water bodies fraction). It is an accumulated field. Skin temperature K Average air temperature at the surface of each grid box. The skin temperature is an average of temperatures given by the three surface types in the grid - inland water, natural land and urban. It is an instantaneous field. Snow albedo dimensionless It is defined as the fraction of solar (shortwave) radiation reflected by the snow, across the solar spectrum, for both direct and diffuse radiation. Values vary between 0 and 1. It is an instantaneous field. Snow density kg m-3 The mean snow density is calculated as the ratio of snow depth water equivalent by the snow depth. It is an instantaneous field. Snow depth m Snow thickness on the ground. It is an instantaneous field. Snow depth water equivalent kg m-2 The mass of liquid water obtained from melting the snow per unit area. This is equivalent to the depth of the liquid water in units of mm. It is an instantaneous field. Snow melt kg m-2 Melting of snow. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. It is an accumulated field. Soil heat flux W m-2 The soil heat flux is the energy receive by the soil to heat it per unit of surface and time. The soil heat flux is positive when the soil receives energy (warms) and negative when the soil loses energy (cools). It is an instantaneous variable. Soil temperature K The model has 14 soil layers. The vertical discretisation (bottom depth of each layer in metres) is as follows - 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. The soil temperature is available for each soil layer.It is an instantaneous field. Surface latent heat flux J m-2 The surface latent heat flux is the accumulated exchange of latent heat (due to phase transitions - evaporation, condensation) with the surface (ground and water) through turbulent diffusion from the initial time of the forecast to the forecast time step. It is given as a mean for the grid area between the three surface types in the grid - inland water, natural land and urban. By model convention downward fluxes are positive. It is an accumulated field. Surface net solar radiation J m-2 The surface net solar radiation is the accumulated solar short-wave radiation that is absorbed at the surface from the initial time of the forecast to the forecast time step. It is calculated as the difference between the downward solar energy and the upward solar energy at the surface. By model convention downward fluxes are positive. It is an accumulated field. Surface net thermal radiation J m-2 The net thermal energy at the surface is accumulated from the initial time of the forecast to the forecast time step. It is calculated as the difference between the downward thermal energy and the upward thermal energy at the surface. By model convention downward fluxes are positive. It is an accumulated field. Surface roughness m The surface roughness describes the aerodynamic roughness length. It is given as a mean for the grid area between the three surface types in the grid (inland water, natural land and urban) and has missing values over the ocean. The roughness length of the surface is the height above the surface at which the wind profile is assumed to become zero. Each grid point has one value representing the mean over the grid point. The effective surface roughness is depending on the orographic component (constant part), the snow depth, the evolution of the Leaf Area Index and the fraction of vegetation, which is different for each month. It is an instantaneous field. Surface runoff kg m-2 The mass per unit area of water at the surface when saturation occurs. This variable is calculated for the natural land, including soil, vegetation and snow. It is an accumulated field. Surface sensible heat flux J m-2 The surface sensible heat flux is the accumulated exchange of heat (no phase transition) with the surface (ground and water) through turbulent diffusion from the initial time of the forecast to the forecast time step. It is given as a mean for the grid area between the three surface types in the grid - inland water, natural land and urban. By model convention downward fluxes are positive. It is an accumulated field. Surface solar radiation downwards J m-2 The surface solar radiation downward is the accumulated total (direct and diffuse) solar short-wave radiation reaching the surface from the initial time of the forecast to the forecast time step. By model convention downward fluxes are positive. It is an accumulated field. Surface thermal radiation downwards J m-2 The surface thermal radiation downward is the amount of thermal (long-wave) radiation reaching the surface accumulated from the initial time of the forecast to the forecast time step. By model convention downward fluxes are positive. It is an accumulated field. Temperature of snow layer K Mean temperature of the 12 snow layers. It is an instantaneous field. Total precipitation kg m-2 Total daily precipitation is the amount of precipitation falling at the surface. It includes all kind of precipitation forms as convective precipitation, large scale precipitation, liquid and solid precipitation. The total precipitation is available only for the analyses at 06h00 UTC. It is an accumulated field from the previous day at 06 UTC to the present day at 06 UTC. Volumetric soil moisture m3 m-3 The volume concentration of liquid and ice water. The vertical discretisation (bottom depth of each layer in metres) is as follows - 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. The volumetric soil moisture is available for each soil layer. It is an instantaneous field. Volumetric transpiration stress-onset (soil moisture) m3 m-3 The soil moisture is the water content of a soil after gravitational drainage. When the water content of the soil reaches this value, the water cannot drain any more by gravity. The vertical discretisation (bottom depth of each layer in metres) is as follows - 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. It is a static variable. Volumetric wilting point m3 m-3 Model soil water content at which the vegetation wilts and can no longer recover. When the soil moisture reaches the wilting point, the vegetation is not able to extract the soil water. The soil moisture content is too low to be absorbed by the vegetation. The vertical discretisation (bottom depth of each layer in metres) is as follows - 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. It is a static variable. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Albedo % The albedo [0-100%] is the total reflectance of downward solar radiation at the surface over the grid box. The albedo is the ratio of one hour time-integrated surface solar radiation upward by the one hour time-integrated surface solar radiation downward. Multiplying the albedo with the one hour accumulated downward solar radiation gives the one hour accumulated upward solar radiation. Albedo % The albedo [0-100%] is the total reflectance of downward solar radiation at the surface over the grid box. The albedo is the ratio of one hour time-integrated surface solar radiation upward by the one hour time-integrated surface solar radiation downward. Multiplying the albedo with the one hour accumulated downward solar radiation gives the one hour accumulated upward solar radiation. Evaporation kg m-2 Evaporation is the amount of water that has evaporated from the earth’s surface from the initial time of the forecast to the forecast time step. It is given as a mean for the grid area between the three surface types in the grid - inland water, natural land and urban. Hence, evaporation is represented by negative values and positive values represent condensation. By model convention downward fluxes are positive. It is an accumulated field. Evaporation kg m-2 Evaporation is the amount of water that has evaporated from the earth’s surface from the initial time of the forecast to the forecast time step. It is given as a mean for the grid area between the three surface types in the grid - inland water, natural land and urban. Hence, evaporation is represented by negative values and positive values represent condensation. By model convention downward fluxes are positive. It is an accumulated field. Fraction of snow cover dimensionless It represents the fraction of natural land covered by snow. It is an instantaneous variable and takes values between 0 and 1. Fraction of snow cover dimensionless It represents the fraction of natural land covered by snow. It is an instantaneous variable and takes values between 0 and 1. Lake bottom temperature K Temperature of water at the bottom of inland water bodies (lakes). The model keeps lake depth and surface area (or fractional cover) constant in time. It is an instantaneous field. Lake bottom temperature K Temperature of water at the bottom of inland water bodies (lakes). The model keeps lake depth and surface area (or fractional cover) constant in time. It is an instantaneous field. Lake depth m Depth of inland water. It is defined for positive fractions only. It is a static field. Lake depth m Depth of inland water. It is defined for positive fractions only. It is a static field. Lake ice depth m The thickness of ice on inland water bodies (lakes). A single ice layer is represented. This parameter is the thickness of that ice layer. It is an instantaneous field. Lake ice depth m The thickness of ice on inland water bodies (lakes). A single ice layer is represented. This parameter is the thickness of that ice layer. It is an instantaneous field. Lake ice temperature K The temperature of the uppermost surface of ice on inland water bodies (lakes). A single ice layer is represented. It is an instantaneous field. Lake ice temperature K The temperature of the uppermost surface of ice on inland water bodies (lakes). A single ice layer is represented. It is an instantaneous field. Lake mix-layer depth m The thickness of the upper most layer of an inland water body (lake) that is well mixed and has a near constant temperature with depth (uniform distribution of temperature). The Flake model  represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. It is an instantaneous field. Lake mix-layer depth m The thickness of the upper most layer of an inland water body (lake) that is well mixed and has a near constant temperature with depth (uniform distribution of temperature). The Flake model  represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. It is an instantaneous field. Lake mix-layer temperature K The temperature of the upper most layer of inland water bodies (lakes) that is well mixed. The Flake model represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. It is an instantaneous field. Lake mix-layer temperature K The temperature of the upper most layer of inland water bodies (lakes) that is well mixed. The Flake model represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. It is an instantaneous field. Lake shape factor dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes). It is used to calculate the lake bottom temperature and other lake-related parameters. The Flake model represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. It is an instantaneous field. Lake shape factor dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes). It is used to calculate the lake bottom temperature and other lake-related parameters. The Flake model represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. It is an instantaneous field. Lake total layer temperature K The mean temperature of total water column in inland water bodies (lakes). The Flake model represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. This parameter is the mean over the two layers. It is an instantaneous field. Lake total layer temperature K The mean temperature of total water column in inland water bodies (lakes). The Flake model represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. This parameter is the mean over the two layers. It is an instantaneous field. Land-sea mask dimensionless The land-sea mask is a field that contains, for every grid point, the proportion of land (including inland water) in the grid box. It is the sum of the three fractions - natural land, urban and inland water. The parameter is dimensionless and the values are between 0 (sea) and 1 (land). It is a static field. Land-sea mask dimensionless The land-sea mask is a field that contains, for every grid point, the proportion of land (including inland water) in the grid box. It is the sum of the three fractions - natural land, urban and inland water. The parameter is dimensionless and the values are between 0 (sea) and 1 (land). It is a static field. Liquid volumetric soil moisture (non-frozen) m3 m-3 The volume concentration of liquid water only. The vertical discretisation (bottom depth of each layer in metres) is as follows - 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. A liquid volumetric soil moisture is available for each soil layer. It is an instantaneous field. Liquid volumetric soil moisture (non-frozen) m3 m-3 The volume concentration of liquid water only. The vertical discretisation (bottom depth of each layer in metres) is as follows - 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. A liquid volumetric soil moisture is available for each soil layer. It is an instantaneous field. Orography m The height above sea level of the land surface. This variable does not change with snow cover. It is a static field. Orography m The height above sea level of the land surface. This variable does not change with snow cover. It is a static field. Percolation kg m-2 The mass per unit area of water that drains below the deepest soil level in the model. The drainage is accumulated from the initial time of the forecast to the forecast time step. This variable is calculated for the natural land, including soil, vegetation and snow (not for urban and water bodies fraction). It is an accumulated field. Percolation kg m-2 The mass per unit area of water that drains below the deepest soil level in the model. The drainage is accumulated from the initial time of the forecast to the forecast time step. This variable is calculated for the natural land, including soil, vegetation and snow (not for urban and water bodies fraction). It is an accumulated field. Skin temperature K Average air temperature at the surface of each grid box. The skin temperature is an average of temperatures given by the three surface types in the grid - inland water, natural land and urban. It is an instantaneous field. Skin temperature K Average air temperature at the surface of each grid box. The skin temperature is an average of temperatures given by the three surface types in the grid - inland water, natural land and urban. It is an instantaneous field. Snow albedo dimensionless It is defined as the fraction of solar (shortwave) radiation reflected by the snow, across the solar spectrum, for both direct and diffuse radiation. Values vary between 0 and 1. It is an instantaneous field. Snow albedo dimensionless It is defined as the fraction of solar (shortwave) radiation reflected by the snow, across the solar spectrum, for both direct and diffuse radiation. Values vary between 0 and 1. It is an instantaneous field. Snow density kg m-3 The mean snow density is calculated as the ratio of snow depth water equivalent by the snow depth. It is an instantaneous field. Snow density kg m-3 The mean snow density is calculated as the ratio of snow depth water equivalent by the snow depth. It is an instantaneous field. Snow depth m Snow thickness on the ground. It is an instantaneous field. Snow depth m Snow thickness on the ground. It is an instantaneous field. Snow depth water equivalent kg m-2 The mass of liquid water obtained from melting the snow per unit area. This is equivalent to the depth of the liquid water in units of mm. It is an instantaneous field. Snow depth water equivalent kg m-2 The mass of liquid water obtained from melting the snow per unit area. This is equivalent to the depth of the liquid water in units of mm. It is an instantaneous field. Snow melt kg m-2 Melting of snow. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. It is an accumulated field. Snow melt kg m-2 Melting of snow. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. It is an accumulated field. Soil heat flux W m-2 The soil heat flux is the energy receive by the soil to heat it per unit of surface and time. The soil heat flux is positive when the soil receives energy (warms) and negative when the soil loses energy (cools). It is an instantaneous variable. Soil heat flux W m-2 The soil heat flux is the energy receive by the soil to heat it per unit of surface and time. The soil heat flux is positive when the soil receives energy (warms) and negative when the soil loses energy (cools). It is an instantaneous variable. Soil temperature K The model has 14 soil layers. The vertical discretisation (bottom depth of each layer in metres) is as follows - 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. The soil temperature is available for each soil layer.It is an instantaneous field. Soil temperature K The model has 14 soil layers. The vertical discretisation (bottom depth of each layer in metres) is as follows - 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. The soil temperature is available for each soil layer.It is an instantaneous field. Surface latent heat flux J m-2 The surface latent heat flux is the accumulated exchange of latent heat (due to phase transitions - evaporation, condensation) with the surface (ground and water) through turbulent diffusion from the initial time of the forecast to the forecast time step. It is given as a mean for the grid area between the three surface types in the grid - inland water, natural land and urban. By model convention downward fluxes are positive. It is an accumulated field. Surface latent heat flux J m-2 The surface latent heat flux is the accumulated exchange of latent heat (due to phase transitions - evaporation, condensation) with the surface (ground and water) through turbulent diffusion from the initial time of the forecast to the forecast time step. It is given as a mean for the grid area between the three surface types in the grid - inland water, natural land and urban. By model convention downward fluxes are positive. It is an accumulated field. Surface net solar radiation J m-2 The surface net solar radiation is the accumulated solar short-wave radiation that is absorbed at the surface from the initial time of the forecast to the forecast time step. It is calculated as the difference between the downward solar energy and the upward solar energy at the surface. By model convention downward fluxes are positive. It is an accumulated field. Surface net solar radiation J m-2 The surface net solar radiation is the accumulated solar short-wave radiation that is absorbed at the surface from the initial time of the forecast to the forecast time step. It is calculated as the difference between the downward solar energy and the upward solar energy at the surface. By model convention downward fluxes are positive. It is an accumulated field. Surface net thermal radiation J m-2 The net thermal energy at the surface is accumulated from the initial time of the forecast to the forecast time step. It is calculated as the difference between the downward thermal energy and the upward thermal energy at the surface. By model convention downward fluxes are positive. It is an accumulated field. Surface net thermal radiation J m-2 The net thermal energy at the surface is accumulated from the initial time of the forecast to the forecast time step. It is calculated as the difference between the downward thermal energy and the upward thermal energy at the surface. By model convention downward fluxes are positive. It is an accumulated field. Surface roughness m The surface roughness describes the aerodynamic roughness length. It is given as a mean for the grid area between the three surface types in the grid (inland water, natural land and urban) and has missing values over the ocean. The roughness length of the surface is the height above the surface at which the wind profile is assumed to become zero. Each grid point has one value representing the mean over the grid point. The effective surface roughness is depending on the orographic component (constant part), the snow depth, the evolution of the Leaf Area Index and the fraction of vegetation, which is different for each month. It is an instantaneous field. Surface roughness m The surface roughness describes the aerodynamic roughness length. It is given as a mean for the grid area between the three surface types in the grid (inland water, natural land and urban) and has missing values over the ocean. The roughness length of the surface is the height above the surface at which the wind profile is assumed to become zero. Each grid point has one value representing the mean over the grid point. The effective surface roughness is depending on the orographic component (constant part), the snow depth, the evolution of the Leaf Area Index and the fraction of vegetation, which is different for each month. It is an instantaneous field. Surface runoff kg m-2 The mass per unit area of water at the surface when saturation occurs. This variable is calculated for the natural land, including soil, vegetation and snow. It is an accumulated field. Surface runoff kg m-2 The mass per unit area of water at the surface when saturation occurs. This variable is calculated for the natural land, including soil, vegetation and snow. It is an accumulated field. Surface sensible heat flux J m-2 The surface sensible heat flux is the accumulated exchange of heat (no phase transition) with the surface (ground and water) through turbulent diffusion from the initial time of the forecast to the forecast time step. It is given as a mean for the grid area between the three surface types in the grid - inland water, natural land and urban. By model convention downward fluxes are positive. It is an accumulated field. Surface sensible heat flux J m-2 The surface sensible heat flux is the accumulated exchange of heat (no phase transition) with the surface (ground and water) through turbulent diffusion from the initial time of the forecast to the forecast time step. It is given as a mean for the grid area between the three surface types in the grid - inland water, natural land and urban. By model convention downward fluxes are positive. It is an accumulated field. Surface solar radiation downwards J m-2 The surface solar radiation downward is the accumulated total (direct and diffuse) solar short-wave radiation reaching the surface from the initial time of the forecast to the forecast time step. By model convention downward fluxes are positive. It is an accumulated field. Surface solar radiation downwards J m-2 The surface solar radiation downward is the accumulated total (direct and diffuse) solar short-wave radiation reaching the surface from the initial time of the forecast to the forecast time step. By model convention downward fluxes are positive. It is an accumulated field. Surface thermal radiation downwards J m-2 The surface thermal radiation downward is the amount of thermal (long-wave) radiation reaching the surface accumulated from the initial time of the forecast to the forecast time step. By model convention downward fluxes are positive. It is an accumulated field. Surface thermal radiation downwards J m-2 The surface thermal radiation downward is the amount of thermal (long-wave) radiation reaching the surface accumulated from the initial time of the forecast to the forecast time step. By model convention downward fluxes are positive. It is an accumulated field. Temperature of snow layer K Mean temperature of the 12 snow layers. It is an instantaneous field. Temperature of snow layer K Mean temperature of the 12 snow layers. It is an instantaneous field. Total precipitation kg m-2 Total daily precipitation is the amount of precipitation falling at the surface. It includes all kind of precipitation forms as convective precipitation, large scale precipitation, liquid and solid precipitation. The total precipitation is available only for the analyses at 06h00 UTC. It is an accumulated field from the previous day at 06 UTC to the present day at 06 UTC. Total precipitation kg m-2 Total daily precipitation is the amount of precipitation falling at the surface. It includes all kind of precipitation forms as convective precipitation, large scale precipitation, liquid and solid precipitation. The total precipitation is available only for the analyses at 06h00 UTC. It is an accumulated field from the previous day at 06 UTC to the present day at 06 UTC. Volumetric soil moisture m3 m-3 The volume concentration of liquid and ice water. The vertical discretisation (bottom depth of each layer in metres) is as follows - 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. The volumetric soil moisture is available for each soil layer. It is an instantaneous field. Volumetric soil moisture m3 m-3 The volume concentration of liquid and ice water. The vertical discretisation (bottom depth of each layer in metres) is as follows - 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. The volumetric soil moisture is available for each soil layer. It is an instantaneous field. Volumetric transpiration stress-onset (soil moisture) m3 m-3 The soil moisture is the water content of a soil after gravitational drainage. When the water content of the soil reaches this value, the water cannot drain any more by gravity. The vertical discretisation (bottom depth of each layer in metres) is as follows - 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. It is a static variable. Volumetric transpiration stress-onset (soil moisture) m3 m-3 The soil moisture is the water content of a soil after gravitational drainage. When the water content of the soil reaches this value, the water cannot drain any more by gravity. The vertical discretisation (bottom depth of each layer in metres) is as follows - 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. It is a static variable. Volumetric wilting point m3 m-3 Model soil water content at which the vegetation wilts and can no longer recover. When the soil moisture reaches the wilting point, the vegetation is not able to extract the soil water. The soil moisture content is too low to be absorbed by the vegetation. The vertical discretisation (bottom depth of each layer in metres) is as follows - 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. It is a static variable. Volumetric wilting point m3 m-3 Model soil water content at which the vegetation wilts and can no longer recover. When the soil moisture reaches the wilting point, the vegetation is not able to extract the soil water. The soil moisture content is too low to be absorbed by the vegetation. The vertical discretisation (bottom depth of each layer in metres) is as follows - 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8, and 12 m. It is a static variable. 222 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-pacific-gyre-area-chlorophyll-time-series-and-trend http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_npg_area_mean North Pacific Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing DEFINITION Oligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data inside the gyre. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021). CONTEXT Oligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth’s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. CMEMS KEY FINDINGS The trend in the North Pacific gyre area for the 1997 Sept – 2021 December period was positive, with a 1.75% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 2.17% reported for the 1997-2020 period. The trend is statistically significant (p<0.05). During the 1997 Sept – 2021 December period, the trend in chlorophyll concentration was negative (-0.26% year-1) in the North Pacific gyre relative to 2000-01-01 values. This trend is slightly less negative than the trend of -0.31% year-1 for the 1997-2020 period, though the sign of the trend remains unchanged and is statistically significant (p<0.05). It must be noted that the difference is small and within the uncertainty of the calculations, indicating that the trend is significant, however there may be no change associated with the timeseries extension. For 2016, The Ocean State Report (Sathyendranath et al. 2018) reported a large increase in gyre area in the Pacific Ocean (both North and South Pacific gyres), probably linked with the 2016 ENSO event which saw large decreases in chlorophyll in the Pacific Ocean. DOI (product):https://doi.org/10.48670/moi-00227 https://doi.org/10.48670/moi-00227 223 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/imperviousness-change-2015-2018-raster-20-m-europe-3 https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/change-maps/2015-2018/imperviousness-change-2015-2018 Imperviousness Change 2015-2018 (raster 20 m), Europe, 3-yearly, Aug. 2020 The High Resolution Layer Imperviousness Change (IMC) 2015-2018 is a 20m raster dataset showing change in imperviousness between 2015 and 2018 reference years, produced in the frame of the EU Copernicus programme. The high resolution imperviousness products capture the percentage and change of soil sealing. Built-up areas are characterized by the substitution of the original (semi-) natural land cover or water surface with an artificial, often impervious cover. These artificial surfaces are usually maintained over long periods of time. A series of high resolution imperviousness datasets (for the 2006, 2009, 2012, 2015 and 2018 reference years) with all artificially sealed areas was produced using automatic derivation based on calibrated Normalized Difference Vegetation Index (NDVI). This series of imperviousness layers constitutes the main status layers. They are per-pixel estimates of impermeable cover of soil (soil sealing) and are mapped as the degree of imperviousness (0-100%). Imperviousness change layers were produced as a difference between the reference years (2006-2009, 2009-2012, 2012-2015, 2015-2018 and additionally 2006-2012, to fully match the CORINE Land Cover production cycle) and are presented 1) as degree of imperviousness change (-100% -- +100%), in 20m and 100m pixel size, and 2) a classified (categorical) 20m change product. This dataset is provided as 20 meter rasters (fully conformant with EEA reference grid) in 100 x 100 km tiles grouped according to the EEA38 countries and the United Kingdom. 224 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-region-bio-geo-chemical-l3-daily-observation http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_ARC_BGC_HR_L3_NRT_009_201 Arctic Region, Bio-Geo-Chemical, L3, daily observation Short description: The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Remote Sensing Reflectances (RRS, expressed in sr-1), Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), spectral particulate backscattering (BBP, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in µg/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products of region ARC are delivered in polar Lambertian Azimuthal Equal Area (LAEA) projection (EPSG:6931, EASE2). To limit file size the products are provided in tiles of 600x800 km². RRS and BBP are delivered at nominal central bands of 443, 492, 560, 665, 704, 740, 783, 865 nm. The primary variable from which it is virtually possible to derive all the geophysical and transparency products is the spectral RRS. This, together with the spectral BBP, constitute the category of the 'optics' products. The spectral BBP product is generated from the RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). The NRT products are generally provided withing 24 hours after end of the day.The RRS product is accompanied by a relative uncertainty estimate (unitless) derived by direct comparison of the products to corresponding fiducial reference measurements provided through the AERONET-OC network. The current day data temporal consistency is evaluated as Quality Index (QI) for TUR, SPM and CHL: QI=(CurrentDataPixel-ClimatologyDataPixel)/STDDataPixel where QI is the difference between current data and the relevant climatological field as a signed multiple of climatological standard deviations (STDDataPixel). Processing information: The HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of: * Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone. * Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression. * Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area. * invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. * Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. * Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month. * Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day. Description of observation methods/instruments: Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton. Quality / Accuracy / Calibration information: A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201to212. Suitability, Expected type of users / uses: This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. Dataset names: *cmems_obs_oc_arc_bgc_geophy_nrt_l3-hr_P1D-m *cmems_obs_oc_arc_bgc_transp_nrt_l3-hr_P1D-m *cmems_obs_oc_arc_bgc_optics_nrt_l3-hr_P1D-m Files format: *netCDF-4, CF-1.7 *INSPIRE compliant. DOI (product) :https://doi.org/10.48670/moi-00061 https://doi.org/10.48670/moi-00061 225 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-cloud-properties https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-cloud-properties satellite-cloud-properties This dataset provides the Essential Climate Variable (ECV) Cloud Properties. Cloud properties describe the state of the Earth´s upper-air atmosphere. Clouds have an effect on weather and climate through their contribution to the Earth’s water cycle and impact on the Earth’s energy budget. They influence the motion of the atmosphere on many scales and modify the atmospheric composition. By accumulating and carrying the evaporated and transpired water in the atmosphere, clouds redistribute water over the globe, which often involves precipitation. Further, the variable cloud properties determines the feedback mechanism that clouds have on the hydrological cycle, directly in terms of precipitation, but also indirectly on the Earth´s energy budget by interacting with radiation fluxes. Usually, clouds reflect more solar radiation back to space than the underlying surface and absorb and re-emit infrared (IR) radiation, leading to less IR radiation leaving the system than without clouds. According to the 5th Intergovernmental Panel on Climate Change (IPCC) assessment report, clouds (together with aerosols) contribute the largest uncertainty to the estimates of the Earth´s energy budget, as well as to the potential feedback mechanisms and responses to climate change. The ECV Cloud Properties contains four main variables, which can be separated into averaged cloudiness, cloud height products and cloud physical properties for ice and liquid water phase. These variables were produced by two “product families”, based on the data from different sensors and algorithms that cover the same four variables. The "CCI product family" was originated by different projects within different organisations at different times. The third organisation, alongside CM SAF and CCI, the Copernicus Climate Change Service (C3S) is only associated with the CCI product family in this dataset to which it provides a continuation of their production chain. This means that the datasets contains three organisation but just two product families. The “CLARA (CM SAF cLoud, Albedo and surface Radiation) product family” and “CCI (Climate Change Initiative) product family” provide two complementary Thematic Climate Data Records (TCDRs), differing in temporal and horizontal resolution. These TCDR timeseries are intended to have sufficient length, consistency, and continuity to detect climate variability and change. They are frequently updated with Interim Climate Data Records (ICDRs) or simply extensions, generated using the same software and algorithms to cover more recent periods. Further details on algorithms, data description, and extensive validation results are given in the Documentation section. 226 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-energy-explorer-europe https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-energy-explorer-europe app-energy-explorer-europe This application explores climate data over Europe and the effect of climate change on energy supply and demand. Data is provided for the European domain, in a multi-variable, multi-timescale view of the climate and energy systems. This is beneficial in anticipating important climate-driven changes in the energy sector for either long-term planning or medium-term operational activities. The application can also be used to investigate the role of temperature on electricity demand across Europe, as well as its affect on renewable energy generation. The data is available in two distinct streams: historical and projections. The historical stream is based on ERA5 reanalysis data as input, from 1979 to near real-time (up to the most recent month), whilst the climate projections stream is based on European regional climate model experiment Euro-CORDEX from 1979 to 2100. Changes in energy variables are based solely on changes in climate, and do not reflect changes due to population growth or other socio-economic factors. Climate and energy variables can be visualised in the interactive map. Selecting a region will add the variable of choice for the given region to a time series graph, which can be customised and downloaded. User-selectable parameters User-selectable parameters Period: historical data (based on reanalysis) or climate projections. Experiment: greenhouse gas emission scenario (projections data only). Variable: climate and energy indicators to display on the main map. Resolution: temporal resolution (annual, seasonal, monthly or daily). Statistic: choose whether to visualise actual values or anomalies (relative to the 1981-2010 reference period). OUTPUT VARIABLES Name Units Description 2m air temperature K The ambient air temperature near to the surface, typically at height of 2m. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Electricity demand MWh, MW (or multiple there of, e.g. GW and GWh) Electricity Demand is the consumption of electricity expressed in energy units (MWh or GWh) or as mean power (MW or GW). The data is provided at the country level (NUTS level 0). Hydro power generation reservoirs Dimensionless, MWh or MW Hydro power generation from reservoirs (HRE) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided at the country level (NUTS level 0) for countries where HRE production exists. Hydro power generation rivers Dimensionless, MWh or MW Hydro power generation from run-of-river (HRO) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided only at the country level (NUTS level 0) for countries where HRO production exists. Pressure at sea level hPa Expected value of the air-pressure at the virtual vertical level defined by the average level of the sea. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Solar photovoltaic power generation Dimensionless, MWh or MW Solar photovoltaic power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Surface downwelling shortwave radiation W m-2 The amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Values are derived from ERA5 surface downwelling shortwave radiation and bias corrected using Climate Research Unit (CRU) cloud cover and effects of inter-annual changes in atmospheric aerosol loading. Total precipitation m Depth of rain water accumulated on a flat, horizontal and impermeable surface per unit area during a given time period. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are accumulated measures. Wind power generation onshore Dimensionless, MWh or MW Onshore wind power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity at heights of 10 metres and 100 metres. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description 2m air temperature K The ambient air temperature near to the surface, typically at height of 2m. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. 2m air temperature K The ambient air temperature near to the surface, typically at height of 2m. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Electricity demand MWh, MW (or multiple there of, e.g. GW and GWh) Electricity Demand is the consumption of electricity expressed in energy units (MWh or GWh) or as mean power (MW or GW). The data is provided at the country level (NUTS level 0). Electricity demand MWh, MW (or multiple there of, e.g. GW and GWh) Electricity Demand is the consumption of electricity expressed in energy units (MWh or GWh) or as mean power (MW or GW). The data is provided at the country level (NUTS level 0). Hydro power generation reservoirs Dimensionless, MWh or MW Hydro power generation from reservoirs (HRE) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided at the country level (NUTS level 0) for countries where HRE production exists. Hydro power generation reservoirs Dimensionless, MWh or MW Hydro power generation from reservoirs (HRE) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided at the country level (NUTS level 0) for countries where HRE production exists. Hydro power generation rivers Dimensionless, MWh or MW Hydro power generation from run-of-river (HRO) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided only at the country level (NUTS level 0) for countries where HRO production exists. Hydro power generation rivers Dimensionless, MWh or MW Hydro power generation from run-of-river (HRO) expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). The data is provided only at the country level (NUTS level 0) for countries where HRO production exists. Pressure at sea level hPa Expected value of the air-pressure at the virtual vertical level defined by the average level of the sea. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Pressure at sea level hPa Expected value of the air-pressure at the virtual vertical level defined by the average level of the sea. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Solar photovoltaic power generation Dimensionless, MWh or MW Solar photovoltaic power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Solar photovoltaic power generation Dimensionless, MWh or MW Solar photovoltaic power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW). Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Surface downwelling shortwave radiation W m-2 The amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Values are derived from ERA5 surface downwelling shortwave radiation and bias corrected using Climate Research Unit (CRU) cloud cover and effects of inter-annual changes in atmospheric aerosol loading. Surface downwelling shortwave radiation W m-2 The amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Values are derived from ERA5 surface downwelling shortwave radiation and bias corrected using Climate Research Unit (CRU) cloud cover and effects of inter-annual changes in atmospheric aerosol loading. Total precipitation m Depth of rain water accumulated on a flat, horizontal and impermeable surface per unit area during a given time period. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are accumulated measures. Total precipitation m Depth of rain water accumulated on a flat, horizontal and impermeable surface per unit area during a given time period. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are accumulated measures. Wind power generation onshore Dimensionless, MWh or MW Onshore wind power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Wind power generation onshore Dimensionless, MWh or MW Onshore wind power generation expressed as: i) capacity factor (ratio of actual generation to installed capacity), ii) energy (MWh or GWh) and iii) mean power (MW or GW) for onshore areas. Data are averaged over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity at heights of 10 metres and 100 metres. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity at heights of 10 metres and 100 metres. The data represents the mean area average over three area aggregations: grid point, country level (NUTS level 0), sub-country level (NUTS level 2). The data values are instantaneous measures. 227 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/water-bodies-2014-2020-raster-300-m-global-10-daily https://land.copernicus.eu/global/products/wb Water Bodies 2014-2020 (raster 300 m), global, 10-daily - version 1 The Water Bodies or WB product detects the areas covered by inland water across the globe, providing the maximum and the minimum extent of the water surface as well as the seasonal dynamics. The area of water bodies is identified as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). 228 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/water-bodies-1998-2018-raster-1-km-global-10-daily https://land.copernicus.eu/global/products/wb Water Bodies 1998-2018 (raster 1 km), global, 10-daily - version 2 The Water Bodies or WB product detects the areas covered by inland water across the globe, providing the maximum and the minimum extent of the water surface as well as the seasonal dynamics. The area of water bodies is identified as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). 229 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/coastal-zones-land-coverland-use-2018-vector-europe-6 https://land.copernicus.eu/local/coastal-zones/coastal-zones-2018?tab=download Coastal Zones Land Cover/Land Use 2018 (vector), Europe, 6-yearly, Feb. 2021 The Coastal Zones (CZ) LC/LU product for 2018 is providing a detailed Land Cover / Land Use (LC/ LU) dataset for areas along the coastline of the EEA38 countries and the United Kingdom, with reference year 2018 for the classification. This product monitors landscape dynamics in European coastal territory to an inland depth of 10 km with a total area of approximately 730,000 km², with all the relevant areas (estuaries, coastal lowlands, nature reserves). The production of the coastal zone layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme, as part of the Copernicus Land Monitoring Service (CLMS) Local Component. The Coastal Zones product covers a buffer zone of coastline derived from EU-Hydro v1.1. Land Cover/Land Use (LC/LU) layer is extracted from Very High Resolution (VHR) satellite data and other available data. The class definitions follow the pre-defined nomenclature on the basis of Mapping and Assessment of Ecosystems and their Services (MAES) typology of ecosystems (Level 1 to Level 4) and CORINE Land Cover adapted to the specific characteristics of coastal zones. The classification provides 71 distinct thematic classes with a Minimum Mapping Unit (MMU) of 0.5 ha and a Minimum Mapping Width (MMW) of 10 m. The product is available for the 2012 and 2018 reference year including change mapping. This CZ dataset is distributed in vector format, in a single OGC GeoPackage SQLite file covering the area of interest. 230 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-surface-temperature-cumulative-trend-map http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTIC_OMI_TEMPSAL_sst_trend Baltic Sea Surface Temperature cumulative trend map from Observations Reprocessing DEFINITION The BALTIC_OMI_TEMPSAL_sst_trend product includes the cumulative/net trend in sea surface temperature anomalies for the Baltic Sea from 1993-2021. The cumulative trend is the rate of change (°C/year) scaled by the number of years (29 years). The SST Level 4 analysis products that provide the input to the trend calculations are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2021. The product has a spatial resolution of 0.02 degrees in latitude and longitude. The OMI time series runs from Jan 1, 1993 to December 31, 2021 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 from 1993 to 2021. See the Copernicus Marine Service Ocean State Reports for more information on the OMI product (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). The times series of monthly anomalies have been used to calculate the trend in SST using Sen’s method with confidence intervals from the Mann-Kendall test (section 3 in Von Schuckmann et al., 2018). CONTEXT SST is an essential climate variable that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change. The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (Høyer and She 2007; Høyer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, Høyer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2oC from 1982 to 2012 considering all months of the year and 3-5oC when only July- September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). CMEMS KEY FINDINGS SST trends were calculated for the Baltic Sea area and the whole region including the North Sea, over the period January 1993 to December 2021. The average trend for the Baltic Sea domain (east of 9°E longitude) is 0.049 °C/year, which represents an average warming of 1.42 °C for the 1993-2021 period considered here. When the North Sea domain is included, the trend decreases to 0.03°C/year corresponding to an average warming of 0.87°C for the 1993-2021 period. Trends are highest for the Baltic Sea region and North Atlantic, especially offshore from Norway, compared to other regions. DOI (product):https://doi.org/10.48670/moi-00206 https://doi.org/10.48670/moi-00206 231 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/insitu-glaciers-elevation-mass https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-glaciers-elevation-mass insitu-glaciers-elevation-mass This dataset provides in situ and remote sensing derived glacier changes from individual glaciers globally. The dataset represents the latest homogenized state-of-the-art glacier change data collected by scientists and the national correspondents of each country as provided to the World Glacier Monitoring Service (WGMS). The product is an extract of the WGMS Fluctuations of Glacier (FoG) database and consists of two data sets providing time series of glacier changes: glacier elevation change series from the geodetic method glacier mass-balance series from the glaciological method glacier elevation change series from the geodetic method glacier elevation change series from the geodetic method glacier mass-balance series from the glaciological method glacier mass-balance series from the glaciological method Both data layers are provided as ESRI shapefiles containing the location of each glacier label point in geographic coordinates (longitude and latitude in degrees) in the World Geodetic System 1984 (WGS84) datum and some general statistical information about each glacier. Both shapefiles come with one ancillary .csv file containing the individual glacier change series linked to the corresponding point shapefile through the WGMS_ID. The mass balance series consist of usually continuous annual measurements in mm water equivalents. The elevation change series consist of multi-annual changes (in mm) with sometimes overlapping survey periods. For combining mass balance and elevation change data in one regional plot the elevation changes need to be converted to annual change rates and to mm water equivalents using a density conversion factor of 850 kg m-3. This dataset has been brokered from the Fluctuations of Glaciers (FoG) database of the World Glacier Monitoring Service (WGMS). DATA DESCRIPTION Data type Point shape file and text attribute file linked through common identifier Projection Geographic Coordinate System: GCS_WGS_1984 (Global Coordinate System, World Geodetic System). Datum: D_WGS_1984 (World Geodetic System). Horizontal coverage Global Horizontal resolution Individual glaciers Vertical coverage Surface Vertical resolution Single level Temporal coverage 1850 to 2019 Temporal resolution Annual to decadal File format ESRI shape files (Shape files can be read by a number of software programs such as ArcGIS and QGIS) and comma-separated value (CSV) text files Versions 20170405, 20170405, 20180601, 20181103, 20191202, 20200824. These versions are refered in the documentation as v1.0, v2.0, v3.0, v4.0, v5.0 and v6.0, respectively. Update frequency Annualy DATA DESCRIPTION DATA DESCRIPTION Data type Point shape file and text attribute file linked through common identifier Data type Point shape file and text attribute file linked through common identifier Projection Geographic Coordinate System: GCS_WGS_1984 (Global Coordinate System, World Geodetic System). Datum: D_WGS_1984 (World Geodetic System). Projection Geographic Coordinate System: GCS_WGS_1984 (Global Coordinate System, World Geodetic System). Datum: D_WGS_1984 (World Geodetic System). Horizontal coverage Global Horizontal coverage Global Horizontal resolution Individual glaciers Horizontal resolution Individual glaciers Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage 1850 to 2019 Temporal coverage 1850 to 2019 Temporal resolution Annual to decadal Temporal resolution Annual to decadal File format ESRI shape files (Shape files can be read by a number of software programs such as ArcGIS and QGIS) and comma-separated value (CSV) text files File format ESRI shape files (Shape files can be read by a number of software programs such as ArcGIS and QGIS) and comma-separated value (CSV) text files Versions 20170405, 20170405, 20180601, 20181103, 20191202, 20200824. These versions are refered in the documentation as v1.0, v2.0, v3.0, v4.0, v5.0 and v6.0, respectively. Versions 20170405, 20170405, 20180601, 20181103, 20191202, 20200824. These versions are refered in the documentation as v1.0, v2.0, v3.0, v4.0, v5.0 and v6.0, respectively. 20170405, 20170405, 20180601, 20181103, 20191202, 20200824. These versions are refered in the documentation as v1.0, v2.0, v3.0, v4.0, v5.0 and v6.0, respectively. Update frequency Annualy Update frequency Annualy MAIN VARIABLES Name Units Description FID Dimensionless Glacier identity number GLAC_REG1 Dimensionless 19 first order regions as defined for the Randolph Glacier Inventory 6.0, technical document, Table 2. Further information: http://www.glims.org/RGI/00_rgi60_TechnicalNote.pdf GLAC_REG2 Dimensionless More than 90 second order regions as defined for the Randolph Glacier Inventory 6.0, technical document, Table 2. Further information: http://www.glims.org/RGI/00_rgi60_TechnicalNote.pdf Latitude Degrees Glacier centre point Longitude Degrees Glacier centre point Name Dimensionless Glacier name (if available) PU Dimensionless Political Unit, ISO 3166 country code Photo_Info Dimensionless Photo owner Photo_url Dimensionless Glacier photo Shape Shape ESRI feature type (glacier outline) WGMS ID Dimensionless World Glacier Monitoring Service identification number MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description FID Dimensionless Glacier identity number FID Dimensionless Glacier identity number GLAC_REG1 Dimensionless 19 first order regions as defined for the Randolph Glacier Inventory 6.0, technical document, Table 2. Further information: http://www.glims.org/RGI/00_rgi60_TechnicalNote.pdf GLAC_REG1 Dimensionless 19 first order regions as defined for the Randolph Glacier Inventory 6.0, technical document, Table 2. Further information: http://www.glims.org/RGI/00_rgi60_TechnicalNote.pdf http://www.glims.org/RGI/00_rgi60_TechnicalNote.pdf GLAC_REG2 Dimensionless More than 90 second order regions as defined for the Randolph Glacier Inventory 6.0, technical document, Table 2. Further information: http://www.glims.org/RGI/00_rgi60_TechnicalNote.pdf GLAC_REG2 Dimensionless More than 90 second order regions as defined for the Randolph Glacier Inventory 6.0, technical document, Table 2. Further information: http://www.glims.org/RGI/00_rgi60_TechnicalNote.pdf http://www.glims.org/RGI/00_rgi60_TechnicalNote.pdf Latitude Degrees Glacier centre point Latitude Degrees Glacier centre point Longitude Degrees Glacier centre point Longitude Degrees Glacier centre point Name Dimensionless Glacier name (if available) Name Dimensionless Glacier name (if available) PU Dimensionless Political Unit, ISO 3166 country code PU Dimensionless Political Unit, ISO 3166 country code Photo_Info Dimensionless Photo owner Photo_Info Dimensionless Photo owner Photo_url Dimensionless Glacier photo Photo_url Dimensionless Glacier photo Shape Shape ESRI feature type (glacier outline) Shape Shape ESRI feature type (glacier outline) WGMS ID Dimensionless World Glacier Monitoring Service identification number WGMS ID Dimensionless World Glacier Monitoring Service identification number RELATED VARIABLES Name Description ANN_BAL Annual mass balance of glacier divided by the area of the glacier. ANN_BAL_UNC Estimated random uncertainty of reported annual balance. AREA Glacier area (in horizontal projection) in the survey year. AREA_CHANGE Area change between reference and survey dates. AREA_SURVEY_YEAR Glacier area in the survey year. ELEV_CH Specific ice thickness change between reference and survey dates. ELEV_CH_UNC Estimated random uncertainty of reported thickness change. INVESTIGATOR Name(s) of the person(s) or agency doing the fieldwork and/or the name(s) of the person(s) or agency processing the data. REFERENCE Reference to publication related to above data and methods. Format: Author et al. (YYYY); Journal, V(I), X-XX p. REFERENCE_DATE Date of previous survey. Date for each survey in numeric format (YYYYMMDD). For unknown day or month: “99” in the corresponding position(s) and a note under “REMARKS” REMARKS Any important information or comments not included above may be given here. Comments about the uncertainty of the numerical data may be made, including quantative comments. Only significant decimals should be given. SPONS_AGENCY Full name, abbreviation and address of the agency that sponsored the survey and/or where the data are held. SURVEY_DATE Date of present survey. Date for each survey in numeric format (YYYYMMDD). For unknown day or month: “99” in the corresponding position(s) and a note under “REMARKS”. SURVEY_ID Numeric key identifying data records related to a specific glacier survey in the FoG database of the World Glacier Monitoring Service (WGMS). This key is assigned by the WGMS in order to distinguish results from different surveys (and sources) for the same glacier and survey period. WGMS_ID Five digit key identifying glaciers in the Fluctuations of Glaciers (FoG) database of the World Glacier Monitoring Service (WGMS). For new glacier entries, this key is assigned by the WGMS. RELATED VARIABLES RELATED VARIABLES Name Description Name Description ANN_BAL Annual mass balance of glacier divided by the area of the glacier. ANN_BAL Annual mass balance of glacier divided by the area of the glacier. ANN_BAL_UNC Estimated random uncertainty of reported annual balance. ANN_BAL_UNC Estimated random uncertainty of reported annual balance. AREA Glacier area (in horizontal projection) in the survey year. AREA Glacier area (in horizontal projection) in the survey year. AREA_CHANGE Area change between reference and survey dates. AREA_CHANGE Area change between reference and survey dates. AREA_SURVEY_YEAR Glacier area in the survey year. AREA_SURVEY_YEAR Glacier area in the survey year. ELEV_CH Specific ice thickness change between reference and survey dates. ELEV_CH Specific ice thickness change between reference and survey dates. ELEV_CH_UNC Estimated random uncertainty of reported thickness change. ELEV_CH_UNC Estimated random uncertainty of reported thickness change. INVESTIGATOR Name(s) of the person(s) or agency doing the fieldwork and/or the name(s) of the person(s) or agency processing the data. INVESTIGATOR Name(s) of the person(s) or agency doing the fieldwork and/or the name(s) of the person(s) or agency processing the data. REFERENCE Reference to publication related to above data and methods. Format: Author et al. (YYYY); Journal, V(I), X-XX p. REFERENCE Reference to publication related to above data and methods. Format: Author et al. (YYYY); Journal, V(I), X-XX p. REFERENCE_DATE Date of previous survey. Date for each survey in numeric format (YYYYMMDD). For unknown day or month: “99” in the corresponding position(s) and a note under “REMARKS” REFERENCE_DATE Date of previous survey. Date for each survey in numeric format (YYYYMMDD). For unknown day or month: “99” in the corresponding position(s) and a note under “REMARKS” REMARKS Any important information or comments not included above may be given here. Comments about the uncertainty of the numerical data may be made, including quantative comments. Only significant decimals should be given. REMARKS Any important information or comments not included above may be given here. Comments about the uncertainty of the numerical data may be made, including quantative comments. Only significant decimals should be given. SPONS_AGENCY Full name, abbreviation and address of the agency that sponsored the survey and/or where the data are held. SPONS_AGENCY Full name, abbreviation and address of the agency that sponsored the survey and/or where the data are held. SURVEY_DATE Date of present survey. Date for each survey in numeric format (YYYYMMDD). For unknown day or month: “99” in the corresponding position(s) and a note under “REMARKS”. SURVEY_DATE Date of present survey. Date for each survey in numeric format (YYYYMMDD). For unknown day or month: “99” in the corresponding position(s) and a note under “REMARKS”. SURVEY_ID Numeric key identifying data records related to a specific glacier survey in the FoG database of the World Glacier Monitoring Service (WGMS). This key is assigned by the WGMS in order to distinguish results from different surveys (and sources) for the same glacier and survey period. SURVEY_ID Numeric key identifying data records related to a specific glacier survey in the FoG database of the World Glacier Monitoring Service (WGMS). This key is assigned by the WGMS in order to distinguish results from different surveys (and sources) for the same glacier and survey period. WGMS_ID Five digit key identifying glaciers in the Fluctuations of Glaciers (FoG) database of the World Glacier Monitoring Service (WGMS). For new glacier entries, this key is assigned by the WGMS. WGMS_ID Five digit key identifying glaciers in the Fluctuations of Glaciers (FoG) database of the World Glacier Monitoring Service (WGMS). For new glacier entries, this key is assigned by the WGMS. 232 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/medium-resolution-vegetation-phenology-and-11 https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum_d1-0.pdf Medium Resolution Vegetation Phenology and Productivity: End-of-season value (raster 500m), Oct. 2022 The End-of-Season Value (EOSV), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European Medium Resolution Vegetation Phenology and Productivity (MR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The End-of-Season Value (EOSV) provides the value of the Plant Phenology Index (PPI) at the end of the vegetation growing season. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The End-of-Season Value (EOSV) is one of the 13 parameters. The full list is available in the Product User Manual: https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… The End-of-Season Value (EOSV) time series dataset is made available as raster files with 500x 500m resolution, in ETRS89-LAEA projection corresponding to the MCD43 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2000 onwards. It is updated in the first quarter of each year. The full on-line access to open and free data for this resource will be made available by the end of 2022. Until then the data will be made available 'on-demand' by filling in the form at: https://land.copernicus.eu/contact-form https://land.copernicus.eu/contact-form 233 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-bio-geo-chemical-l4-monthly-means-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_BLK_BGC_HR_L4_NRT_009_212 Black Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation Short description: The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in µg/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km². BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azcárate et al., 2021). These types of L4 products are generated and delivered one month after the respective period. Processing information: The HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of: * Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone. * Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression. * Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area. * invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. * invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. * Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. * Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month. * Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day. Description of observation methods/instruments: Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton. Quality / Accuracy / Calibration information: A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212. Suitability, Expected type of users / uses: This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. Dataset names: *cmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1M-v01 *cmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1M-v01 *cmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1M-v01 *cmems_obs_oc_blk_bgc_geophy_nrt_l4-hr_P1D-v01 *cmems_obs_oc_blk_bgc_transp_nrt_l4-hr_P1D-v01 *cmems_obs_oc_blk_bgc_optics_nrt_l4-hr_P1D-v01 Files format: *netCDF-4, CF-1.7 *INSPIRE compliant. DOI (product) :https://doi.org/10.48670/moi-00087 https://doi.org/10.48670/moi-00087 234 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/coastal-zones-land-coverland-use-2012-vector-europe-6 https://land.copernicus.eu/local/coastal-zones/coastal-zones-2012?tab=download Coastal Zones Land Cover/Land Use 2012 (vector), Europe, 6-yearly, Feb. 2021 The Coastal Zones (CZ) LC/LU product for 2012 is providing a detailed Land Cover / Land Use (LC/ LU) dataset for areas along the coastline of the EEA38 countries and the United Kingdom, with reference year 2012 for the classification. This product monitors landscape dynamics in European coastal territory to an inland depth of 10 km with a total area of approximately 730,000 km², with all the relevant areas (estuaries, coastal lowlands, nature reserves). The production of the coastal zone layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme, as part of the Copernicus Land Monitoring Service (CLMS) Local Component. The Coastal Zones product covers a buffer zone of coastline derived from EU-Hydro v1.1. Land Cover/Land Use (LC/LU) layer is extracted from Very High Resolution (VHR) satellite data and other available data. The class definitions follow the pre-defined nomenclature on the basis of Mapping and Assessment of Ecosystems and their Services (MAES) typology of ecosystems (Level 1 to Level 4) and CORINE Land Cover adapted to the specific characteristics of coastal zones. The classification provides 71 distinct thematic classes with a Minimum Mapping Unit (MMU) of 0.5 ha and a Minimum Mapping Width (MMW) of 10 m. The product is available for the 2012 and 2018 reference year including change mapping. This CZ dataset is distributed in vector format, in a single OGC GeoPackage SQLite file covering the area of interest. 235 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-region-bio-geo-chemical-l4-monthly-means-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_ARC_BGC_HR_L4_NRT_009_207 Arctic Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation Short description: The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in µg/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products of region ARC are delivered in polar Lambertian Azimuthal Equal Area (LAEA) projection (EPSG:6931, EASE2). To limit file size the products are provided in tiles of 600x800 km². BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azcárate et al., 2021). These types of L4 products are generated and delivered one month after the respective period. Processing information: The HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of: * Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone. * Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression. * Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area. * invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. * Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. * Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month. * Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day. Description of observation methods/instruments: Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton. Quality / Accuracy / Calibration information: A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212. Suitability, Expected type of users / uses: This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. Dataset names: *cmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1M-v01 *cmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1M-v01 *cmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1M-v01 *cmems_obs_oc_arc_bgc_geophy_nrt_l4-hr_P1D-v01 *cmems_obs_oc_arc_bgc_transp_nrt_l4-hr_P1D-v01 *cmems_obs_oc_arc_bgc_optics_nrt_l4-hr_P1D-v01 Files format: *netCDF-4, CF-1.7 *INSPIRE compliant. DOI (product) :https://doi.org/10.48670/moi-00062 https://doi.org/10.48670/moi-00062 236 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/water-and-wetness-2015-raster-100-m-europe-3-yearly-nov https://land.copernicus.eu/pan-european/high-resolution-layers/water-wetness/status-maps/2015/view Water and Wetness 2015 (raster 100 m), Europe, 3-yearly, Nov. 2020 The Copernicus High Resolution Water and Wetness (WAW) 2015 layer is a thematic product showing the occurrence of water and wet surfaces over the period from 2009 to 2015 for the EEA38 area and the United Kingdom . This metadata corresponds to the aggregation of the 20m classified product into a 100m raster. The production of the High Resolution Water and Wetness layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. Two WAW products are available: - The main Water and Wetness (WAW) product, with defined classes of (1) permanent water, (2) temporary water, (3) permanent wetness and (4) temporary wetness. - The additional expert product: Water and Wetness Probability Index (WWPI). The products show the occurrence of water and indicate the degree of wetness in a physical sense, assessed independently of the actual vegetation cover and are thus not limited to a specific land cover class and their relative frequencies. Data is provided as a mosaic of the full area, and as tiles with a side length of 1000 km x 1000 km. In 2020, due to methodological improvements, the temporary wet class has been reprocessed during the update for the 2018 reference year. 237 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-tourism-fire-danger-indicators-projections https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-tourism-fire-danger-indicators-projections app-tourism-fire-danger-indicators-projections This application presents fire danger indicators for Europe, based on the Canadian Fire Weather Index System (FWI) fire danger model. This model is used by the European Forest Fire Information System (EFFIS) standard and provides a numerical, non-dimensional rating of fire potential based exclusively on meteorological conditions favourable to the start, spread and sustainability of fires. This application presents the FWI rating for the current climate, calculated using ERA5 reanalysis, and future projections derived from general circulation model/regional climate model pairs. Multi-model statistics are provided and may be used to estimate the range of uncertainty in climate model simulations. The future projections are also bias-adjusted versus the FWI values calculated using the ERA5 reanalysis. The FWI values are available for the current climate (1981-2005), near future (2021-2040), mid-century (2041-2060), and end of century (2079-2098). Description of the graphical output Description of the graphical output The application displays Fire Weather Index (FWI) indicators on an interactive map averaged over NUTS level 3 (NUTS3) regions of Europe. The non-dimensional FWI ratings have been classified using the European Forest Fire Information System (EFFIS) classification of fire risk in this application to better portray the risk of fire to the user. Indicators can be visualised for a range of time horizons, climate change scenarios, and model statistics. By clicking on a NUTS3 region, the user accesses a side-panel bar chart which can be used to compare different fire danger indicators, model statistics, climate change scenarios or time horizons for the selected area. MAIN VARIABLES Name Units Description Source Daily Fire Weather Index Dimensionless The Fire Weather Index values at a daily temporal resolution for the selected year. The higher the index value, the more favorable the meteorological conditions to to sustain a fire once one is ignited. Fire danger from climate projections and Fire danger from CEMS Number of days with high fire danger Count Number of days per year with a Fire Weather Index greater than 30. This is the level chosen by the European Forest Fire Information System (EFFIS) to define high danger in Europe. Fire danger from climate projections Number of days with moderate fire danger Count Number of days per year with a Fire Weather Index greater than 15. This is the level chosen by the European Forest Fire Information System (EFFIS) to define moderate danger in Europe. Fire danger from climate projections Number of days with very high fire danger Count Number of days per year with a Fire Weather Index greater than 45. This is the level chosen by the European Forest Fire Information System (EFFIS) to define very high danger in Europe. Fire danger from climate projections Seasonal Fire Weather Index Dimensionless The mean Fire Weather Index value over the European fire season (June-September). This is calculated as the sum of the daily fire weather index over the European fire season divided by the total number of days within this date range. The higher the index value, the more favorable the meteorological conditions to to sustain a fire once one is ignited. Fire danger from climate projections MAIN VARIABLES MAIN VARIABLES Name Units Description Source Name Units Description Source Daily Fire Weather Index Dimensionless The Fire Weather Index values at a daily temporal resolution for the selected year. The higher the index value, the more favorable the meteorological conditions to to sustain a fire once one is ignited. Fire danger from climate projections and Fire danger from CEMS Daily Fire Weather Index Dimensionless The Fire Weather Index values at a daily temporal resolution for the selected year. The higher the index value, the more favorable the meteorological conditions to to sustain a fire once one is ignited. Fire danger from climate projections and Fire danger from CEMS Fire danger from climate projections Fire danger from CEMS Number of days with high fire danger Count Number of days per year with a Fire Weather Index greater than 30. This is the level chosen by the European Forest Fire Information System (EFFIS) to define high danger in Europe. Fire danger from climate projections Number of days with high fire danger Count Number of days per year with a Fire Weather Index greater than 30. This is the level chosen by the European Forest Fire Information System (EFFIS) to define high danger in Europe. Fire danger from climate projections Fire danger from climate projections Number of days with moderate fire danger Count Number of days per year with a Fire Weather Index greater than 15. This is the level chosen by the European Forest Fire Information System (EFFIS) to define moderate danger in Europe. Fire danger from climate projections Number of days with moderate fire danger Count Number of days per year with a Fire Weather Index greater than 15. This is the level chosen by the European Forest Fire Information System (EFFIS) to define moderate danger in Europe. Fire danger from climate projections Fire danger from climate projections Number of days with very high fire danger Count Number of days per year with a Fire Weather Index greater than 45. This is the level chosen by the European Forest Fire Information System (EFFIS) to define very high danger in Europe. Fire danger from climate projections Number of days with very high fire danger Count Number of days per year with a Fire Weather Index greater than 45. This is the level chosen by the European Forest Fire Information System (EFFIS) to define very high danger in Europe. Fire danger from climate projections Fire danger from climate projections Seasonal Fire Weather Index Dimensionless The mean Fire Weather Index value over the European fire season (June-September). This is calculated as the sum of the daily fire weather index over the European fire season divided by the total number of days within this date range. The higher the index value, the more favorable the meteorological conditions to to sustain a fire once one is ignited. Fire danger from climate projections Seasonal Fire Weather Index Dimensionless The mean Fire Weather Index value over the European fire season (June-September). This is calculated as the sum of the daily fire weather index over the European fire season divided by the total number of days within this date range. The higher the index value, the more favorable the meteorological conditions to to sustain a fire once one is ignited. Fire danger from climate projections Fire danger from climate projections 238 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-high-resolution-and-ultra-high-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_MED_SST_L4_NRT_OBSERVATIONS_010_004 Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis Short description: For the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides daily gap-free (L4) maps at high (HR 0.0625°) and ultra-high (UHR 0.01°) spatial resolution over the Mediterranean Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625°) and ultra-high (UHR 0.01°) spatial resolution, applying statistical techniques. These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers. DOI (product) :https://doi.org/10.48670/moi-00172 https://doi.org/10.48670/moi-00172 239 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-radiative-forcings https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-radiative-forcings cams-global-radiative-forcings This dataset provides geographical distributions of the radiative forcing (RF) by key atmospheric constituents. The radiative forcing estimates are based on the CAMS reanalysis and additional model simulations and are provided separately for... carbon dioxide methane tropospheric ozone stratospheric ozone interactions between anthropogenic aerosols and radiation interactions between anthropogenic aerosols and clouds carbon dioxide methane tropospheric ozone stratospheric ozone interactions between anthropogenic aerosols and radiation interactions between anthropogenic aerosols and clouds Radiative forcing measures the imbalance in the Earth’s energy budget caused by a perturbation of the climate system, such as changes in atmospheric composition caused by human activities. RF is a useful predictor of globally-averaged temperature change, especially when rapid adjustments of atmospheric temperature and moisture profiles are taken into account. RF has therefore become a quantitative metric to compare the potential climate response to different perturbations. Increases in greenhouse gas concentrations over the industrial era exerted a positive RF, causing a gain of energy in the climate system. In contrast, concurrent changes in atmospheric aerosol concentrations are thought to exert a negative RF leading to a loss of energy. Products are quantified both in “all-sky” conditions, meaning that the radiative effects of clouds are included in the radiative transfer calculations, and in “clear-sky” conditions, which are computed by excluding clouds in the radiative transfer calculations. The upgrade from version 1.5 to 2 consists of an extension of the period by 2017-2018, the addition of an "effective radiative forcing" product and new ways to calculate the pre-industrial reference state for aerosols and cloud condensation nuclei. More details are given in the documentation section. New versions may be released in future as scientific methods develop, and existing versions may be extended with later years if data for the period is available from the CAMS reanalysis. Newer versions supercede old versions so it is always recommended to use the latest one. CAMS also produces distributions of aerosol optical depths, distinguishing natural from anthropogenic aerosols, which are a separate dataset. See "Related Data". More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 3° x 3° (1° x 1° for v1.5 aerosol/cloud interaction forcings) Vertical coverage Three levels: surface, tropopause and top-of-atmosphere Vertical resolution Single level Temporal coverage 2003-2017 for version 1.5; 2003-2018 for version 2 Temporal resolution Monthly (daily for v1.5 aerosol/cloud interaction forcings) File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions 1.5, 2 Update frequency Infrequent and irregular DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 3° x 3° (1° x 1° for v1.5 aerosol/cloud interaction forcings) Horizontal resolution 3° x 3° (1° x 1° for v1.5 aerosol/cloud interaction forcings) Vertical coverage Three levels: surface, tropopause and top-of-atmosphere Vertical coverage Three levels: surface, tropopause and top-of-atmosphere Vertical resolution Single level Vertical resolution Single level Temporal coverage 2003-2017 for version 1.5; 2003-2018 for version 2 Temporal coverage 2003-2017 for version 1.5; 2003-2018 for version 2 Temporal resolution Monthly (daily for v1.5 aerosol/cloud interaction forcings) Temporal resolution Monthly (daily for v1.5 aerosol/cloud interaction forcings) File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions 1.5, 2 Versions 1.5, 2 Update frequency Infrequent and irregular Update frequency Infrequent and irregular MAIN VARIABLES Name Units Radiative forcing of anthropogenic aerosol-cloud interactions W m-2 Radiative forcing of anthropogenic aerosol-radiation interactions W m-2 Radiative forcing of carbon dioxide W m-2 Radiative forcing of methane W m-2 Radiative forcing of stratospheric ozone W m-2 Radiative forcing of tropospheric ozone W m-2 MAIN VARIABLES MAIN VARIABLES Name Units Name Units Radiative forcing of anthropogenic aerosol-cloud interactions W m-2 Radiative forcing of anthropogenic aerosol-cloud interactions W m-2 Radiative forcing of anthropogenic aerosol-radiation interactions W m-2 Radiative forcing of anthropogenic aerosol-radiation interactions W m-2 Radiative forcing of carbon dioxide W m-2 Radiative forcing of carbon dioxide W m-2 Radiative forcing of methane W m-2 Radiative forcing of methane W m-2 Radiative forcing of stratospheric ozone W m-2 Radiative forcing of stratospheric ozone W m-2 Radiative forcing of tropospheric ozone W m-2 Radiative forcing of tropospheric ozone W m-2 240 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/insitu-glaciers-extent https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-glaciers-extent insitu-glaciers-extent This dataset, commonly known as the Randolph Glacier Inventory (RGI), provides global glacier outlines compiled from maps, aerial photographs and satellite images. The data is provided as a "snapshot" (single time-slice) constructed from images that were mostly acquired in the period 2000-2010. The dataset represents the latest globally complete and homogenized state-of-the-art glacier outline collection. The regional datasets have been created by scientists from all over the world and were provided to the Global Land Ice Measurements from Space (GLIMS) glacier database at the National Snow and Ice Data Center (NSIDC). Among others, the dataset plays a key role in determining the contribution of glacier melt to global sea-level rise and regional hydrology, as well as modelling of past and future glacier evolution. The provided RGI is brokered from the GLIMS glacier database at NSIDC and consists of two datasets which provide: glacier outlines with attribute information in shape file format tabular data describing the area of each glacier in a specific elevation interval, i.e. its hypsometry glacier outlines with attribute information in shape file format tabular data describing the area of each glacier in a specific elevation interval, i.e. its hypsometry Both datasets are arranged in 19 first order regions that cover all glacierized regions in the world. Coordinates are in longitude and latitude with the World Geodetic System 1984 (WGS84) datum. The RGI glacier outlines provided here are widely used by the glacier community to spatially constrain calculations of the geodetic glacier mass balance, which are also available in the Climate Data Store when forwarded to the World Glacier Monitoring Service (WGMS). DATA DESCRIPTION Data type Polygons and data aggregated over shapes Horizontal coverage Global Horizontal resolution Vector outlines in most of the time 30 x 30 meters resolution Vertical coverage Surface Vertical resolution Single level Temporal coverage Around the year 2000 Temporal resolution Snapshot (single time slice) File format ESRI shape files (shape files can be read by a number of software programs such as ArcGIS and QGIS) and comma-separated value (CSV) text files Versions 5.0 and 6.0 Update frequency Variable DATA DESCRIPTION DATA DESCRIPTION Data type Polygons and data aggregated over shapes Data type Polygons and data aggregated over shapes Horizontal coverage Global Horizontal coverage Global Horizontal resolution Vector outlines in most of the time 30 x 30 meters resolution Horizontal resolution Vector outlines in most of the time 30 x 30 meters resolution Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage Around the year 2000 Temporal coverage Around the year 2000 Temporal resolution Snapshot (single time slice) Temporal resolution Snapshot (single time slice) File format ESRI shape files (shape files can be read by a number of software programs such as ArcGIS and QGIS) and comma-separated value (CSV) text files File format ESRI shape files (shape files can be read by a number of software programs such as ArcGIS and QGIS) and comma-separated value (CSV) text files Versions 5.0 and 6.0 Versions 5.0 and 6.0 Update frequency Variable Update frequency Variable 241 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-west-shelf-region-bio-geo-chemical-l4-monthly-means http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_NWS_BGC_HR_L4_NRT_009_209 North West Shelf Region, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation Short description: The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in µg/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km². BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azcárate et al., 2021). These types of L4 products are generated and delivered one month after the respective period. Processing information: The HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of: * Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone. * Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression. * Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area. * invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. * Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. * Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month. * Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day. Description of observation methods/instruments: Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton. Quality / Accuracy / Calibration information: A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212. Suitability, Expected type of users / uses: This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. Dataset names: *cmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1M-v01 *cmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1M-v01 *cmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1M-v01 *cmems_obs_oc_nws_bgc_geophy_nrt_l4-hr_P1D-v01 *cmems_obs_oc_nws_bgc_transp_nrt_l4-hr_P1D-v01 *cmems_obs_oc_nws_bgc_optics_nrt_l4-hr_P1D-v01 Files format: *netCDF-4, CF-1.7 *INSPIRE compliant. DOI (product) :https://doi.org/10.48670/moi-00119 https://doi.org/10.48670/moi-00119 242 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/iberic-sea-bio-geo-chemical-l4-monthly-means-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_IBI_BGC_HR_L4_NRT_009_210 Iberic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation Short description: The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in µg/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km². BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azcárate et al., 2021). These types of L4 products are generated and delivered one month after the respective period. Processing information: The HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of: * Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone. * Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression. * Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area. * invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. * Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. * Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month. * Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day. Description of observation methods/instruments: Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton. Quality / Accuracy / Calibration information: A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212. Suitability, Expected type of users / uses: This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. Dataset names: *cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1M-v01 *cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1M-v01 *cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1M-v01 *cmems_obs_oc_ibi_bgc_geophy_nrt_l4-hr_P1D-v01 *cmems_obs_oc_ibi_bgc_transp_nrt_l4-hr_P1D-v01 *cmems_obs_oc_ibi_bgc_optics_nrt_l4-hr_P1D-v01 Files format: *netCDF-4, CF-1.7 *INSPIRE compliant. DOI (product) :https://doi.org/10.48670/moi-00108 https://doi.org/10.48670/moi-00108 243 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/european-north-west-shelf-sea-surface-temperature-trend http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=NORTHWESTSHELF_OMI_TEMPSAL_sst_trend European North West Shelf Sea Surface Temperature trend map from Observations Reprocessing DEFINITION The northwestshelf_omi_tempsal_sst_trend product includes the Sea Surface Temperature (SST) trend for the European North West Shelf Seas over the period 1993-2021, i.e. the rate of change (°C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf), which provided the SSTs used to compute the SST trend over the European North West Shef Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05° grid resolution SST maps built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens’s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf CONTEXT Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). CMEMS KEY FINDINGS Over the period 1993-2021, the European North West Shef Seas mean Sea Surface Temperature (SST) increased at a rate of 0.001 ± 0.001 °C/Year. DOI (product):https://doi.org/10.48670/moi-00276 https://doi.org/10.48670/moi-00276 244 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-bio-geo-chemical-l4-monthly-means-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_MED_BGC_HR_L4_NRT_009_211 Mediterranean Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation Short description: The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in µg/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km². BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azcárate et al., 2021). These types of L4 products are generated and delivered one month after the respective period. Processing information: The HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of: * Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone. * Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression. * Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area. * invalid pixel identification takes into account corrupted (L1-) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. * Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. * Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month. * Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day. Description of observation methods/instruments: Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton. Quality / Accuracy / Calibration information: A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212. Suitability, Expected type of users / uses: This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. Dataset names: *cmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1M-v01+D19 *cmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1M-v01 *cmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1M-v01 *cmems_obs_oc_med_bgc_geophy_nrt_l4-hr_P1D-v01 *cmems_obs_oc_med_bgc_transp_nrt_l4-hr_P1D-v01 *cmems_obs_oc_med_bgc_optics_nrt_l4-hr_P1D-v01 Files format: *netCDF-4, CF-1.7 *INSPIRE compliant. DOI (product) :https://doi.org/10.48670/moi-00110 https://doi.org/10.48670/moi-00110 245 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-greenland-ice-sheet-velocity https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-greenland-ice-sheet-velocity satellite-greenland-ice-sheet-velocity This dataset contains annual ice velocity maps of the Greenland Ice Sheet derived from Sentinel-1 data. The data represent the current state-of-the-art in Europe for production of satellite-based ice velocity data records. It follows on from the ESA Greenland Ice Sheet Climate Change Initiative and is guided by the GCOS (Global Climate Observing System) targets for the Ice Sheets Essential Climate Variable. Mapping glacier flow velocity and its temporal changes provides key information for investigating the dynamic response of glaciers and ice sheets to changing boundary environmental conditions. Remote sensing techniques that utilise satellite data are the only feasible manner to derive accurate surface velocities of the remote Greenland glaciers on a regular basis. The surface velocity is derived by applying feature tracking techniques using Sentinel-1 synthetic aperture radar (SAR) data acquired in the Interferometric Wide (IW) swath mode. Ice velocity is provided at 250m and 500m grid spacing in North Polar Stereographic projection (depending on version). The horizontal velocity components are provided in true meters per day, towards easting and northing direction of the grid. The vertical displacement is derived from a digital elevation model. Provided is a NetCDF file with the velocity components: vx, vy, vz, along with maps showing the magnitude of the horizontal components, the valid pixel count and uncertainty (based on the standard deviation). The product combines all ice velocity maps, based on 6- and 12-day repeats, acquired over a full year in an annually averaged product running from October 1st to September 30th, mimicking a glaciological year. The dataset is extended on an annual basis. This dataset is produced on behalf of the Copernicus Climate Change Service (C3S). DATA DESCRIPTION Data type Gridded Projection Polar-stereographic, WGS84 Horizontal coverage Greenland ice sheet Horizontal resolution 250 m & 500 m Vertical coverage Ice sheet surface Vertical resolution Single surface layer Temporal coverage 2017-10-01 to 2021-09-31 Temporal resolution Annual File format NetCDF Conventions CF-v1.6 Versions v1.2 (2017-10-01 to 2019-09-31, 500 m) v1.3 (2017-10-01 to 2020-09-31. 250 m, fixed ice-ocean mask) v1.4 (2020-10-01 to 2021-09-31, 250 m, dynamic ice-ocean mask) Update frequency Annual updates DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Polar-stereographic, WGS84 Projection Polar-stereographic, WGS84 Horizontal coverage Greenland ice sheet Horizontal coverage Greenland ice sheet Horizontal resolution 250 m & 500 m Horizontal resolution 250 m & 500 m Vertical coverage Ice sheet surface Vertical coverage Ice sheet surface Vertical resolution Single surface layer Vertical resolution Single surface layer Temporal coverage 2017-10-01 to 2021-09-31 Temporal coverage 2017-10-01 to 2021-09-31 Temporal resolution Annual Temporal resolution Annual File format NetCDF File format NetCDF Conventions CF-v1.6 Conventions CF-v1.6 Versions v1.2 (2017-10-01 to 2019-09-31, 500 m) v1.3 (2017-10-01 to 2020-09-31. 250 m, fixed ice-ocean mask) v1.4 (2020-10-01 to 2021-09-31, 250 m, dynamic ice-ocean mask) Versions v1.2 (2017-10-01 to 2019-09-31, 500 m) v1.3 (2017-10-01 to 2020-09-31. 250 m, fixed ice-ocean mask) v1.4 (2020-10-01 to 2021-09-31, 250 m, dynamic ice-ocean mask) v1.2 (2017-10-01 to 2019-09-31, 500 m) v1.3 (2017-10-01 to 2020-09-31. 250 m, fixed ice-ocean mask) v1.4 (2020-10-01 to 2021-09-31, 250 m, dynamic ice-ocean mask) Update frequency Annual updates Update frequency Annual updates MAIN VARIABLES Name Units Description Land ice surface easting velocity m day-1 Velocity of the ice surface in the eastward direction. Land ice surface northing velocity m day-1 Velocity of the ice surface in the northward direction. Land ice surface velocity magnitude m day-1 The absolute horizontal velocity of the ice surface. Land ice surface vertical velocity m day-1 Velocity of the ice surface in the vertical direction. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Land ice surface easting velocity m day-1 Velocity of the ice surface in the eastward direction. Land ice surface easting velocity m day-1 Velocity of the ice surface in the eastward direction. Land ice surface northing velocity m day-1 Velocity of the ice surface in the northward direction. Land ice surface northing velocity m day-1 Velocity of the ice surface in the northward direction. Land ice surface velocity magnitude m day-1 The absolute horizontal velocity of the ice surface. Land ice surface velocity magnitude m day-1 The absolute horizontal velocity of the ice surface. Land ice surface vertical velocity m day-1 Velocity of the ice surface in the vertical direction. Land ice surface vertical velocity m day-1 Velocity of the ice surface in the vertical direction. RELATED VARIABLES Name Units Description Land ice surface measurement count number of pixels The number of valid pixels used in the velocity estimate. Land ice surface velocity standard deviation m day-1 The standard deviation of the land ice surface velocity magnitude. RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Land ice surface measurement count number of pixels The number of valid pixels used in the velocity estimate. Land ice surface measurement count number of pixels The number of valid pixels used in the velocity estimate. Land ice surface velocity standard deviation m day-1 The standard deviation of the land ice surface velocity magnitude. Land ice surface velocity standard deviation m day-1 The standard deviation of the land ice surface velocity magnitude. 246 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/directional-albedo-1999-2020-raster-1-km-global-10-daily http://land.copernicus.eu/global/products/sa Directional Albedo 1999-2020 (raster 1 km), global, 10-daily - version 1 The surface albedo quantifies the fraction of irradiance reflected by the surface of the Earth. It provides information on the radiative basis, thus on the temperature and water balance. The directional albedo or directional-hemispherical reflectance (also called black-sky albedo) is defined as the integration of the bi-directional reflectance over the viewing hemisphere. It assumes all energy is coming from a direct radiation from the sun and is computed for the local solar noon. 247 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/water-and-wetness-2015-raster-20-m-europe-3-yearly-nov https://land.copernicus.eu/pan-european/high-resolution-layers/water-wetness/status-maps/2015/view Water and Wetness 2015 (raster 20 m), Europe, 3-yearly, Nov. 2020 The Copernicus High Resolution Water and Wetness (WAW) 2015 layer is a thematic product showing the occurrence of water and wet surfaces over the period from 2009 to 2015 for the EEA38 area and the United Kingdom. This metadata corresponds to the 20m classified Water and Wetness product. The production of the High Resolution Water and Wetness layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. Two WAW products are available: - The main Water and Wetness (WAW) product, with defined classes of (1) permanent water, (2) temporary water, (3) permanent wetness and (4) temporary wetness. - The additional expert product: Water and Wetness Probability Index (WWPI). The products show the occurrence of water and indicate the degree of wetness in a physical sense, assessed independently of the actual vegetation cover and are thus not limited to a specific land cover class and their relative frequencies. Data is provided as a mosaic of the full area, and as tiles with a side length of 1000 km x 1000 km. In 2020, and due to methodological improvements, the temporary wet class has been reprocessed during the update for the 2018 reference year. 248 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-atlantic-gyre-area-chlorophyll-time-series-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_nag_area_mean North Atlantic Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing DEFINITION Oligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021). CONTEXT Oligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth’s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al. 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. CMEMS KEY FINDINGS The trend in the North Atlantic gyre area for the 1997 Sept – 2021 December period was positive, with a 0.14% year-1 increase in area relative to 2000-01-01 values. This trend has decreased compared with the 1997-2019 trend of 0.39%, and is no longer statistically significant (p>0.05). During the 1997 Sept – 2021 December period, the trend in chlorophyll concentration was negative (-0.21% year-1) inside the North Atlantic gyre relative to 2000-01-01 values. This is a slightly lower rate of change compared with the -0.24% trend for the 1997-2020 period but is still statistically significant (p<0.05). DOI (product):https://doi.org/10.48670/moi-00226 https://doi.org/10.48670/moi-00226 249 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-bio-geo-chemical-l4-monthly-means-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_BAL_BGC_HR_L4_NRT_009_208 Baltic Sea, Bio-Geo-Chemical, L4, monthly means and interpolated daily observation Short description: The High-Resolution Ocean Colour (HR-OC) Consortium (Brockmann Consult, Royal Belgian Institute of Natural Sciences, Flemish Institute for Technological Research) distributes Level 4 (L4) Turbidity (TUR, expressed in FNU), Solid Particulate Matter Concentration (SPM, expressed in mg/l), particulate backscattering at 443nm (BBP443, expressed in m-1) and chlorophyll-a concentration (CHL, expressed in µg/l) for the Sentinel 2/MSI sensor at 100m resolution for a 20km coastal zone. The products are delivered on a geographic lat-lon grid (EPSG:4326). To limit file size the products are provided in tiles of 600x800 km². BBP443, constitute the category of the 'optics' products. The BBP443 product is generated from the L3 RRS products using a quasi-analytical algorithm (Lee et al. 2002). The 'transparency' products include TUR and SPM). They are retrieved through the application of automated switching algorithms to the RRS spectra adapted to varying water conditions (Novoa et al. 2017). The GEOPHYSICAL product consists of the Chlorophyll-a concentration (CHL) retrieved via a multi-algorithm approach with optimized quality flagging (O'Reilly et al. 2019, Gons et al. 2005, Lavigne et al. 2021). Monthly products (P1M) are temporal aggregates of the daily L3 products. Daily products contain gaps in cloudy areas and where there is no overpass at the respective day. Aggregation collects the non-cloudy (and non-frozen) contributions to each pixel. Contributions are averaged per variable. While this does not guarantee data availability in all pixels in case of persistent clouds, it provides a more complete product compared to the sparsely filled daily products. The Monthly L4 products (P1M) are generally provided withing 4 days after the last acquisition date of the month. Daily gap filled L4 products (P1D) are generated using the DINEOF (Data Interpolating Empirical Orthogonal Functions) approach which reconstructs missing data in geophysical datasets by using a truncated Empirical Orthogonal Functions (EOF) basis in an iterative approach. DINEOF reconstructs missing data in a geophysical dataset by extracting the main patterns of temporal and spatial variability from the data. While originally designed for low resolution data products, recent research has resulted in the optimization of DINEOF to handle high resolution data provided by Sentinel-2 MSI, including cloud shadow detection (Alvera-Azcárate et al., 2021). These types of L4 products are generated and delivered one month after the respective period. Processing information: The HR-OC processing system is deployed on Creodias where Sentinel 2/MSI L1C data are available. The production control element is being hosted within the infrastructure of Brockmann Consult. The processing chain consists of: * Resampling to 60m and mosaic generation of the set of Sentinel-2 MSI L1C granules of a single overpass that cover a single UTM zone. * Application of a coastal mask with 20km water + 20km land. The result is a L1C mosaic tile with data just in the coastal area optimized for compression. * Level 2 processing with pixel identification (IdePix), atmospheric correction (C2RCC and ACOLITE or iCOR), in-water processing and merging (HR-OC L2W processor). The result is a 60m product with the same extent as the L1C mosaic, with variables for optics, transparency, and geophysics, and with data filled in the water part of the coastal area. * invalid pixel identification takes into account corrupted (L1) pixels, clouds, cloud shadow, glint, dry-fallen intertidal flats, coastal mixed-pixels, sea ice, melting ice, floating vegetation, non-water objects, and bottom reflection. * Daily L3 aggregation merges all Level 2 mosaics of a day intersecting with a target tile. All valid water pixels are included in the 20km coastal stripes; all other values are set to NaN. There may be more than a single overpass a day, in particular in the northern regions. The main contribution usually is the mosaic of the zone, but also adjacent mosaics may overlap. This step comprises resampling to the 100m target grid. * Monthly L4 aggregation combines all Level 3 products of a month and a single tile. The output is a set of 3 NetCDF datasets for optics, transparency, and geophysics respectively, for the tile and month. * Gap filling combines all daily products of a period and generates (partially) gap-filled daily products again. The output of gap filling are 3 datasets for optics (BBP443 only), transparency, and geophysics per day. Description of observation methods/instruments: Ocean colour technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so-called ocean colour which is affected by the presence of phytoplankton. Quality / Accuracy / Calibration information: A detailed description of the calibration and validation activities performed over this product can be found on the CMEMS web portal and in CMEMS-BGP_HR-QUID-009-201_to_212. Suitability, Expected type of users / uses: This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. Dataset names: *cmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1M-v01 *cmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1M-v01 *cmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1M-v01 *cmems_obs_oc_bal_bgc_geophy_nrt_l4-hr_P1D-v01 *cmems_obs_oc_bal_bgc_transp_nrt_l4-hr_P1D-v01 *cmems_obs_oc_bal_bgc_optics_nrt_l4-hr_P1D-v01 Files format: *netCDF-4, CF-1.7 *INSPIRE compliant. DOI (product) :https://doi.org/10.48670/moi-00080 https://doi.org/10.48670/moi-00080 250 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/riparian-zones-land-cover-and-land-use-change-2012-2018 https://land.copernicus.eu/local/riparian-zones/riparian-zones-change-2012-2018?tab=download Riparian Zones Land Cover and Land Use Change 2012-2018 (vector), Europe, 6-yearly, Dec. 2021 Riparian zones represent transitional areas occurring between land and freshwater ecosystems, characterised by distinctive hydrology, soil and biotic conditions and strongly influenced by the stream water. They provide a wide range of riparian functions (e.g. chemical filtration, flood control, bank stabilization, aquatic life and riparian wildlife support, etc.) and ecosystem services. The Riparian Zones products support the objectives of several European legal acts and policy initiatives, such as the EU Biodiversity Strategy to 2020, the Habitats and Birds Directives and the Water Framework Directive. This metadata refers to the Riparian Zones Land Cover/Land Use (LC/LU) change for 2012-2018. The LC/LU classification is tailored to the needs of biodiversity monitoring in a variable buffer zone of selected rivers (Strahler levels 2-9 derived from EU-Hydro) for the change layer 2012-2018. LC/LU is extracted from Very High Resolution (VHR) satellite data and other available data in a buffer zone of selected rivers for supporting biodiversity monitoring and mapping and assessment of ecosystems and their services. The class definitions follow the pre-defined nomenclature on the basis of Mapping and Assessment of Ecosystems and their Services (MAES) typology of ecosystems (Level 1 to Level 4) and CORINE Land Cover. The classification provides 55 distinct thematic classes with a Minimum Mapping Unit (MMU) of 0.5 ha and a Minimum Mapping Width (MMW) of 10 m. The production of the Riparian Zones products was coordinated by the European Environment Agency in the frame of the EU Copernicus programme. 251 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/riparian-zones-land-coverland-use-2012-vector-europe-6 https://land.copernicus.eu/local/riparian-zones/riparian-zones-2012 Riparian Zones Land Cover/Land Use 2012 (vector), Europe, 6-yearly, Dec. 2021 Riparian zones represent transitional areas occurring between land and freshwater ecosystems, characterised by distinctive hydrology, soil and biotic conditions and strongly influenced by the stream water. They provide a wide range of riparian functions (e.g. chemical filtration, flood control, bank stabilization, aquatic life and riparian wildlife support, etc.) and ecosystem services. The Riparian Zones products support the objectives of several European legal acts and policy initiatives, such as the EU Biodiversity Strategy to 2020, the Habitats and Birds Directives and the Water Framework Directive. This metadata refers to the Riparian Zones 2012 Land Cover/Land Use (LC/LU), which LC/LU classification is tailored to the needs of biodiversity monitoring in a variable buffer zone of selected rivers (Strahler levels 2-9 derived from EU-Hydro) for the reference year 2012. LC/LU is extracted from Very High Resolution (VHR) satellite data and other available data in a buffer zone of selected rivers for supporting biodiversity monitoring and mapping and assessment of ecosystems and their services. The class definitions follow the pre-defined nomenclature on the basis of Mapping and Assessment of Ecosystems and their Services (MAES) typology of ecosystems (Level 1 to Level 4) and CORINE Land Cover. The classification provides 55 distinct thematic classes with a Minimum Mapping Unit (MMU) of 0.5 ha and a Minimum Mapping Width (MMW) of 10 m. The nomenclature has been revised in 2020 with the aim to harmonize the products of the local components (mainly Riparian Zones and NATURA 2000 products) while maintaining user requirements for both products. A revised version of the Riparian Zones 2012 has been subsequently released in December 2021, together with the reference year 2018. The production of the Riparian Zones products was coordinated by the European Environment Agency in the frame of the EU Copernicus programme. 252 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-satellite-humidity-latitude-distribution https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-satellite-humidity-latitude-distribution app-satellite-humidity-latitude-distribution The application shows the latitudinal distribution of humidity in the lowest 12 kilometers of Earth's atmosphere, as well as the corresponding variability. The statistics were derived from a large number of humidity profiles retrieved from satellite-based Radio Occultation (RO) measurements. Atmospheric humidity plays an important role in the Earth's climate system, both for its strong greenhouse effect but also for its role in the global energy transport. It is central to the hydrological cycle and contributes to determining the fundamental conditions for the biosphere, including distribution of rainfall and droughts. The left-hand panel shows that the humidity peaks near the equator. The peak moves slightly back and forth in the north-south direction over the year, as it follows the seasonal movements of the Hadley circulation and the Intertropical Convergence Zone (ITCZ). The ITCZ is a near-equatorial zone of intense convection where water vapour is transported into upper air. The right-hand panel shows that the variability peaks slightly south and north of the humidity peak, while it has a local minimum right at the peak. This behaviour can be understood as a consequence of spatial humidity variations along constant latitudes. Near the mean ITCZ, there is a band of high and rather uniform humidity. Just south and north of this band, we find both humid and dry regions along the same latitude. As a consequence of these longitudinal variations, the standard deviations are increased in the 5-degree latitude bands, seen as the two peaks in the variability plots. Users can choose the year and month to display data for by changing in the dropdown menu, and the option of downloading the displayed data is given. The gridded monthly-mean tropospheric humidity dataset, originating from EUMETSAT's Radio Occultation Meteorology Satellite Application Facility (ROM SAF) facility, comprises a time series of continuous humidity observations from space, starting in 2006 and regularly extended up to present. As measurements encompass the entire globe, from the surface up to an altitude of 12 kilometers above mean sea level (MSL), and have high vertical resolution revealing fine scale details of the variations with height, the dataset is well suited for analysis of the latitudinal and height distributions of humidity. User-selectable parameters User-selectable parameters Year: 2007-2020 Month: January-December Year: 2007-2020 Year: 2007-2020 Month: January-December Month: January-December INPUT VARIABLES Name Units Description Source Specific humidity g kg-1 The ratio of the mass of water vapour in air to the total mass of the mixture of air and water vapour. Values represent the monthly mean for 5-degree latitude bands and altitudes below 12 km for all longitudes. Satellite humidity profiles INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Specific humidity g kg-1 The ratio of the mass of water vapour in air to the total mass of the mixture of air and water vapour. Values represent the monthly mean for 5-degree latitude bands and altitudes below 12 km for all longitudes. Satellite humidity profiles Specific humidity g kg-1 The ratio of the mass of water vapour in air to the total mass of the mixture of air and water vapour. Values represent the monthly mean for 5-degree latitude bands and altitudes below 12 km for all longitudes. Satellite humidity profiles Satellite humidity profiles 253 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-surface-temperature-time-series-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies Mediterranean Sea Surface Temperature time series and trend from Observations Reprocessing DEFINITION The blksea_omi_tempsal_sst_trend product includes the cumulative/net Sea Surface Temperature (SST) trend for the Black Sea over the period 1993-2021, i.e. the rate of change (°C/year) multiplied by the number years in the timeseries (29). This OMI is derived from the CMEMS Reprocessed Black Sea L4 SST satellite product (SST_BS_SST_L4_REP_OBSERVATIONS_010_022, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf), which provided the SSTs used to compute the SST trend over the Black Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05° grid resolution SST maps over the Black Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens’s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018). http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf CONTEXT Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). In the last decades, since the availability of satellite data (beginning of 1980s), the Black Sea has experienced a warming trend in SST (see e.g. Buongiorno Nardelli et al., 2010; Mulet et al., 2018). CMEMS KEY FINDINGS The spatial pattern of the Black Sea SST trend reveals a general warming tendency, ranging from 0.053 °C/year to 0.080 °C/year. The spatial pattern of SST trend is rather homogeneous over the whole basin. Highest values characterize the eastern basin, where the trend reaches the extreme value, while lower values are found close to the western coasts, in correspondence of main rivers inflow. The Black Sea SST trend continues to show the highest intensity among all the other European Seas. DOI (product):https://doi.org/10.48670/moi-00268 https://doi.org/10.48670/moi-00268 254 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-surface-temperature-cumulative-trend-map http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BLKSEA_OMI_TEMPSAL_sst_trend Black Sea Surface Temperature cumulative trend map from Observations Reprocessing DEFINITION The blksea_omi_tempsal_sst_trend product includes the cumulative/net Sea Surface Temperature (SST) trend for the Black Sea over the period 1993-2021, i.e. the rate of change (°C/year) multiplied by the number years in the timeseries (29). This OMI is derived from the CMEMS Reprocessed Black Sea L4 SST satellite product (SST_BS_SST_L4_REP_OBSERVATIONS_010_022, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf), which provided the SSTs used to compute the SST trend over the Black Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05° grid resolution SST maps over the Black Sea built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens’s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018). http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf CONTEXT Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). In the last decades, since the availability of satellite data (beginning of 1980s), the Black Sea has experienced a warming trend in SST (see e.g. Buongiorno Nardelli et al., 2010; Mulet et al., 2018). CMEMS KEY FINDINGS The spatial pattern of the Black Sea SST trend reveals a general warming tendency, ranging from 0.053 °C/year to 0.080 °C/year. The spatial pattern of SST trend is rather homogeneous over the whole basin. Highest values characterize the eastern basin, where the trend reaches the extreme value, while lower values are found close to the western coasts, in correspondence of main rivers inflow. The Black Sea SST trend continues to show the highest intensity among all the other European Seas. DOI (product):https://doi.org/10.48670/moi-00218 https://doi.org/10.48670/moi-00218 255 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-hydrology-meteorology-derived-projections https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-hydrology-meteorology-derived-projections sis-hydrology-meteorology-derived-projections This dataset provides precipitation and near surface air temperature for Europe as Essential Climate Variables (ECVs) and as a set of Climate Impact Indicators (CIIs) based on the ECVs. ECV datasets provide the empirical evidence needed to understand the current climate and predict future changes. CIIs contain condensed climate information which facilitate relatively quick and efficient subsequent analysis. Therefore, CIIs make climate information accessible to application focussed users within a sector. The ECVs and CIIs provided here were derived within the water management sectoral information service to address questions specific to the water sector. However, the products are provided in a generic form and are relevant for a range of sectors, for example agriculture and energy. The data represent the current state-of-the-art in Europe for regional climate modelling and indicator production. Data from eight model simulations included in the Coordinated Regional Climate Downscaling Experiment (CORDEX) were used to calculate a total of two ECVs and five CIIs at a spatial resolution of 0.11° x 0.11° and 5km x 5km. The ECV data meet the technical specification set by the Global Climate Observing System (GCOS), as such they are provided on a daily time step. They are bias adjusted using the EFAS gridded observations as a reference dataset. Note these are model output data, not observation data as is the general case for ECVs. The CIIs are provided as mean values over a 30-year time period. For the reference period (1971-2000) data is provided as absolute values, for the future periods the data is provided as absolute values and as the relative or absolute change from the reference period. The future periods cover 3 fixed time periods (2011-2040, 2041-2070 and 2071-2100) and 3 "degree scenario" periods defined by when global warming exceeds a given threshold (1.5 °C, 2.0 °C or 3.0 °C). The global warming is calculated from the global climate model (GCM) used, therefore the actual time period of the degree scenarios will be different for each GCM. This dataset is produced and quality assured by the Swedish Meteorological and Hydrological Institute on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Projection Lambert azimuthal equal area and rotated grid Horizontal coverage Europe Horizontal resolution 5km x 5km and 0.11° x 0.11° Vertical coverage Single level Vertical resolution Surface Temporal coverage from 1971 to 2100 Temporal resolution Annual, monthly and daily File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v.1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert azimuthal equal area and rotated grid Projection Lambert azimuthal equal area and rotated grid Horizontal coverage Europe Horizontal coverage Europe Horizontal resolution 5km x 5km and 0.11° x 0.11° Horizontal resolution 5km x 5km and 0.11° x 0.11° Vertical coverage Single level Vertical coverage Single level Vertical resolution Surface Vertical resolution Surface Temporal coverage from 1971 to 2100 Temporal coverage from 1971 to 2100 Temporal resolution Annual, monthly and daily Temporal resolution Annual, monthly and daily File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v.1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v.1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Versions 1.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description 2m air temperature K (ECV) oC (CII) The ambient air temperature close to the surface. The essential climate variable (ECV) data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset The climate impact indicator (CII) of 2m air temperature is defined as the monthly/annual mean of the daily mean 2m air temperature, averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Highest 5-day precipitation amount mm 5day-1 Highest five-day precipitation amount is defined as the maximum of 5-day precipitation totals. The value is given as a maximum over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Longest dry spells days Longest dry spells is defined as the maximum number of consecutive dry days (dry day: daily precipitation < 1mm) over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Number of dry spells Number of spells Number of dry spells is defined as the number of dry periods (dry day: daily precipitation < 1mm) of more than 5 days for a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Precipitation kg m-2 s-1 (ECV) mm day-1 (CII) Precipitation is defined as the deposition of water to the Earth"s surface in the form of rain, snow, ice or hail. The essential climate variable (ECV) data is given as the mass of water per unit area and time. The data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset The climate impact indicator (CII) of precipitation is defined as the monthly/annual mean of the liquid water equivalent daily precipitation, averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 2m air temperature K (ECV) oC (CII) The ambient air temperature close to the surface. The essential climate variable (ECV) data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset The climate impact indicator (CII) of 2m air temperature is defined as the monthly/annual mean of the daily mean 2m air temperature, averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). 2m air temperature K (ECV) oC (CII) K (ECV) oC (CII) The ambient air temperature close to the surface. The essential climate variable (ECV) data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset The climate impact indicator (CII) of 2m air temperature is defined as the monthly/annual mean of the daily mean 2m air temperature, averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). The ambient air temperature close to the surface. The essential climate variable (ECV) data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset The climate impact indicator (CII) of 2m air temperature is defined as the monthly/annual mean of the daily mean 2m air temperature, averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Highest 5-day precipitation amount mm 5day-1 Highest five-day precipitation amount is defined as the maximum of 5-day precipitation totals. The value is given as a maximum over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Highest 5-day precipitation amount mm 5day-1 Highest five-day precipitation amount is defined as the maximum of 5-day precipitation totals. The value is given as a maximum over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Longest dry spells days Longest dry spells is defined as the maximum number of consecutive dry days (dry day: daily precipitation < 1mm) over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Longest dry spells days Longest dry spells is defined as the maximum number of consecutive dry days (dry day: daily precipitation < 1mm) over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Number of dry spells Number of spells Number of dry spells is defined as the number of dry periods (dry day: daily precipitation < 1mm) of more than 5 days for a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Number of dry spells Number of spells Number of dry spells is defined as the number of dry periods (dry day: daily precipitation < 1mm) of more than 5 days for a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Precipitation kg m-2 s-1 (ECV) mm day-1 (CII) Precipitation is defined as the deposition of water to the Earth"s surface in the form of rain, snow, ice or hail. The essential climate variable (ECV) data is given as the mass of water per unit area and time. The data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset The climate impact indicator (CII) of precipitation is defined as the monthly/annual mean of the liquid water equivalent daily precipitation, averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Precipitation kg m-2 s-1 (ECV) mm day-1 (CII) kg m-2 s-1 (ECV) mm day-1 (CII) Precipitation is defined as the deposition of water to the Earth"s surface in the form of rain, snow, ice or hail. The essential climate variable (ECV) data is given as the mass of water per unit area and time. The data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset The climate impact indicator (CII) of precipitation is defined as the monthly/annual mean of the liquid water equivalent daily precipitation, averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Precipitation is defined as the deposition of water to the Earth"s surface in the form of rain, snow, ice or hail. The essential climate variable (ECV) data is given as the mass of water per unit area and time. The data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset The climate impact indicator (CII) of precipitation is defined as the monthly/annual mean of the liquid water equivalent daily precipitation, averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). 256 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-physics-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTICSEA_MULTIYEAR_PHY_003_011 Baltic Sea Physics Reanalysis Short description: This Baltic Sea Physical Reanalysis product provides a reanalysis for the physical conditions for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus 1-1.5 year compared to real time. The product is produced by using the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include sea level, ice concentration, ice thickness, salinity, temperature, horizonal velocities and the mixed layer depths. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers). DOI (product) :https://doi.org/10.48670/moi-00013 https://doi.org/10.48670/moi-00013 257 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-pacific-gyre-oscillation-observations-reprocessing http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_CLIMVAR_NPGO_sla_eof_mode_projection North Pacific Gyre Oscillation from Observations Reprocessing DEFINITION The North Pacific Gyre Oscillation (NPGO) is a climate pattern introduced by Di Lorenzo et al. (2008) and further reported by Tranchant et al. (2019) in the CMEMS Ocean State Report #3. The NPGO is defined as the second dominant mode of variability of Sea Surface Height (SSH) anomaly and SST anomaly in the Northeast Pacific (25°– 62°N, 180°– 250°E). The North Pacific Gyre Oscillation index has been defined as the second Principal Component of model SSH anomaly calculated over the period 1950-2004. From the principal component analysis, the EOF’s amplitude pattern has been inferred from the principal component analysis. This regression map of the model North Pacific Gyre Oscillation index is used/projected with satellite altimeter delayed-time sea level anomalies to calculate and update the NPGO index The NPGO index disseminated on CMEMS was specifically updated from 2004 onward using up-to-date altimeter products (DT2021 version; SEALEVEL_GLO_PHY_L4_MY _008_047 CMEMS product and the near-real time SEALEVEL_GLO_PHY_L4_NRT_OBSERVATIONS_008_046 CMEMS product). Users that previously used the index disseminated on www.o3d.org/npgo/ web page will find slight differences induced by this update. The change in the reprocessed version (previously DT-2018) and the extension of the mean value of the SSH anomaly (now 27 years, previously 20 years) induce some slight changes not impacting the general variability of the NPGO. www.o3d.org/npgo/ CONTEXT NPGO mode emerges as the leading mode of decadal variability for surface salinity and upper ocean nutrients (Di Lorenzo et al., 2009). The North Pacific Gyre Oscillation (NPGO) term is used because its fluctuations reflect changes in the intensity of the central and eastern branches of the North Pacific gyres circulations (Chhak et al., 2009). This index measures change in the North Pacific gyres circulation and explains key physical-biological ocean variables including temperature, salinity, sea level, nutrients, chlorophyll-a. A positive North Pacific Gyre Oscillation phase is a dipole pattern with negative SSH anomaly north of 40°N and the opposite south of 40°N. Di Lorenzo et al. (2008) suggested that the North Pacific Gyre Oscillation is the oceanic expression of the atmospheric variability of the North Pacific Oscillation (Walker and Bliss, 1932), which has an expression in both the 2nd EOFs of SSH and Sea Surface Temperature (SST) anomalies (Ceballos et al., 2009). This finding is further supported by the recent work of Yi et al. (2018) showing consistent pattern features between the atmospheric North Pacific Oscillation and the oceanic North Pacific Gyre Oscillation in the Coupled Model Intercomparison Project Phase 5 (CMIP5) database. CMEMS KEY FINDINGS The NPGO index is presently in a negative phase, associated with a positive SSH anomaly north of 40°N and negative south of 40°N. This reflects a reduced amplitude of the central and eastern branches of the North Pacific gyre, corresponding to a reduced coastal upwelling and thus a lower sea surface salinity and concentration of nutrients. DOI (product):https://doi.org/10.48670/moi-00221 https://doi.org/10.48670/moi-00221 258 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-marine-spatial-planning-explorer https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-marine-spatial-planning-explorer app-marine-spatial-planning-explorer Marine Spatial Planning (MSP) is a public process of analysing and allocating marine space to human activities to achieve ecological, economic, and social objectives. Resulting plans regulate the sharing of marine space by different industries and nature conservation. Conservation and broader ecosystem-based management of ocean species and habitats contribute to the sustainability of dependent industries (e.g. wild-capture fisheries) that are key drivers of the ocean economy, all of which are affected by MSP. This Marine Spatial Planning application was co-designed with MSP practitioners to facilitate access, and the ability, to manipulate the ocean climate change modelling datasets available within the CDS in support of adaptive decision-making in Europe. We exemplify this by combining here access to climate modelling data; MSP-specific modelling analysis methods; and the distribution of protected areas and fishing grounds. A key feature of the application is the ability to visualise whether climate change is the driver of observed change in different ecosystem attributes of interest to marine species and habitats affected by MSP. To this end, we employ the meta-analysis statistic Hedges’ g, for which the calculation is such that if a climate trend is observed over time, the value is negative, and positive otherwise. Because this statistic considers both the mean and variation of each variable, the user can then visually determine which aspects of the marine environment are changing as a result of climate related processes in their region, and compare their magnitude. In addition to helping to identify where vulnerable and or resilient sites are located, the user is in this way also able to determine what part of a changing environment may be most concerning in their region (e.g., changes in temperature cf. changes in dissolved oxygen). The application uses as input a subset of ocean physical-biogeochemical projections available in the CDS catalogue entry ‘Marine biogeochemistry data for the Northwest European Shelf and Mediterranean Sea from 2006 up to 2100 derived from climate projections’, generated by the European Regional Seas Ecosystem model (ERSEM). ERSEM is driven by a global climate model generated for the Coupled Model Inter-comparison Project Phase 5 (CMIP5) at the open ocean boundaries, in combination with downscaled atmospheric data generated using the Swedish Meteorological and Hydrological Institute (SMHI) Rossby Centre Regional Atmospheric Model (RCA4). Note, the single combination of global – region climate models inevitably under-represent the uncertainty associated with the projections of the variables examined. Users can select climate variables for the ocean as well as various future 10-year periods, up to 2080, which are to be contrasted to their state in the present decade [2011-2020]. This decadal resolution allows for a direct link to the European Union’s Marine Spatial Planning Directive assessment cycle (10 years). User can select pre-defined areas, including a Greek example that was defined with MSP stakeholders defined in a C3S Use Case) define an area of interest (using bounding coordinates) or make additional selections in the interactive map produced by the CDS Marine Spatial Planning Application via double click on any region. This delivers the calculation for the selected variable for the ocean in that site given the selected time-period of interest, model product and time-periods selected. The location selected is indicated via pin in the map. INPUT VARIABLES Name Units Description Source Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Marine properties Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Marine properties Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Marine properties Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Marine properties INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Marine properties Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Marine properties Marine properties Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Marine properties Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Marine properties Marine properties Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Marine properties Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Marine properties Marine properties Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Marine properties Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Marine properties Marine properties OUTPUT VARIABLES Name Units Description Hedges' g Dimensionless The Hedges' g is a statistical technique to compare and/or jointly analyse modelling datasets representing climate variables for the ocean with different underlying data structures, toward the identification of the location of climate change hotspots. Please refer to the application user guide for more details. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Hedges' g Dimensionless The Hedges' g is a statistical technique to compare and/or jointly analyse modelling datasets representing climate variables for the ocean with different underlying data structures, toward the identification of the location of climate change hotspots. Please refer to the application user guide for more details. Hedges' g Dimensionless The Hedges' g is a statistical technique to compare and/or jointly analyse modelling datasets representing climate variables for the ocean with different underlying data structures, toward the identification of the location of climate change hotspots. Please refer to the application user guide for more details. 259 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-ocean-colour-plankton-reflectance-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_MED_BGC_L3_MY_009_143 Mediterranean Sea Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations Short description: For the Mediterranean Sea Ocean Satellite Observations, the Italian National Research Council (CNR – Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets: * ''plankton'' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017) * ''reflectance'' with the spectral Remote Sensing Reflectance (RRS) * ''transparency'' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for ""multi"" observations achieved via region-specific algorithm, Volpe et al., 2019) * ''optics'' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates) Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A for the ""multi"" products, and OLCI-S3A & S3B for the ""olci"" products Temporal resolution: daily Spatial resolution: 1 km for ""multi"" and 300 meters for ""olci"" To find this product in the catalogue, use the search keyword ""OCEANCOLOUR_MED_BGC_L3_MY"". DOI (product) :https://doi.org/10.48670/moi-00299 https://doi.org/10.48670/moi-00299 260 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-sea-level-black-sea https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-black-sea satellite-sea-level-black-sea Sea level anomaly is the height of water over the mean sea surface in a given time and region. Up-to-date altimeter standards are used to estimate the sea level anomalies with a mapping algorithm dedicated to the Black sea region. Anomalies are computed with respect to a twenty-year mean reference period (1993-2012). The steady number of reference satellite used in the production of this dataset contributes to the long-term stability of the sea level record. Improvements of the accuracy, sampling of meso-scale processes and of the high-latitude coverage were achieved by using a few additional satellite missions. This dataset includes uncertainties for each grid cell. More details about the sea level retrieval, additional filters, optimisation procedures, and the error estimation are given in the Documentation section. DATA DESCRIPTION Data type Gridded Horizontal coverage Black Sea Horizontal resolution 0.125° x 0.125°0 Temporal coverage 1 January 1993 to 3 June 2020 Temporal resolution Daily File format NetCDF Versions vDT2018 Update frequency No longer updated DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Black Sea Horizontal coverage Black Sea Horizontal resolution 0.125° x 0.125°0 Horizontal resolution 0.125° x 0.125°0 Temporal coverage 1 January 1993 to 3 June 2020 Temporal coverage 1 January 1993 to 3 June 2020 Temporal resolution Daily Temporal resolution Daily File format NetCDF File format NetCDF Versions vDT2018 Versions vDT2018 Update frequency No longer updated Update frequency No longer updated MAIN VARIABLES Name Units Description Geostrophic velocity anomalies meridian component m s-1 Northward component of the geostrophic current Geostrophic velocity anomalies zonal component m s-1 Eastward component of the geostrophic current Sea level anomaly m Sea surface height above mean sea surface computed with respect to a 20-year mean reference period (1993-2012) MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Geostrophic velocity anomalies meridian component m s-1 Northward component of the geostrophic current Geostrophic velocity anomalies meridian component m s-1 Northward component of the geostrophic current Geostrophic velocity anomalies zonal component m s-1 Eastward component of the geostrophic current Geostrophic velocity anomalies zonal component m s-1 Eastward component of the geostrophic current Sea level anomaly m Sea surface height above mean sea surface computed with respect to a 20-year mean reference period (1993-2012) Sea level anomaly m Sea surface height above mean sea surface computed with respect to a 20-year mean reference period (1993-2012) 261 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-biodiversity-era5-global https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-biodiversity-era5-global sis-biodiversity-era5-global This dataset provides a historical global reconstruction of bioclimatic indicators derived from ERA5 reanalysis on a latitude-longitude grid. These bioclimate indicators describe how the climate affects ecosystems, the services they deliver, and nature’s biodiversity. They are specifically relevant for applications within the biodiversity and ecosystem services community. The 78 indicators cover bioclimatic variables from both land and marine environments characterising surface energy, drought, soil moisture and the (near-)surface climate including wind as well as Essential Climate Variables (ECV) relevant to the biodiversity community and are based on hourly or monthly ERA5 reanalysis data. The bioclimatic indicators are widely used within the biodiversity community and have been chosen based on user requirements and consultation with stakeholders, in order to facilitate the direct use of climate information in screening analyses or in diverse downstream applications. The temporal resolution differs depending on the indicator varying between monthly, annual, and multi-annual averages. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 0.5° x 0.5° Vertical coverage Surface Vertical resolution Single level Temporal coverage 1979 to 2018 Temporal resolution Monthly, annual and 40-year average File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.5° x 0.5° Horizontal resolution 0.5° x 0.5° Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage 1979 to 2018 Temporal coverage 1979 to 2018 Temporal resolution Monthly, annual and 40-year average Temporal resolution Monthly, annual and 40-year average File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.7, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Versions 1.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Annual mean temperature (BIO01) K Annual mean of the monthly mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01. Annual precipitation (BIO12) m s-1 Annual mean of the monthly mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Aridity annual mean Dimensionless Monthly potential evaporation (m s-1) divided by the monthly mean precipitation (m s-1), averaged over the year. Aridity coldest quarter Dimensionless Monthly potential evaporation (m s-1) divided by the monthly mean precipitation (m s-1), averaged over the coldest quarter. Aridity driest quarter Dimensionless Monthly potential evaporation (m s-1) divided by the monthly mean precipitation (m s-1), averaged over the driest quarter. Aridity warmest quarter Dimensionless Monthly potential evaporation (m s-1) divided by the monthly mean precipitation (m s-1), averaged over the warmest quarter. Aridity wettest quarter Dimensionless Monthly potential evaporation (m s-1) divided by the monthly mean precipitation (m s-1), averaged over the wettest quarter. Cloud cover Dimensionless Fraction of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. The data are provided as both annual and monthly means. Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Evaporative fraction annual mean Dimensionless Monthly surface latent heat flux (W m-2) divided by the monthly total sensible and latent heat flux (W m-2), averaged over the year. Evaporative fraction coldest quarter Dimensionless Monthly surface latent heat flux (W m-2) divided by the monthly total sensible and latent heat flux (W m-2), averaged over the coldest quarter. Evaporative fraction driest quarter Dimensionless Monthly surface latent heat flux (W m-2) divided by the monthly total sensible and latent heat flux (W m-2), averaged over the driest quarter. Evaporative fraction warmest quarter Dimensionless Monthly surface latent heat flux (W m-2) divided by the monthly total sensible and latent heat flux (W m-2), averaged over the warmest quarter. Evaporative fraction wettest quarter Dimensionless Monthly surface latent heat flux (W m-2) divided by the monthly total sensible and latent heat flux (W m-2), averaged over the wettest quarter. Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). The data is aggregated as a monthly sum and annual sum. Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). The data is aggregated over the months. Growing degree days during growing season length K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC) during the period between the growing season start and end. Growing season end of season day of year The first day of a period of 5 consecutive days in the second half of the year with a mean daily temperature below 278 K (5oC). Growing season length day Number of days between the start and the end of the growing season. Growing season start of season day of year The first day of the year of a period of 5 consecutive days with a mean daily temperature above 278 K (5 oC). Isothermality (BIO03) % Monthly mean diurnal range divided by temperature annual range, multiplied by 100: (monthly mean diurnal range/temperature annual range) x100. This indicator corresponds to the official BIOCLIM variable BIO03. Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds (40-year averages). The Köppen-Geiger class indicator is provided based on 40-year mean climatologies of monthly temperature and precipitation, in line with the Köppen-Geiger standard. Maximum 2m temperature K Mean of the daily maximum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Maximum length of dry spells day Maximum number of consecutive dry spell days within a year. Maximum precipitation m s-1 Maximum of the daily mean precipitation. The data is aggregated over the year. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000. Maximum temperature of the warmest month (BIO05) K Maximum daily temperature averaged over the month with the highest monthly mean of daily maximum temperature. This indicator corresponds to the official BIOCLIM variable BIO05. Mean diurnal range (BIO02) K Mean of the monthly maximum temperature minus the monthly minimum temperature. The data is aggregated over the months. This indicator corresponds to the official BIOCLIM variable BIO02. Mean intensity of dry spells day Determine the consecutive dry days at each day in a year, then take the average of these daily values over the year. Mean length of dry spells day Mean length of dry spell days with a minimum of 5 days within a year. Mean precipitation m s-1 Average of the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Mean temperature of coldest quarter (BIO11) K The mean of monthly mean temperature during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO11. Mean temperature of driest quarter (BIO09) K The mean of monthly mean temperature during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO09. Mean temperature of warmest quarter (BIO10) K The mean of monthly mean temperature during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO10. Mean temperature of wettest quarter (BIO08) K The mean of monthly mean temperature during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO08. Meridional wind speed m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity near the surface. Minimum 2m temperature K Mean of the daily minimum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Minimum temperature of the coldest month (BIO06) K Minimum daily temperature averaged over the month with the lowest monthly mean of daily minimum temperature. This indicator corresponds to the official BIOCLIM variable BIO06. Number of dry spells Dimensionless Number of dry spells with a minimum of 5 days that occur in a year. Potential evaporation annual mean m s-1 Annual averaged amount of water that would evaporate and transpire if there is unlimited water supply. Potential evaporation coldest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the coldest quarter. Potential evaporation driest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the driest quarter. Potential evaporation warmest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the warmest quarter. Potential evaporation wettest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the wettest quarter. Precipitation of coldest quarter (BIO19) m s-1 The mean of the monthly mean precipitation during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO19. Precipitation of driest month (BIO14) m s-1 Minimum of the monthly precipitation. This indicator corresponds to the official BIOCLIM variable BIO14. Precipitation of driest quarter (BIO17) m s-1 The mean of monthly mean precipitation during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO17. Precipitation of warmest quarter (BIO18) m s-1 The mean of monthly mean precipitation during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO18. Precipitation of wettest month (BIO13) m s-1 Maximum of the monthly precipitation. This indicator corresponds to the official BIOCLIM variable BIO13. Precipitation of wettest quarter (BIO16) m s-1 The mean of monthly mean precipitation during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO16. Precipitation seasonality (BIO15) % Annual coefficient of variation of the monthly precipitation. This indicator corresponds to the official BIOCLIM variable BIO15. Sea ice concentration Dimensionless The fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land sea mask and lake cover, at the resolution being used. The data is available per month. Sea surface temperature K Temperature of sea water near the surface. The data is available per month. Summer days day Number of days in a year for which the daily maximum temperature is not lower than 298.15 K (25 oC). Surface latent heat flux annual mean W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface latent heat flux coldest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the through the effects of turbulent air motion, averaged over the coldest quarter. Surface latent heat flux driest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface latent heat flux warmest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface latent heat flux wettest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the wettest quarter. Surface sensible heat flux annual mean W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface sensible heat flux coldest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the coldest quarter. Surface sensible heat flux driest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface sensible heat flux warmest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface sensible heat flux wettest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere the effects of turbulent air motion, averaged over the wettest quarter. Temperature annual range (BIO07) K Daily maximum temperature averaged over the warmest month (BIO05) minus daily minimum temperature averaged over the coldest month (BIO06). This indicator corresponds to the official BIOCLIM variable BIO07. Temperature seasonality (BIO04) K Standard deviation of the monthly mean temperature multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO04. Volumetric soil water layer 1 annual mean m3 m-3 The volume of water in soil layer 1 (0-7cm, the surface is at 0 cm) averaged over the year. The ECMWF Integrated Forecasting System model has a four-layer representation of soil; Layer 1: 0-7 cm; Layer 2: 7-28 cm; Layer 3: 28-100 cm; Layer 4: 100-289 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 coldest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the coldest quarter. Volumetric soil water layer 1 driest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the driest quarter. Volumetric soil water layer 1 warmest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the warmest quarter. Volumetric soil water layer 1 wettest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the wettest quarter. Water vapour pressure Pa Contribution to the total atmospheric pressure provided by the water vapour over the period 00-24h local time per unit of time. The data are provided as both annual and monthly means. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Zonal wind speed m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity near the surface. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Annual mean temperature (BIO01) K Annual mean of the monthly mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01. Annual mean temperature (BIO01) K Annual mean of the monthly mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01. Annual precipitation (BIO12) m s-1 Annual mean of the monthly mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Annual precipitation (BIO12) m s-1 Annual mean of the monthly mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Aridity annual mean Dimensionless Monthly potential evaporation (m s-1) divided by the monthly mean precipitation (m s-1), averaged over the year. Aridity annual mean Dimensionless Monthly potential evaporation (m s-1) divided by the monthly mean precipitation (m s-1), averaged over the year. Aridity coldest quarter Dimensionless Monthly potential evaporation (m s-1) divided by the monthly mean precipitation (m s-1), averaged over the coldest quarter. Aridity coldest quarter Dimensionless Monthly potential evaporation (m s-1) divided by the monthly mean precipitation (m s-1), averaged over the coldest quarter. Aridity driest quarter Dimensionless Monthly potential evaporation (m s-1) divided by the monthly mean precipitation (m s-1), averaged over the driest quarter. Aridity driest quarter Dimensionless Monthly potential evaporation (m s-1) divided by the monthly mean precipitation (m s-1), averaged over the driest quarter. Aridity warmest quarter Dimensionless Monthly potential evaporation (m s-1) divided by the monthly mean precipitation (m s-1), averaged over the warmest quarter. Aridity warmest quarter Dimensionless Monthly potential evaporation (m s-1) divided by the monthly mean precipitation (m s-1), averaged over the warmest quarter. Aridity wettest quarter Dimensionless Monthly potential evaporation (m s-1) divided by the monthly mean precipitation (m s-1), averaged over the wettest quarter. Aridity wettest quarter Dimensionless Monthly potential evaporation (m s-1) divided by the monthly mean precipitation (m s-1), averaged over the wettest quarter. Cloud cover Dimensionless Fraction of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. The data are provided as both annual and monthly means. Cloud cover Dimensionless Fraction of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. The data are provided as both annual and monthly means. Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Evaporative fraction annual mean Dimensionless Monthly surface latent heat flux (W m-2) divided by the monthly total sensible and latent heat flux (W m-2), averaged over the year. Evaporative fraction annual mean Dimensionless Monthly surface latent heat flux (W m-2) divided by the monthly total sensible and latent heat flux (W m-2), averaged over the year. Evaporative fraction coldest quarter Dimensionless Monthly surface latent heat flux (W m-2) divided by the monthly total sensible and latent heat flux (W m-2), averaged over the coldest quarter. Evaporative fraction coldest quarter Dimensionless Monthly surface latent heat flux (W m-2) divided by the monthly total sensible and latent heat flux (W m-2), averaged over the coldest quarter. Evaporative fraction driest quarter Dimensionless Monthly surface latent heat flux (W m-2) divided by the monthly total sensible and latent heat flux (W m-2), averaged over the driest quarter. Evaporative fraction driest quarter Dimensionless Monthly surface latent heat flux (W m-2) divided by the monthly total sensible and latent heat flux (W m-2), averaged over the driest quarter. Evaporative fraction warmest quarter Dimensionless Monthly surface latent heat flux (W m-2) divided by the monthly total sensible and latent heat flux (W m-2), averaged over the warmest quarter. Evaporative fraction warmest quarter Dimensionless Monthly surface latent heat flux (W m-2) divided by the monthly total sensible and latent heat flux (W m-2), averaged over the warmest quarter. Evaporative fraction wettest quarter Dimensionless Monthly surface latent heat flux (W m-2) divided by the monthly total sensible and latent heat flux (W m-2), averaged over the wettest quarter. Evaporative fraction wettest quarter Dimensionless Monthly surface latent heat flux (W m-2) divided by the monthly total sensible and latent heat flux (W m-2), averaged over the wettest quarter. Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). The data is aggregated as a monthly sum and annual sum. Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). The data is aggregated as a monthly sum and annual sum. Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). The data is aggregated over the months. Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). The data is aggregated over the months. Growing degree days during growing season length K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC) during the period between the growing season start and end. Growing degree days during growing season length K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC) during the period between the growing season start and end. Growing season end of season day of year The first day of a period of 5 consecutive days in the second half of the year with a mean daily temperature below 278 K (5oC). Growing season end of season day of year The first day of a period of 5 consecutive days in the second half of the year with a mean daily temperature below 278 K (5oC). Growing season length day Number of days between the start and the end of the growing season. Growing season length day Number of days between the start and the end of the growing season. Growing season start of season day of year The first day of the year of a period of 5 consecutive days with a mean daily temperature above 278 K (5 oC). Growing season start of season day of year The first day of the year of a period of 5 consecutive days with a mean daily temperature above 278 K (5 oC). Isothermality (BIO03) % Monthly mean diurnal range divided by temperature annual range, multiplied by 100: (monthly mean diurnal range/temperature annual range) x100. This indicator corresponds to the official BIOCLIM variable BIO03. Isothermality (BIO03) % Monthly mean diurnal range divided by temperature annual range, multiplied by 100: (monthly mean diurnal range/temperature annual range) x100. This indicator corresponds to the official BIOCLIM variable BIO03. Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds (40-year averages). The Köppen-Geiger class indicator is provided based on 40-year mean climatologies of monthly temperature and precipitation, in line with the Köppen-Geiger standard. Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds (40-year averages). The Köppen-Geiger class indicator is provided based on 40-year mean climatologies of monthly temperature and precipitation, in line with the Köppen-Geiger standard. Maximum 2m temperature K Mean of the daily maximum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Maximum 2m temperature K Mean of the daily maximum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Maximum length of dry spells day Maximum number of consecutive dry spell days within a year. Maximum length of dry spells day Maximum number of consecutive dry spell days within a year. Maximum precipitation m s-1 Maximum of the daily mean precipitation. The data is aggregated over the year. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000. Maximum precipitation m s-1 Maximum of the daily mean precipitation. The data is aggregated over the year. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000. Maximum temperature of the warmest month (BIO05) K Maximum daily temperature averaged over the month with the highest monthly mean of daily maximum temperature. This indicator corresponds to the official BIOCLIM variable BIO05. Maximum temperature of the warmest month (BIO05) K Maximum daily temperature averaged over the month with the highest monthly mean of daily maximum temperature. This indicator corresponds to the official BIOCLIM variable BIO05. Mean diurnal range (BIO02) K Mean of the monthly maximum temperature minus the monthly minimum temperature. The data is aggregated over the months. This indicator corresponds to the official BIOCLIM variable BIO02. Mean diurnal range (BIO02) K Mean of the monthly maximum temperature minus the monthly minimum temperature. The data is aggregated over the months. This indicator corresponds to the official BIOCLIM variable BIO02. Mean intensity of dry spells day Determine the consecutive dry days at each day in a year, then take the average of these daily values over the year. Mean intensity of dry spells day Determine the consecutive dry days at each day in a year, then take the average of these daily values over the year. Mean length of dry spells day Mean length of dry spell days with a minimum of 5 days within a year. Mean length of dry spells day Mean length of dry spell days with a minimum of 5 days within a year. Mean precipitation m s-1 Average of the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Mean precipitation m s-1 Average of the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Mean temperature of coldest quarter (BIO11) K The mean of monthly mean temperature during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO11. Mean temperature of coldest quarter (BIO11) K The mean of monthly mean temperature during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO11. Mean temperature of driest quarter (BIO09) K The mean of monthly mean temperature during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO09. Mean temperature of driest quarter (BIO09) K The mean of monthly mean temperature during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO09. Mean temperature of warmest quarter (BIO10) K The mean of monthly mean temperature during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO10. Mean temperature of warmest quarter (BIO10) K The mean of monthly mean temperature during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO10. Mean temperature of wettest quarter (BIO08) K The mean of monthly mean temperature during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO08. Mean temperature of wettest quarter (BIO08) K The mean of monthly mean temperature during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO08. Meridional wind speed m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity near the surface. Meridional wind speed m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity near the surface. Minimum 2m temperature K Mean of the daily minimum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Minimum 2m temperature K Mean of the daily minimum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Minimum temperature of the coldest month (BIO06) K Minimum daily temperature averaged over the month with the lowest monthly mean of daily minimum temperature. This indicator corresponds to the official BIOCLIM variable BIO06. Minimum temperature of the coldest month (BIO06) K Minimum daily temperature averaged over the month with the lowest monthly mean of daily minimum temperature. This indicator corresponds to the official BIOCLIM variable BIO06. Number of dry spells Dimensionless Number of dry spells with a minimum of 5 days that occur in a year. Number of dry spells Dimensionless Number of dry spells with a minimum of 5 days that occur in a year. Potential evaporation annual mean m s-1 Annual averaged amount of water that would evaporate and transpire if there is unlimited water supply. Potential evaporation annual mean m s-1 Annual averaged amount of water that would evaporate and transpire if there is unlimited water supply. Potential evaporation coldest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the coldest quarter. Potential evaporation coldest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the coldest quarter. Potential evaporation driest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the driest quarter. Potential evaporation driest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the driest quarter. Potential evaporation warmest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the warmest quarter. Potential evaporation warmest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the warmest quarter. Potential evaporation wettest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the wettest quarter. Potential evaporation wettest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the wettest quarter. Precipitation of coldest quarter (BIO19) m s-1 The mean of the monthly mean precipitation during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO19. Precipitation of coldest quarter (BIO19) m s-1 The mean of the monthly mean precipitation during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO19. Precipitation of driest month (BIO14) m s-1 Minimum of the monthly precipitation. This indicator corresponds to the official BIOCLIM variable BIO14. Precipitation of driest month (BIO14) m s-1 Minimum of the monthly precipitation. This indicator corresponds to the official BIOCLIM variable BIO14. Precipitation of driest quarter (BIO17) m s-1 The mean of monthly mean precipitation during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO17. Precipitation of driest quarter (BIO17) m s-1 The mean of monthly mean precipitation during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO17. Precipitation of warmest quarter (BIO18) m s-1 The mean of monthly mean precipitation during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO18. Precipitation of warmest quarter (BIO18) m s-1 The mean of monthly mean precipitation during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO18. Precipitation of wettest month (BIO13) m s-1 Maximum of the monthly precipitation. This indicator corresponds to the official BIOCLIM variable BIO13. Precipitation of wettest month (BIO13) m s-1 Maximum of the monthly precipitation. This indicator corresponds to the official BIOCLIM variable BIO13. Precipitation of wettest quarter (BIO16) m s-1 The mean of monthly mean precipitation during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO16. Precipitation of wettest quarter (BIO16) m s-1 The mean of monthly mean precipitation during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO16. Precipitation seasonality (BIO15) % Annual coefficient of variation of the monthly precipitation. This indicator corresponds to the official BIOCLIM variable BIO15. Precipitation seasonality (BIO15) % Annual coefficient of variation of the monthly precipitation. This indicator corresponds to the official BIOCLIM variable BIO15. Sea ice concentration Dimensionless The fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land sea mask and lake cover, at the resolution being used. The data is available per month. Sea ice concentration Dimensionless The fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land sea mask and lake cover, at the resolution being used. The data is available per month. Sea surface temperature K Temperature of sea water near the surface. The data is available per month. Sea surface temperature K Temperature of sea water near the surface. The data is available per month. Summer days day Number of days in a year for which the daily maximum temperature is not lower than 298.15 K (25 oC). Summer days day Number of days in a year for which the daily maximum temperature is not lower than 298.15 K (25 oC). Surface latent heat flux annual mean W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface latent heat flux annual mean W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface latent heat flux coldest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the through the effects of turbulent air motion, averaged over the coldest quarter. Surface latent heat flux coldest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the through the effects of turbulent air motion, averaged over the coldest quarter. Surface latent heat flux driest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface latent heat flux driest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface latent heat flux warmest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface latent heat flux warmest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface latent heat flux wettest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the wettest quarter. Surface latent heat flux wettest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the wettest quarter. Surface sensible heat flux annual mean W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface sensible heat flux annual mean W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface sensible heat flux coldest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the coldest quarter. Surface sensible heat flux coldest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the coldest quarter. Surface sensible heat flux driest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface sensible heat flux driest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface sensible heat flux warmest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface sensible heat flux warmest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface sensible heat flux wettest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere the effects of turbulent air motion, averaged over the wettest quarter. Surface sensible heat flux wettest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere the effects of turbulent air motion, averaged over the wettest quarter. Temperature annual range (BIO07) K Daily maximum temperature averaged over the warmest month (BIO05) minus daily minimum temperature averaged over the coldest month (BIO06). This indicator corresponds to the official BIOCLIM variable BIO07. Temperature annual range (BIO07) K Daily maximum temperature averaged over the warmest month (BIO05) minus daily minimum temperature averaged over the coldest month (BIO06). This indicator corresponds to the official BIOCLIM variable BIO07. Temperature seasonality (BIO04) K Standard deviation of the monthly mean temperature multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO04. Temperature seasonality (BIO04) K Standard deviation of the monthly mean temperature multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO04. Volumetric soil water layer 1 annual mean m3 m-3 The volume of water in soil layer 1 (0-7cm, the surface is at 0 cm) averaged over the year. The ECMWF Integrated Forecasting System model has a four-layer representation of soil; Layer 1: 0-7 cm; Layer 2: 7-28 cm; Layer 3: 28-100 cm; Layer 4: 100-289 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 annual mean m3 m-3 The volume of water in soil layer 1 (0-7cm, the surface is at 0 cm) averaged over the year. The ECMWF Integrated Forecasting System model has a four-layer representation of soil; Layer 1: 0-7 cm; Layer 2: 7-28 cm; Layer 3: 28-100 cm; Layer 4: 100-289 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 coldest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the coldest quarter. Volumetric soil water layer 1 coldest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the coldest quarter. Volumetric soil water layer 1 driest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the driest quarter. Volumetric soil water layer 1 driest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the driest quarter. Volumetric soil water layer 1 warmest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the warmest quarter. Volumetric soil water layer 1 warmest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the warmest quarter. Volumetric soil water layer 1 wettest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the wettest quarter. Volumetric soil water layer 1 wettest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the wettest quarter. Water vapour pressure Pa Contribution to the total atmospheric pressure provided by the water vapour over the period 00-24h local time per unit of time. The data are provided as both annual and monthly means. Water vapour pressure Pa Contribution to the total atmospheric pressure provided by the water vapour over the period 00-24h local time per unit of time. The data are provided as both annual and monthly means. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Zonal wind speed m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity near the surface. Zonal wind speed m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity near the surface. 262 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/kuroshio-phase-observations-reprocessing http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_CURRENTS_kuroshio_phase_area_averaged Kuroshio Phase from Observations Reprocessing DEFINITION The indicator of the Kuroshio extension phase variations is based on the standardized high frequency altimeter Eddy Kinetic Energy (EKE) averaged in the area 142-149°E and 32-37°N and computed from the DUACS (https://duacs.cls.fr) delayed-time (reprocessed version DT-2021, CMEMS SEALEVEL_GLO_PHY_L4_MY_008_047) and near real-time (CMEMS SEALEVEL_GLO_PHY_L4_NRT_OBSERVATIONS_008_046) altimeter sea level gridded products. The change in the reprocessed version (previously DT-2018) and the extension of the mean value of the EKE (now 27 years, previously 20 years) induce some slight changes not impacting the general variability of the Kuroshio extension (correlation coefficient of 0.988 for the total period, 0.994 for the delayed time period only). https://duacs.cls.fr CONTEXT The long-term mean and trends alone do not give a complete view of the likely changes in position of unstable western boundary current extensions (Kelly et al., 2010). The Kuroshio Extension is an eastward-flowing current in the subtropical western North Pacific after the Kuroshio separates from the coast of Japan at 35°N, 140°E. Being the extension of a wind-driven western boundary current, the Kuroshio Extension is characterized by a strong variability and is rich in large-amplitude meanders and energetic eddies (Niiler et al., 2003; Qiu, 2003, 2002). The Kuroshio Extension region has the largest sea surface height variability on sub-annual and decadal time scales in the extratropical North Pacific Ocean (Jayne et al., 2009; Qiu and Chen, 2010, 2005). Prediction and monitoring of the path of the Kuroshio are of huge importance for local economies as the position of the Kuroshio extension strongly determines the regions where phytoplankton and hence fish are located. CMEMS KEY FINDINGS The different states of the Kuroshio extension phase have been presented and validated by Bessières et al. (2013) and further reported by Drévillon et al. (2018) in the Copernicus Ocean State Report #2. Two rather different states of the Kuroshio extension are observed: an ‘elongated state’ (also called ‘strong state’) corresponding to a narrow strong steady jet, and a ‘contracted state’ (also called ‘weak state’) in which the jet is weaker and more unsteady, spreading on a wider latitudinal band. When the Kuroshio Extension jet is in a contracted (elongated) state, the upstream Kuroshio Extension path tends to become more (less) variable and regional eddy kinetic energy level tends to be higher (lower). In between these two opposite phases, the Kuroshio extension jet has many intermediate states of transition and presents either progressively weakening or strengthening trends. In 2018, the indicator reveals an elongated state followed by a weakening neutral phase since then. DOI (product):https://doi.org/10.48670/moi-00222 https://doi.org/10.48670/moi-00222 263 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-europe-air-quality-reanalyses-deprecated https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses-deprecated cams-europe-air-quality-reanalyses-deprecated This dataset has been deprecated, please visit the updated version. This dataset has been deprecated, please visit the updated version. the updated version. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Horizontal coverage Europe (east boundary=25.0° W, west=45.0° E, south=30.0° N, north=72.0°) Horizontal resolution 0.1°x0.1° (10 km x 10 km) Vertical coverage Surface, 50m, 250m, 500m, 1000m, 2000m, 3000m, 5000m Temporal coverage 2018, 2020 Temporal resolution monthly files containing 1-hourly analyses File format NetCDF Update frequency twice a year DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Europe (east boundary=25.0° W, west=45.0° E, south=30.0° N, north=72.0°) Horizontal coverage Europe (east boundary=25.0° W, west=45.0° E, south=30.0° N, north=72.0°) Horizontal resolution 0.1°x0.1° (10 km x 10 km) Horizontal resolution 0.1°x0.1° (10 km x 10 km) Vertical coverage Surface, 50m, 250m, 500m, 1000m, 2000m, 3000m, 5000m Vertical coverage Surface, 50m, 250m, 500m, 1000m, 2000m, 3000m, 5000m Temporal coverage 2018, 2020 Temporal coverage 2018, 2020 Temporal resolution monthly files containing 1-hourly analyses Temporal resolution monthly files containing 1-hourly analyses File format NetCDF File format NetCDF Update frequency twice a year Update frequency twice a year MAIN VARIABLES Name Units Ammonia µg m-3 Carbon monoxide µg m-3 Nitrogen dioxide µg m-3 Nitrogen monoxide µg m-3 Non-methane volatile organic compounds (VOCs) µg m-3 Ozone µg m-3 PM10 dust fraction µg m-3 PM2.5 secondary inorganic aerosol fraction µg m-3 Particulate matter d < 10 µm (PM10) µg m-3 Particulate matter d < 2.5 µm (PM2.5) µg m-3 Peroxyacyl nitrates µg m-3 Sulphur dioxide µg m-3 MAIN VARIABLES MAIN VARIABLES Name Units Name Units Ammonia µg m-3 Ammonia µg m-3 Carbon monoxide µg m-3 Carbon monoxide µg m-3 Nitrogen dioxide µg m-3 Nitrogen dioxide µg m-3 Nitrogen monoxide µg m-3 Nitrogen monoxide µg m-3 Non-methane volatile organic compounds (VOCs) µg m-3 Non-methane volatile organic compounds (VOCs) µg m-3 Ozone µg m-3 Ozone µg m-3 PM10 dust fraction µg m-3 PM10 dust fraction µg m-3 PM2.5 secondary inorganic aerosol fraction µg m-3 PM2.5 secondary inorganic aerosol fraction µg m-3 Particulate matter d < 10 µm (PM10) µg m-3 Particulate matter d < 10 µm (PM10) µg m-3 Particulate matter d < 2.5 µm (PM2.5) µg m-3 Particulate matter d < 2.5 µm (PM2.5) µg m-3 Peroxyacyl nitrates µg m-3 Peroxyacyl nitrates µg m-3 Sulphur dioxide µg m-3 Sulphur dioxide µg m-3 264 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-biodiversity-era5-regional https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-biodiversity-era5-regional sis-biodiversity-era5-regional This dataset provides a historical reconstruction of bioclimatic indicators at 1 x 1 km resolution for Europe, Northern Brazil and Central Africa. These bioclimate indicators describe how the climate affects ecosystems, the services they deliver, and nature’s biodiversity. They are specifically relevant for applications within the biodiversity and ecosystem services community. The 76 indicators cover bioclimatic variables for the land environment, characterising surface energy, drought, soil moisture and the (near-) surface climate including wind as well as Essential Climate Variables (ECV) relevant to the biodiversity community and are based on hourly or monthly ERA5 (ERA5-Land) reanalysis data. The indicators have been downscaled to 1 x 1 km resolution using a statistical downscaling methodology that takes into account the relationship between orography and a climate state variable. The bioclimatic indicators are widely used within the biodiversity community and have been chosen based on user requirements and consultation with stakeholders, in order to facilitate the direct use of climate information in screening analyses or in diverse downstream applications. The temporal resolution differs depending on the indicator, varying between; monthly, annual, and multi-annual averages. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage Central Africa (15S-25N, 0E-35E) Europe (35N-70N, 25W-35E) Northern Brazil (12S-20S, 35W-43W) Horizontal resolution 1 km x 1 km Vertical coverage Surface Vertical resolution Single level Temporal coverage ERA5 reanalysis: from 1979 to 2018 ERA5-Land reanalysis: from 2001 to 2018 Temporal resolution Central Africa: ERA5 - 20-year average (1979-1998), no data for ERA5-Land Europe: ERA5 - 40-year average (1979-2018), ERA5-Land - 18-year average (2001-2018) Northern Brazil: ERA5 - 40-year average (1979-2018), ERA5-Land - 18-year average (2001-2018) File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Central Africa (15S-25N, 0E-35E) Europe (35N-70N, 25W-35E) Northern Brazil (12S-20S, 35W-43W) Horizontal coverage Central Africa (15S-25N, 0E-35E) Europe (35N-70N, 25W-35E) Northern Brazil (12S-20S, 35W-43W) Central Africa (15S-25N, 0E-35E) Europe (35N-70N, 25W-35E) Northern Brazil (12S-20S, 35W-43W) Horizontal resolution 1 km x 1 km Horizontal resolution 1 km x 1 km Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage ERA5 reanalysis: from 1979 to 2018 ERA5-Land reanalysis: from 2001 to 2018 Temporal coverage ERA5 reanalysis: from 1979 to 2018 ERA5-Land reanalysis: from 2001 to 2018 ERA5 reanalysis: from 1979 to 2018 ERA5-Land reanalysis: from 2001 to 2018 Temporal resolution Central Africa: ERA5 - 20-year average (1979-1998), no data for ERA5-Land Europe: ERA5 - 40-year average (1979-2018), ERA5-Land - 18-year average (2001-2018) Northern Brazil: ERA5 - 40-year average (1979-2018), ERA5-Land - 18-year average (2001-2018) Temporal resolution Central Africa: ERA5 - 20-year average (1979-1998), no data for ERA5-Land Europe: ERA5 - 40-year average (1979-2018), ERA5-Land - 18-year average (2001-2018) Northern Brazil: ERA5 - 40-year average (1979-2018), ERA5-Land - 18-year average (2001-2018) Central Africa: ERA5 - 20-year average (1979-1998), no data for ERA5-Land Europe: ERA5 - 40-year average (1979-2018), ERA5-Land - 18-year average (2001-2018) Northern Brazil: ERA5 - 40-year average (1979-2018), ERA5-Land - 18-year average (2001-2018) File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.7, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Versions 1.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Annual mean temperature (BIO01) K Annual mean of the monthly mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Annual precipitation (BIO12) m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Aridity annual mean Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the year. Aridity coldest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the coldest quarter. Aridity driest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the driest quarter. Aridity warmest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the warmest quarter. Aridity wettest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the wettest quarter. Cloud cover Dimensionless Fraction of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Evaporative fraction annual mean Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the year. Evaporative fraction coldest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the coldest quarter. Evaporative fraction driest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the driest quarter. Evaporative fraction warmest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the warmest quarter. Evaporative fraction wettest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the wettest quarter. Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). The data is aggregated over the months. Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). The data is aggregated over the months. Growing degree days during growing season length K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC) during the period between the growing season start and end. Growing season end of season day of year The first day of a period of 5 consecutive days in the second half of the year with a mean daily temperature below 278 K (5oC). Growing season length day Number of days between the start and the end of the growing season. Growing season start of season day of year The first day of the year of a period of 5 consecutive days with a mean daily temperature above 278 K (5 oC). Isothermality (BIO03) % Monthly mean diurnal range divided by temperature annual range multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO03. Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds (40-year averages). The Köppen-Geiger class indicator is provided based on 40-year mean climatologies of monthly temperature and precipitation, in line with the Köppen-Geiger standard. Maximum 2m temperature K Mean of the daily maximum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Maximum length of dry spells day Maximum number of consecutive dry spells days within a year. Maximum precipitation m s-1 Maximum of the daily mean precipitation. The data is aggregated over the year. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000. Maximum temperature of the warmest month (BIO05) K Maximum daily temperature of the month with the highest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO05. Mean diurnal range (BIO02) K Mean of the daily maximum temperature minus the daily minimum temperature. The data is aggregated over the months. This indicator corresponds to the official BIOCLIM variable BIO02. Mean intensity of dry spells day Determine the consecutive dry days at each day in a year, then take the average of these daily values over the year. Mean length of dry spells day Mean length of dry spells with a minimum of 5 days within a year. Mean precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Mean temperature K Mean of the daily mean temperature near the surface. The data is aggregated over the months and the year (BIO01). Mean temperature of coldest quarter (BIO11) K The mean of monthly mean temperature during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO11. Mean temperature of driest quarter (BIO09) K The mean of monthly mean temperature during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO09. Mean temperature of warmest quarter (BIO10) K The mean of monthly mean temperature during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO10. Mean temperature of wettest quarter (BIO08) K The mean of monthly mean temperature during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO08. Meridional wind speed m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity near the surface. Minimum temperature K Mean of the daily minimum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Minimum temperature of the coldest month (BIO06) K Minimum daily temperature of the month with the lowest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO06. Number of dry spells Dimensionless Number of dry spells with a minimum of 5 days that occur in a year. Potential evaporation annual mean m s-1 Annual averaged amount of water that would evaporate and transpire if there is unlimited water supply. Potential evaporation of coldest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the coldest quarter defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. Potential evaporation of driest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the driest quarter defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. Potential evaporation of warmest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the warmest quarter defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. Potential evaporation of wettest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the wettest quarter defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. Precipitation of coldest quarter (BIO19) m s-1 The mean of monthly mean precipitation amount during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO19. Precipitation of driest month (BIO14) m s-1 Minimum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO14. Precipitation of driest quarter (BIO17) m s-1 The mean of monthly mean precipitation amount during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO17. Precipitation of warmest quarter (BIO18) m s-1 The mean of monthly mean precipitation amount during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO18. Precipitation of wettest month (BIO13) m s-1 Maximum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO13. Precipitation of wettest quarter (BIO16) m s-1 The mean of monthly mean precipitation amount during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO16. Precipitation seasonality (BIO15) % Annual coefficient of variation of the monthly precipitation sums. This indicator corresponds to the official BIOCLIM variable BIO15. Summer days day Number of days in a year for which the daily maximum temperature is not lower than 298.15 K (25 oC). Surface latent heat flux of annual mean W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface latent heat flux of coldest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the through the effects of turbulent air motion, averaged over the coldest quarter. Surface latent heat flux of driest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface latent heat flux of warmest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface latent heat flux of wettest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the wettest quarter. Surface sensible heat flux of annual mean W m-2 The transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface sensible heat flux of coldest quarter W m-2 The transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the coldest quarter. Surface sensible heat flux of driest quarter W m-2 The transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface sensible heat flux of warmest quarter W m-2 The transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface sensible heat flux of wettest quarter W m-2 The transfer of heat between the Earth's surface and the atmosphere the effects of turbulent air motion, averaged over the wettest quarter. Temperature annual range (BIO07) K Maximum temperature of the warmest month minus minimum temperature of the coldest month. This indicator corresponds to the official BIOCLIM variable BIO07. Temperature seasonality (BIO04) K Standard deviation of the monthly mean temperature multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO04. Volumetric soil water layer 1 annual mean m3 m-3 The volume of water in soil layer 1 (0-7cm, the surface is at 0 cm) averaged over the year. The ECMWF Integrated Forecasting System model has a four-layer representation of soil; Layer 1: 0-7 cm; Layer 2: 7-28 cm; Layer 3: 28-100 cm; Layer 4: 100-289 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 coldest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the coldest quarter. Volumetric soil water layer 1 driest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the driest quarter. Volumetric soil water layer 1 warmest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the warmest quarter. Volumetric soil water layer 1 wettest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the wettest quarter. Water vapor pressure Pa Contribution to the total atmospheric pressure provided by the water vapor over the period 00-24h local time per unit of time. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Zonal wind speed m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity near the surface. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Annual mean temperature (BIO01) K Annual mean of the monthly mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Annual mean temperature (BIO01) K Annual mean of the monthly mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Annual precipitation (BIO12) m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Annual precipitation (BIO12) m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Aridity annual mean Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the year. Aridity annual mean Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the year. Aridity coldest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the coldest quarter. Aridity coldest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the coldest quarter. Aridity driest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the driest quarter. Aridity driest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the driest quarter. Aridity warmest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the warmest quarter. Aridity warmest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the warmest quarter. Aridity wettest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the wettest quarter. Aridity wettest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the wettest quarter. Cloud cover Dimensionless Fraction of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. Cloud cover Dimensionless Fraction of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Evaporative fraction annual mean Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the year. Evaporative fraction annual mean Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the year. Evaporative fraction coldest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the coldest quarter. Evaporative fraction coldest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the coldest quarter. Evaporative fraction driest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the driest quarter. Evaporative fraction driest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the driest quarter. Evaporative fraction warmest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the warmest quarter. Evaporative fraction warmest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the warmest quarter. Evaporative fraction wettest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the wettest quarter. Evaporative fraction wettest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the wettest quarter. Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). The data is aggregated over the months. Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). The data is aggregated over the months. Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). The data is aggregated over the months. Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). The data is aggregated over the months. Growing degree days during growing season length K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC) during the period between the growing season start and end. Growing degree days during growing season length K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC) during the period between the growing season start and end. Growing season end of season day of year The first day of a period of 5 consecutive days in the second half of the year with a mean daily temperature below 278 K (5oC). Growing season end of season day of year The first day of a period of 5 consecutive days in the second half of the year with a mean daily temperature below 278 K (5oC). Growing season length day Number of days between the start and the end of the growing season. Growing season length day Number of days between the start and the end of the growing season. Growing season start of season day of year The first day of the year of a period of 5 consecutive days with a mean daily temperature above 278 K (5 oC). Growing season start of season day of year The first day of the year of a period of 5 consecutive days with a mean daily temperature above 278 K (5 oC). Isothermality (BIO03) % Monthly mean diurnal range divided by temperature annual range multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO03. Isothermality (BIO03) % Monthly mean diurnal range divided by temperature annual range multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO03. Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds (40-year averages). The Köppen-Geiger class indicator is provided based on 40-year mean climatologies of monthly temperature and precipitation, in line with the Köppen-Geiger standard. Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds (40-year averages). The Köppen-Geiger class indicator is provided based on 40-year mean climatologies of monthly temperature and precipitation, in line with the Köppen-Geiger standard. Maximum 2m temperature K Mean of the daily maximum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Maximum 2m temperature K Mean of the daily maximum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Maximum length of dry spells day Maximum number of consecutive dry spells days within a year. Maximum length of dry spells day Maximum number of consecutive dry spells days within a year. Maximum precipitation m s-1 Maximum of the daily mean precipitation. The data is aggregated over the year. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000. Maximum precipitation m s-1 Maximum of the daily mean precipitation. The data is aggregated over the year. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000. Maximum temperature of the warmest month (BIO05) K Maximum daily temperature of the month with the highest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO05. Maximum temperature of the warmest month (BIO05) K Maximum daily temperature of the month with the highest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO05. Mean diurnal range (BIO02) K Mean of the daily maximum temperature minus the daily minimum temperature. The data is aggregated over the months. This indicator corresponds to the official BIOCLIM variable BIO02. Mean diurnal range (BIO02) K Mean of the daily maximum temperature minus the daily minimum temperature. The data is aggregated over the months. This indicator corresponds to the official BIOCLIM variable BIO02. Mean intensity of dry spells day Determine the consecutive dry days at each day in a year, then take the average of these daily values over the year. Mean intensity of dry spells day Determine the consecutive dry days at each day in a year, then take the average of these daily values over the year. Mean length of dry spells day Mean length of dry spells with a minimum of 5 days within a year. Mean length of dry spells day Mean length of dry spells with a minimum of 5 days within a year. Mean precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Mean precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Mean temperature K Mean of the daily mean temperature near the surface. The data is aggregated over the months and the year (BIO01). Mean temperature K Mean of the daily mean temperature near the surface. The data is aggregated over the months and the year (BIO01). Mean temperature of coldest quarter (BIO11) K The mean of monthly mean temperature during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO11. Mean temperature of coldest quarter (BIO11) K The mean of monthly mean temperature during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO11. Mean temperature of driest quarter (BIO09) K The mean of monthly mean temperature during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO09. Mean temperature of driest quarter (BIO09) K The mean of monthly mean temperature during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO09. Mean temperature of warmest quarter (BIO10) K The mean of monthly mean temperature during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO10. Mean temperature of warmest quarter (BIO10) K The mean of monthly mean temperature during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO10. Mean temperature of wettest quarter (BIO08) K The mean of monthly mean temperature during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO08. Mean temperature of wettest quarter (BIO08) K The mean of monthly mean temperature during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO08. Meridional wind speed m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity near the surface. Meridional wind speed m s-1 Magnitude of the northward component of the two-dimensional horizontal air velocity near the surface. Minimum temperature K Mean of the daily minimum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Minimum temperature K Mean of the daily minimum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Minimum temperature of the coldest month (BIO06) K Minimum daily temperature of the month with the lowest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO06. Minimum temperature of the coldest month (BIO06) K Minimum daily temperature of the month with the lowest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO06. Number of dry spells Dimensionless Number of dry spells with a minimum of 5 days that occur in a year. Number of dry spells Dimensionless Number of dry spells with a minimum of 5 days that occur in a year. Potential evaporation annual mean m s-1 Annual averaged amount of water that would evaporate and transpire if there is unlimited water supply. Potential evaporation annual mean m s-1 Annual averaged amount of water that would evaporate and transpire if there is unlimited water supply. Potential evaporation of coldest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the coldest quarter defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. Potential evaporation of coldest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the coldest quarter defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. Potential evaporation of driest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the driest quarter defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. Potential evaporation of driest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the driest quarter defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. Potential evaporation of warmest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the warmest quarter defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. Potential evaporation of warmest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the warmest quarter defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. Potential evaporation of wettest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the wettest quarter defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. Potential evaporation of wettest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the wettest quarter defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. Precipitation of coldest quarter (BIO19) m s-1 The mean of monthly mean precipitation amount during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO19. Precipitation of coldest quarter (BIO19) m s-1 The mean of monthly mean precipitation amount during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO19. Precipitation of driest month (BIO14) m s-1 Minimum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO14. Precipitation of driest month (BIO14) m s-1 Minimum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO14. Precipitation of driest quarter (BIO17) m s-1 The mean of monthly mean precipitation amount during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO17. Precipitation of driest quarter (BIO17) m s-1 The mean of monthly mean precipitation amount during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO17. Precipitation of warmest quarter (BIO18) m s-1 The mean of monthly mean precipitation amount during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO18. Precipitation of warmest quarter (BIO18) m s-1 The mean of monthly mean precipitation amount during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO18. Precipitation of wettest month (BIO13) m s-1 Maximum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO13. Precipitation of wettest month (BIO13) m s-1 Maximum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO13. Precipitation of wettest quarter (BIO16) m s-1 The mean of monthly mean precipitation amount during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO16. Precipitation of wettest quarter (BIO16) m s-1 The mean of monthly mean precipitation amount during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO16. Precipitation seasonality (BIO15) % Annual coefficient of variation of the monthly precipitation sums. This indicator corresponds to the official BIOCLIM variable BIO15. Precipitation seasonality (BIO15) % Annual coefficient of variation of the monthly precipitation sums. This indicator corresponds to the official BIOCLIM variable BIO15. Summer days day Number of days in a year for which the daily maximum temperature is not lower than 298.15 K (25 oC). Summer days day Number of days in a year for which the daily maximum temperature is not lower than 298.15 K (25 oC). Surface latent heat flux of annual mean W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface latent heat flux of annual mean W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface latent heat flux of coldest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the through the effects of turbulent air motion, averaged over the coldest quarter. Surface latent heat flux of coldest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the through the effects of turbulent air motion, averaged over the coldest quarter. Surface latent heat flux of driest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface latent heat flux of driest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface latent heat flux of warmest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface latent heat flux of warmest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface latent heat flux of wettest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the wettest quarter. Surface latent heat flux of wettest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the wettest quarter. Surface sensible heat flux of annual mean W m-2 The transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface sensible heat flux of annual mean W m-2 The transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface sensible heat flux of coldest quarter W m-2 The transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the coldest quarter. Surface sensible heat flux of coldest quarter W m-2 The transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the coldest quarter. Surface sensible heat flux of driest quarter W m-2 The transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface sensible heat flux of driest quarter W m-2 The transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface sensible heat flux of warmest quarter W m-2 The transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface sensible heat flux of warmest quarter W m-2 The transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface sensible heat flux of wettest quarter W m-2 The transfer of heat between the Earth's surface and the atmosphere the effects of turbulent air motion, averaged over the wettest quarter. Surface sensible heat flux of wettest quarter W m-2 The transfer of heat between the Earth's surface and the atmosphere the effects of turbulent air motion, averaged over the wettest quarter. Temperature annual range (BIO07) K Maximum temperature of the warmest month minus minimum temperature of the coldest month. This indicator corresponds to the official BIOCLIM variable BIO07. Temperature annual range (BIO07) K Maximum temperature of the warmest month minus minimum temperature of the coldest month. This indicator corresponds to the official BIOCLIM variable BIO07. Temperature seasonality (BIO04) K Standard deviation of the monthly mean temperature multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO04. Temperature seasonality (BIO04) K Standard deviation of the monthly mean temperature multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO04. Volumetric soil water layer 1 annual mean m3 m-3 The volume of water in soil layer 1 (0-7cm, the surface is at 0 cm) averaged over the year. The ECMWF Integrated Forecasting System model has a four-layer representation of soil; Layer 1: 0-7 cm; Layer 2: 7-28 cm; Layer 3: 28-100 cm; Layer 4: 100-289 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 annual mean m3 m-3 The volume of water in soil layer 1 (0-7cm, the surface is at 0 cm) averaged over the year. The ECMWF Integrated Forecasting System model has a four-layer representation of soil; Layer 1: 0-7 cm; Layer 2: 7-28 cm; Layer 3: 28-100 cm; Layer 4: 100-289 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 coldest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the coldest quarter. Volumetric soil water layer 1 coldest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the coldest quarter. Volumetric soil water layer 1 driest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the driest quarter. Volumetric soil water layer 1 driest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the driest quarter. Volumetric soil water layer 1 warmest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the warmest quarter. Volumetric soil water layer 1 warmest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the warmest quarter. Volumetric soil water layer 1 wettest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the wettest quarter. Volumetric soil water layer 1 wettest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the wettest quarter. Water vapor pressure Pa Contribution to the total atmospheric pressure provided by the water vapor over the period 00-24h local time per unit of time. Water vapor pressure Pa Contribution to the total atmospheric pressure provided by the water vapor over the period 00-24h local time per unit of time. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Zonal wind speed m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity near the surface. Zonal wind speed m s-1 Magnitude of the eastward component of the two-dimensional horizontal air velocity near the surface. 265 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/medium-resolution-vegetation-phenology-and-productivity https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum_d1-0.pdf Medium Resolution Vegetation Phenology and Productivity: Season length (raster 500m), Oct. 2022 The raster file is the temporal trend in the length of the vegetation growing season. The length of growing season data set is based on the time series of the Plant Phenology Index (PPI) derived from the MODIS BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR). The PPI index is optimized for efficient monitoring of vegetation phenology and is derived from the source MODIS data using radiative transfer solutions applied to the reflectance in visible-red and near infrared spectral domains. The season length indicator is based on calculating the start and end of the growing season from the annual PPI temporal curve using the TIMESAT software for each year between and including 2000 and 2021. The Season Length (LENGTH), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Season Length is the number of days between the start and end dates of the vegetation growing season in the time profile of the Plant Phenology Index (PPI). The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Season Length is one of the 13 parameters. The full list is available in the Product User Manual: https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… The Season Length time series dataset is made available as raster files with 500x 500m resolution, in ETRS89-LAEA projection corresponding to the MCD43 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2000 onwards. It is updated in the first quarter of each year. The full on-line access to open and free data for this resource will be made available by the end of 2022. Until then the data will be made available 'on-demand' by filling in the form at: https://land.copernicus.eu/contact-form https://land.copernicus.eu/contact-form 266 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-high-resolution-and-ultra-high-resolution-sea http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_BS_SST_L4_NRT_OBSERVATIONS_010_006 Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis Short description: For the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain providess daily gap-free (L4) maps at high (HR 0.0625°) and ultra-high (UHR 0.01°) spatial resolution over the Black Sea. Remotely-sensed L4 SST datasets are operationally produced and distributed in near-real time by the Consiglio Nazionale delle Ricerche - Gruppo di Oceanografia da Satellite (CNR-GOS). These SST products are based on the nighttime images collected by the infrared sensors mounted on different satellite platforms, and cover the Southern European Seas. The CNR-GOS processing chain includes several modules, from the data extraction and preliminary quality control, to cloudy pixel removal and satellite images collating/merging. A two-step algorithm finally allows to interpolate SST data at high (HR 0.0625°) and ultra-high (UHR 0.01°) spatial resolution, applying statistical techniques. These L4 data are also used to estimate the SST anomaly with respect to a pentad climatology. The basic design and the main algorithms used are described in the following papers. DOI (product) :https://doi.org/10.48670/moi-00159 https://doi.org/10.48670/moi-00159 267 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-c3s-monthly-climate-covid-19-explorer https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-c3s-monthly-climate-covid-19-explorer app-c3s-monthly-climate-covid-19-explorer This application provides visualizations of data related to the COVID-19 virus spread along with climate information from the C3S Climate Data Store and atmospheric composition data from the CAMS Atmosphere Data Store. An interactive world map shows time averages of air temperature, humidity over selectable predefined periods and climatological surface concentrations of fine particulate matter (PM10) and nitrogen dioxide (NO2). Circles, representing the number of deaths related to COVID-19, are placed on regions where the virus has spread. Clicking on each circle, the time evolution of the number of fatalities in the corresponding region is shown, together with information on the local temperature and humidity for the selected period. This application is inspired by a series of research studies exploring the diffusion efficiency of the COVID-19 and the Influenza virus in different atmospheric stable conditions (e.g., see Sajadi et al., 2020; Lowen et al., 2007; Tamerius et al., 2013), as well as some recent work investigating at links between SARS-CoV-2, COVID-19 and air pollution which are still in the peer-review process (Wu et al., 2020; Setti et al., 2020). Given the novelty of the COVID-19 virus and the lack of confirmed relationships between the infection and the relevant climate variables, the application should only be considered as an exploratory tool. For simplicity, the map shows meteorological and atmospheric composition variables averaged over the same periods which fatalities numbers refer to, without taking into account any delay between infection and eventual death. While the white regions in both the temperature (humidity) map and the time series correspond to the values identified in Sajadi et al. (2020) as the most favorable ones for the spread of the virus, the other ranges used for the color palettes have been chosen arbitrarily. The number of fatalities has been chosen as indicator for the virus spread given its robustness with respect to the other available data (number of confirmed cases and number of recovered patients). The values represented in the meteorological time series refer to single points located approximately at the centre of each circle. COVID-19 related data are provided by Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE), and are available at the following GitHub repository. These are used in the application without any prior quality control by C3S. Meteorological data are from ERA5 reanalysis: hourly data on single levels and pressure levels and monthly averages on single levels and pressure levels. Atmospheric composition data (PM10 and NO2) are from the CAMS EAC4 global reanalysis (Inness et al., 2019). Shown are monthly means at the model level closest to the surface averaged over the period 2003-2018. The atmospheric composition fields therefore represent climatological conditions over a long period of time. It is noted that, over the period, anthropogenic emissions have changed in time due to evolving economies and in some areas abatement measures to improve air quality, but such a long period is useful to capture the variability of the natural sources of particulate matter such as wind-blown dust, wildfires and sea salts. GitHub repository The designations employed and the presentation of material on the map do not imply the expression of any opinion whatsoever on the part of the European Union concerning the legal status of any country, territory or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. User-selectable parameters User-selectable parameters Variable: temperature, humidity, particulate matter (PM10) and nitrogen dioxide; Month for COVID-19 related data and for climate data; Previous month for climate data. Variable: temperature, humidity, particulate matter (PM10) and nitrogen dioxide; Month for COVID-19 related data and for climate data; Previous month for climate data. Description of the graphical output Description of the graphical output The application presents an interactive world map showing time averages of one variable, selectable between air temperature, specific humidity, particulate matter (PM10) and nitrogen dioxide. The averaging month can be selected via a drop-down menu: for the meteorological variables for months since the beginning of 2020, values for 2020 are shown; for the remaining months of the year, a climatological average (2000-2019) is presented. For the meteorological variables, the average for the previous month may also be selected through a second drop-down menu. For the atmospheric composition variables, a climatological average (2003-2018) is presented. On the map, circles are placed in the centre of regions where the virus has spread; their size is proportional to the number of the deaths related to COVID-19 occurred in that region during the selected month. Clicking on a circle, a side window appears showing a plot of the daily time evolution of the local number of fatalities attributed to the virus, and plots of air temperature and specific humidity at a grid-point close to the centre of the circle, for the same time steps. INPUT VARIABLES Name Units Description Source Daily cumulated number of deaths attributed to COVID-19 virus Counts Daily cumulated number of deaths attributed to COVID-19 virus CSSEGISand Data - Brokered externally Hourly specific humidity at 1000 hPa kg kg-1 ERA5 hourly specific humidity at 1000 hPa ERA5 Hourly surface air temperature K ERA5 hourly 2m air temperature ERA5 Monthly average NO2 mass fraction in air kg kg-1 CAMS EAC4 monthly average NO2 mass fraction in air at surface CAMS EAC4 Monthly average PM10 concentration kg m-3 CAMS EAC4 monthly average PM10 concentration at surface CAMS EAC4 Monthly average specific humidity at 1000 hPa kg kg-1 ERA5 monthly average specific humidity at 1000 hPa ERA5 Monthly average surface air temperature K ERA5 monthly average 2m air temperature ERA5 INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Daily cumulated number of deaths attributed to COVID-19 virus Counts Daily cumulated number of deaths attributed to COVID-19 virus CSSEGISand Data - Brokered externally Daily cumulated number of deaths attributed to COVID-19 virus Counts Daily cumulated number of deaths attributed to COVID-19 virus CSSEGISand Data - Brokered externally Hourly specific humidity at 1000 hPa kg kg-1 ERA5 hourly specific humidity at 1000 hPa ERA5 Hourly specific humidity at 1000 hPa kg kg-1 ERA5 hourly specific humidity at 1000 hPa ERA5 ERA5 Hourly surface air temperature K ERA5 hourly 2m air temperature ERA5 Hourly surface air temperature K ERA5 hourly 2m air temperature ERA5 ERA5 Monthly average NO2 mass fraction in air kg kg-1 CAMS EAC4 monthly average NO2 mass fraction in air at surface CAMS EAC4 Monthly average NO2 mass fraction in air kg kg-1 CAMS EAC4 monthly average NO2 mass fraction in air at surface CAMS EAC4 CAMS EAC4 Monthly average PM10 concentration kg m-3 CAMS EAC4 monthly average PM10 concentration at surface CAMS EAC4 Monthly average PM10 concentration kg m-3 CAMS EAC4 monthly average PM10 concentration at surface CAMS EAC4 CAMS EAC4 Monthly average specific humidity at 1000 hPa kg kg-1 ERA5 monthly average specific humidity at 1000 hPa ERA5 Monthly average specific humidity at 1000 hPa kg kg-1 ERA5 monthly average specific humidity at 1000 hPa ERA5 ERA5 Monthly average surface air temperature K ERA5 monthly average 2m air temperature ERA5 Monthly average surface air temperature K ERA5 monthly average 2m air temperature ERA5 ERA5 268 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-biodiversity-cmip5-regional https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-biodiversity-cmip5-regional sis-biodiversity-cmip5-regional The dataset provides bioclimatic indicators derived from CMIP5 climate projections at 1 km x 1 km resolution for selected regions; Europe, Northern Brazil and Central Africa. This comprehensive set of bioclimatic indicators is specifically relevant for applications within the biodiversity and ecosystem services community. The 76 indicators cover bioclimatic variables for the land environment, characterising surface energy, drought, soil moisture and the (near-) surface climate including wind as well as Essential Climate Variables (ECV). The selection of indicators is based upon user requirements and consultation with stakeholders in order to facilitate the direct use of climate information in screening analyses or in diverse downstream applications. The indicators are calculated based on daily CMIP5 climate projections from 10 Global Circulation Models for two future climate scenarios, Representative Concentration Pathway (RCP) 4.5 & RCP 8.5. The indicators have been downscaled to 1 x 1 km resolution using a statistical downscaling methodology that takes into account the relationship between orography and a climate state variable. The data have been additionally bias-adjusted against ERA5 reanalysis data. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage Central Africa (15S-25N, 0E-35E) Europe (35N-70N, 25W-35E) Northern Brazil (12S-20S, 35W-43W) Horizontal resolution 1 km x 1 km Vertical coverage Surface Vertical resolution Single level Temporal coverage 1950-2100 Temporal resolution 20-year average File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Central Africa (15S-25N, 0E-35E) Europe (35N-70N, 25W-35E) Northern Brazil (12S-20S, 35W-43W) Horizontal coverage Central Africa (15S-25N, 0E-35E) Europe (35N-70N, 25W-35E) Northern Brazil (12S-20S, 35W-43W) Central Africa (15S-25N, 0E-35E) Europe (35N-70N, 25W-35E) Northern Brazil (12S-20S, 35W-43W) Horizontal resolution 1 km x 1 km Horizontal resolution 1 km x 1 km Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage 1950-2100 Temporal coverage 1950-2100 Temporal resolution 20-year average Temporal resolution 20-year average File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.7, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Versions 1.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Annual mean temperature (BIO01) K Annual mean of the daily mean temperature near the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Annual precipitation (BIO12) m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Aridity annual mean Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the year. Aridity coldest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the coldest quarter. Aridity driest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the driest quarter. Aridity warmest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the warmest quarter. Aridity wettest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the wettest quarter. Cloud cover Dimensionless Fraction of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Evaporative fraction annual mean Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the year. Evaporative fraction coldest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the coldest quarter. Evaporative fraction driest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the driest quarter Evaporative fraction warmest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the warmest quarter. Evaporative fraction wettest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the wettest quarter. Frost days day Number of days during the growing season with minimum temperature below 273 K (0oC). The data is aggregated over the months. Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5oC). The data is aggregated over the months. Growing degree days during growing season length K day year-1 Growing degree days in the growing season. Growing season end of season day of year The first day of a period of 5 consecutive days in the second half of the year with a mean daily temperature below 278 K (5oC). Growing season length day Number of days between the start and the end of the growing season. Growing season start of season day of year The first day of the year of a period of 5 consecutive days with a mean daily temperature above 278 K (5oC). Isothermality (BIO03) % (Monthly mean diurnal range divided by temperature annual range multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO03. Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds. Maximum 2m temperature K Mean of the daily maximum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Maximum length of dry spells day Maximum number of consecutive days of the dry spells within a year. Maximum precipitation m s-1 Maximum of the daily mean precipitation. The data is aggregated over the year. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000. Maximum temperature of the warmest month (BIO05) K Maximum daily temperature of the month with the highest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO05. Mean diurnal range (BIO02) K Mean of the daily maximum temperature minus the daily minimum temperature. The data is aggregated over the months. This indicator corresponds to the official BIOCLIM variable BIO02. Mean intensity of dry spells day Determine the consecutive dry days at each day in a year, then take the average of these daily values over the year. Mean length of dry spells day Mean length of dry spells with a minimum of 5 days within a year. Mean precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000 by 30.4 (average number of days per month) or by 365. Mean temperature K Mean of the daily mean temperature near the surface. The data is aggregated over the months and the year (=BIO01). Mean temperature of coldest quarter (BIO11) K The mean of monthly mean temperature during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO11. Mean temperature of driest quarter (BIO09) K The mean of monthly mean temperature during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO09. Mean temperature of warmest quarter (BIO10) K The mean of monthly mean temperature during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO10. Mean temperature of wettest quarter (BIO08) K The mean of monthly mean temperature during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO08. Minimum temperature K Mean of the daily minimum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Minimum temperature of the coldest month (BIO06) K Minimum daily temperature of the month with the lowest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO06. Number of dry spells Dimensionless Number of dry spells with a minimum of 5 days that occur in a year. Potential evaporation annual mean m s-1 Annual averaged amount of water that would evaporate and transpire if there is unlimited water supply. Potential evaporation coldest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the coldest quarter. Potential evaporation driest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the driest quarter. Potential evaporation warmest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the warmest quarter. Potential evaporation wettest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the wettest quarter. Precipitation in coldest quarter (BIO19) m s-1 The mean of monthly mean precipitation during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO19. Precipitation in driest quarter (BIO17) m s-1 The mean of monthly mean precipitation during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO17. Precipitation in warmest quarter (BIO18) m s-1 The mean of monthly mean precipitation during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO18. Precipitation in wettest quarter (BIO16) m s-1 The mean of monthly mean precipitation during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO16. Precipitation of driest month (BIO14) m s-1 Minimum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)*1000. This indicator corresponds to the official BIOCLIM variable BIO14. Precipitation of wettest month (BIO13) m s-1 Maximum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)*1000. This indicator corresponds to the official BIOCLIM variable BIO13. Precipitation seasonality (BIO15) % Annual coefficient of variation of the monthly precipitation sums. This indicator corresponds to the official BIOCLIM variable BIO15. Summer days day Number of days in a year for which the daily maximum temperature is not lower than 298.15 K (25oC). Surface latent heat flux annual mean W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface latent heat flux coldest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the through the effects of turbulent air motion, averaged over the coldest quarter. Surface latent heat flux driest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface latent heat flux warmest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface latent heat flux wettest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the wettest quarter. Surface sensible heat flux annual mean W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface sensible heat flux coldest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the coldest quarter. Surface sensible heat flux driest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface sensible heat flux warmest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface sensible heat flux wettest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere the effects of turbulent air motion, averaged over the wettest quarter. Temperature annual range (BIO07) K Maximum temperature of the warmest month minus minimum temperature of the coldest month. This indicator corresponds to the official BIOCLIM variable BIO07. Temperature seasonality (BIO04) K Standard deviation of the monthly mean temperature multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO04. Volumetric soil water layer 1 annual mean m3 m-3 The volume of water in soil layer 1 (0-7cm, the surface is at 0 cm) averaged over the year. The ECMWF Integrated Forecasting System model has a four-layer representation of soil; Layer 1: 0-7 cm; Layer 2: 7-28 cm; Layer 3: 28-100 cm; Layer 4: 100-289 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 coldest quarter m3 m-3 The volume of water in soil layer 1 (0 7cm, the surface is at 0 cm) averaged over the coldest quarter. Volumetric soil water layer 1 driest quarter m3 m-3 The volume of water in soil layer 1 (0 7cm, the surface is at 0 cm) averaged over the driest quarter. Volumetric soil water layer 1 warmest quarter m3 m-3 The volume of water in soil layer 1 (0 7cm, the surface is at 0 cm) averaged over the warmest quarter. Volumetric soil water layer 1 wettest quarter m3 m-3 The volume of water in soil layer 1 (0 7cm, the surface is at 0 cm) averaged over the wettest quarter. Water vapor pressure Pa Contribution to the total atmospheric pressure provided by the water vapor over the period 00-24h local time per unit of time. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Annual mean temperature (BIO01) K Annual mean of the daily mean temperature near the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Annual mean temperature (BIO01) K Annual mean of the daily mean temperature near the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Annual precipitation (BIO12) m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Annual precipitation (BIO12) m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Aridity annual mean Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the year. Aridity annual mean Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the year. Aridity coldest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the coldest quarter. Aridity coldest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the coldest quarter. Aridity driest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the driest quarter. Aridity driest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the driest quarter. Aridity warmest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the warmest quarter. Aridity warmest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the warmest quarter. Aridity wettest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the wettest quarter. Aridity wettest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the wettest quarter. Cloud cover Dimensionless Fraction of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. Cloud cover Dimensionless Fraction of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Evaporative fraction annual mean Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the year. Evaporative fraction annual mean Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the year. Evaporative fraction coldest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the coldest quarter. Evaporative fraction coldest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the coldest quarter. Evaporative fraction driest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the driest quarter Evaporative fraction driest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the driest quarter Evaporative fraction warmest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the warmest quarter. Evaporative fraction warmest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the warmest quarter. Evaporative fraction wettest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the wettest quarter. Evaporative fraction wettest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the wettest quarter. Frost days day Number of days during the growing season with minimum temperature below 273 K (0oC). The data is aggregated over the months. Frost days day Number of days during the growing season with minimum temperature below 273 K (0oC). The data is aggregated over the months. Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5oC). The data is aggregated over the months. Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5oC). The data is aggregated over the months. Growing degree days during growing season length K day year-1 Growing degree days in the growing season. Growing degree days during growing season length K day year-1 Growing degree days in the growing season. Growing season end of season day of year The first day of a period of 5 consecutive days in the second half of the year with a mean daily temperature below 278 K (5oC). Growing season end of season day of year The first day of a period of 5 consecutive days in the second half of the year with a mean daily temperature below 278 K (5oC). Growing season length day Number of days between the start and the end of the growing season. Growing season length day Number of days between the start and the end of the growing season. Growing season start of season day of year The first day of the year of a period of 5 consecutive days with a mean daily temperature above 278 K (5oC). Growing season start of season day of year The first day of the year of a period of 5 consecutive days with a mean daily temperature above 278 K (5oC). Isothermality (BIO03) % (Monthly mean diurnal range divided by temperature annual range multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO03. Isothermality (BIO03) % (Monthly mean diurnal range divided by temperature annual range multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO03. Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds. Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds. Maximum 2m temperature K Mean of the daily maximum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Maximum 2m temperature K Mean of the daily maximum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Maximum length of dry spells day Maximum number of consecutive days of the dry spells within a year. Maximum length of dry spells day Maximum number of consecutive days of the dry spells within a year. Maximum precipitation m s-1 Maximum of the daily mean precipitation. The data is aggregated over the year. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000. Maximum precipitation m s-1 Maximum of the daily mean precipitation. The data is aggregated over the year. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000. Maximum temperature of the warmest month (BIO05) K Maximum daily temperature of the month with the highest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO05. Maximum temperature of the warmest month (BIO05) K Maximum daily temperature of the month with the highest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO05. Mean diurnal range (BIO02) K Mean of the daily maximum temperature minus the daily minimum temperature. The data is aggregated over the months. This indicator corresponds to the official BIOCLIM variable BIO02. Mean diurnal range (BIO02) K Mean of the daily maximum temperature minus the daily minimum temperature. The data is aggregated over the months. This indicator corresponds to the official BIOCLIM variable BIO02. Mean intensity of dry spells day Determine the consecutive dry days at each day in a year, then take the average of these daily values over the year. Mean intensity of dry spells day Determine the consecutive dry days at each day in a year, then take the average of these daily values over the year. Mean length of dry spells day Mean length of dry spells with a minimum of 5 days within a year. Mean length of dry spells day Mean length of dry spells with a minimum of 5 days within a year. Mean precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000 by 30.4 (average number of days per month) or by 365. Mean precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000 by 30.4 (average number of days per month) or by 365. Mean temperature K Mean of the daily mean temperature near the surface. The data is aggregated over the months and the year (=BIO01). Mean temperature K Mean of the daily mean temperature near the surface. The data is aggregated over the months and the year (=BIO01). Mean temperature of coldest quarter (BIO11) K The mean of monthly mean temperature during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO11. Mean temperature of coldest quarter (BIO11) K The mean of monthly mean temperature during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO11. Mean temperature of driest quarter (BIO09) K The mean of monthly mean temperature during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO09. Mean temperature of driest quarter (BIO09) K The mean of monthly mean temperature during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO09. Mean temperature of warmest quarter (BIO10) K The mean of monthly mean temperature during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO10. Mean temperature of warmest quarter (BIO10) K The mean of monthly mean temperature during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO10. Mean temperature of wettest quarter (BIO08) K The mean of monthly mean temperature during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO08. Mean temperature of wettest quarter (BIO08) K The mean of monthly mean temperature during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO08. Minimum temperature K Mean of the daily minimum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Minimum temperature K Mean of the daily minimum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Minimum temperature of the coldest month (BIO06) K Minimum daily temperature of the month with the lowest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO06. Minimum temperature of the coldest month (BIO06) K Minimum daily temperature of the month with the lowest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO06. Number of dry spells Dimensionless Number of dry spells with a minimum of 5 days that occur in a year. Number of dry spells Dimensionless Number of dry spells with a minimum of 5 days that occur in a year. Potential evaporation annual mean m s-1 Annual averaged amount of water that would evaporate and transpire if there is unlimited water supply. Potential evaporation annual mean m s-1 Annual averaged amount of water that would evaporate and transpire if there is unlimited water supply. Potential evaporation coldest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the coldest quarter. Potential evaporation coldest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the coldest quarter. Potential evaporation driest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the driest quarter. Potential evaporation driest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the driest quarter. Potential evaporation warmest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the warmest quarter. Potential evaporation warmest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the warmest quarter. Potential evaporation wettest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the wettest quarter. Potential evaporation wettest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the wettest quarter. Precipitation in coldest quarter (BIO19) m s-1 The mean of monthly mean precipitation during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO19. Precipitation in coldest quarter (BIO19) m s-1 The mean of monthly mean precipitation during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO19. Precipitation in driest quarter (BIO17) m s-1 The mean of monthly mean precipitation during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO17. Precipitation in driest quarter (BIO17) m s-1 The mean of monthly mean precipitation during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO17. Precipitation in warmest quarter (BIO18) m s-1 The mean of monthly mean precipitation during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO18. Precipitation in warmest quarter (BIO18) m s-1 The mean of monthly mean precipitation during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO18. Precipitation in wettest quarter (BIO16) m s-1 The mean of monthly mean precipitation during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO16. Precipitation in wettest quarter (BIO16) m s-1 The mean of monthly mean precipitation during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO16. Precipitation of driest month (BIO14) m s-1 Minimum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)*1000. This indicator corresponds to the official BIOCLIM variable BIO14. Precipitation of driest month (BIO14) m s-1 Minimum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)*1000. This indicator corresponds to the official BIOCLIM variable BIO14. Precipitation of wettest month (BIO13) m s-1 Maximum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)*1000. This indicator corresponds to the official BIOCLIM variable BIO13. Precipitation of wettest month (BIO13) m s-1 Maximum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)*1000. This indicator corresponds to the official BIOCLIM variable BIO13. Precipitation seasonality (BIO15) % Annual coefficient of variation of the monthly precipitation sums. This indicator corresponds to the official BIOCLIM variable BIO15. Precipitation seasonality (BIO15) % Annual coefficient of variation of the monthly precipitation sums. This indicator corresponds to the official BIOCLIM variable BIO15. Summer days day Number of days in a year for which the daily maximum temperature is not lower than 298.15 K (25oC). Summer days day Number of days in a year for which the daily maximum temperature is not lower than 298.15 K (25oC). Surface latent heat flux annual mean W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface latent heat flux annual mean W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface latent heat flux coldest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the through the effects of turbulent air motion, averaged over the coldest quarter. Surface latent heat flux coldest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the through the effects of turbulent air motion, averaged over the coldest quarter. Surface latent heat flux driest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface latent heat flux driest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface latent heat flux warmest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface latent heat flux warmest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface latent heat flux wettest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the wettest quarter. Surface latent heat flux wettest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the wettest quarter. Surface sensible heat flux annual mean W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface sensible heat flux annual mean W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface sensible heat flux coldest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the coldest quarter. Surface sensible heat flux coldest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the coldest quarter. Surface sensible heat flux driest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface sensible heat flux driest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface sensible heat flux warmest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface sensible heat flux warmest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface sensible heat flux wettest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere the effects of turbulent air motion, averaged over the wettest quarter. Surface sensible heat flux wettest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere the effects of turbulent air motion, averaged over the wettest quarter. Temperature annual range (BIO07) K Maximum temperature of the warmest month minus minimum temperature of the coldest month. This indicator corresponds to the official BIOCLIM variable BIO07. Temperature annual range (BIO07) K Maximum temperature of the warmest month minus minimum temperature of the coldest month. This indicator corresponds to the official BIOCLIM variable BIO07. Temperature seasonality (BIO04) K Standard deviation of the monthly mean temperature multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO04. Temperature seasonality (BIO04) K Standard deviation of the monthly mean temperature multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO04. Volumetric soil water layer 1 annual mean m3 m-3 The volume of water in soil layer 1 (0-7cm, the surface is at 0 cm) averaged over the year. The ECMWF Integrated Forecasting System model has a four-layer representation of soil; Layer 1: 0-7 cm; Layer 2: 7-28 cm; Layer 3: 28-100 cm; Layer 4: 100-289 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 annual mean m3 m-3 The volume of water in soil layer 1 (0-7cm, the surface is at 0 cm) averaged over the year. The ECMWF Integrated Forecasting System model has a four-layer representation of soil; Layer 1: 0-7 cm; Layer 2: 7-28 cm; Layer 3: 28-100 cm; Layer 4: 100-289 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 coldest quarter m3 m-3 The volume of water in soil layer 1 (0 7cm, the surface is at 0 cm) averaged over the coldest quarter. Volumetric soil water layer 1 coldest quarter m3 m-3 The volume of water in soil layer 1 (0 7cm, the surface is at 0 cm) averaged over the coldest quarter. Volumetric soil water layer 1 driest quarter m3 m-3 The volume of water in soil layer 1 (0 7cm, the surface is at 0 cm) averaged over the driest quarter. Volumetric soil water layer 1 driest quarter m3 m-3 The volume of water in soil layer 1 (0 7cm, the surface is at 0 cm) averaged over the driest quarter. Volumetric soil water layer 1 warmest quarter m3 m-3 The volume of water in soil layer 1 (0 7cm, the surface is at 0 cm) averaged over the warmest quarter. Volumetric soil water layer 1 warmest quarter m3 m-3 The volume of water in soil layer 1 (0 7cm, the surface is at 0 cm) averaged over the warmest quarter. Volumetric soil water layer 1 wettest quarter m3 m-3 The volume of water in soil layer 1 (0 7cm, the surface is at 0 cm) averaged over the wettest quarter. Volumetric soil water layer 1 wettest quarter m3 m-3 The volume of water in soil layer 1 (0 7cm, the surface is at 0 cm) averaged over the wettest quarter. Water vapor pressure Pa Contribution to the total atmospheric pressure provided by the water vapor over the period 00-24h local time per unit of time. Water vapor pressure Pa Contribution to the total atmospheric pressure provided by the water vapor over the period 00-24h local time per unit of time. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. 269 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-oras5 https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-oras5 reanalysis-oras5 This dataset provides global ocean and sea-ice reanalysis (ORAS5: Ocean Reanalysis System 5) monthly mean data prepared by the European Centre for Medium-Range Weather Forecasts (ECMWF) OCEAN5 ocean analysis-reanalysis system. This system comprises 5 ensemble members from which one member is published in this catalogue entry. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset taking into account the laws of physics. The reanalysis provides information without temporal and spatial gaps, i.e. the data are continuous in time, and the assimilation system provides information on every model grid point independently of whether observations are available nearby or not. The OCEAN5 reanalysis system uses the Nucleus for European Modelling of the Ocean (NEMO) ocean model and the NEMOVAR ocean assimilation system. NEMOVAR uses the so-called 3D-Var FGAT (First Guess at Appropriate Time) assimilation technique, which assimilates sub-surface temperature, salinity, sea-ice concentration and sea-level anomalies. The ORAS5 data is forced by either global atmospheric reanalysis (for the consolidated product) or the ECMWF/IFS operational analysis (for the operational product) and is also constrained by observational data of sea surface temperature, sea surface salinity, sea-ice concentration, global-mean-sea-level trends and climatological variations of the ocean mass. The consolidated product (referred to as "Consolidated" in the download form) uses reanalysis atmospheric forcing (ERA-40 until 1978 and ERA-Interim from 1979 to 2014) and re-processed observations. The near real-time (referred to as "Operational" in the download form) ORAS5 product is available from 2015 onwards and is updated on a monthly basis 15 days behind real time. It uses ECMWF operational atmospheric forcing and near real time observations. The consolidated data benefits from atmospheric forcing consistency. The operational data benefits from near real-time latency. ORAS5 data are also available at the Copernicus Marine Environment Monitoring Service (CMEMS) and at the Integrated Climate Data Centre (ICDC), Hamburg University. The present dataset, at the time of publication, provides more variables than the others and has regular updates with near real-time data. For the period from 2015 to the present, the operational ORAS5 data provided in the CDS is different from the dataset provided by CMEMS, because different atmospheric forcings and ocean observation data were used in the generation of the two products. The ORAS5 dataset is produced by ECMWF and funded by the Copernicus Climate Change Service (C3S). DATA DESCRIPTION Data type Gridded Projection Tripolar model grid Horizontal coverage Global Horizontal resolution Approximately 0.25° x 0.25° (around 25 km in the tropics and 9 km in the Arctic) Vertical coverage Depends on the variable: Single level: two-dimensional variables (2D) All levels: three-dimensional variables (3D) from 0m (sea level) to approximately 5500m depth Vertical resolution Depends on the variable: Single level: 2D variables All levels: 75 ocean model levels for 3D variables Temporal coverage From January 1958 to present without gaps Consolidated product: 1958 to 2014 Operational product: 2015 to present Temporal resolution Monthly File format NetCDF4 Versions v0.1 Update frequency Depends on the product: Consolidated product: Possible future extension in time Operational product: New near real-time data added monthly on the 15th of the month DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Tripolar model grid Projection Tripolar model grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution Approximately 0.25° x 0.25° (around 25 km in the tropics and 9 km in the Arctic) Horizontal resolution Approximately 0.25° x 0.25° (around 25 km in the tropics and 9 km in the Arctic) Vertical coverage Depends on the variable: Single level: two-dimensional variables (2D) All levels: three-dimensional variables (3D) from 0m (sea level) to approximately 5500m depth Vertical coverage Depends on the variable: Single level: two-dimensional variables (2D) All levels: three-dimensional variables (3D) from 0m (sea level) to approximately 5500m depth Depends on the variable: Single level: two-dimensional variables (2D) All levels: three-dimensional variables (3D) from 0m (sea level) to approximately 5500m depth Vertical resolution Depends on the variable: Single level: 2D variables All levels: 75 ocean model levels for 3D variables Vertical resolution Depends on the variable: Single level: 2D variables All levels: 75 ocean model levels for 3D variables Depends on the variable: Single level: 2D variables All levels: 75 ocean model levels for 3D variables Temporal coverage From January 1958 to present without gaps Consolidated product: 1958 to 2014 Operational product: 2015 to present Temporal coverage From January 1958 to present without gaps Consolidated product: 1958 to 2014 Operational product: 2015 to present From January 1958 to present without gaps Consolidated product: 1958 to 2014 Operational product: 2015 to present Temporal resolution Monthly Temporal resolution Monthly File format NetCDF4 File format NetCDF4 Versions v0.1 Versions v0.1 Update frequency Depends on the product: Consolidated product: Possible future extension in time Operational product: New near real-time data added monthly on the 15th of the month Update frequency Depends on the product: Consolidated product: Possible future extension in time Operational product: New near real-time data added monthly on the 15th of the month Depends on the product: Consolidated product: Possible future extension in time Operational product: New near real-time data added monthly on the 15th of the month MAIN VARIABLES Name Units Description Depth of 14°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 14°C. This variable is a 2D field. Depth of 17°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 17°C. This variable is a 2D field. Depth of 20°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 20°C. This variable is a 2D field. Depth of 26°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 26°C. This variable is a 2D field. Depth of 28°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 28°C. This variable is a 2D field. Meridional velocity m s-1 Horizontal velocity of a water parcel in the direction of y axis of the ocean model. The horizontal velocity is a vector quantity, which can be decomposed into zonal (along the model x axis) and meridional (along the model y axis) components. This variable is a 3D field. Meridional wind stress N m-2 Horizontal shear stress exerted by the sea surface wind in the direction of the y axis of the ocean model. This variable is a 2D field. Mixed layer depth 0.01 m The depth of the ocean where the average sea water density exceeds the near surface density plus 0.01 kg/m3. This variable is a 2D field. Mixed layer depth 0.03 m The depth of the ocean where the average sea water density exceeds the near surface density plus 0.03 kg/m3. This variable is a 2D field. Net downward heat flux W m-2 Surface downward energy flux into the ocean. It includes both solar and non-solar heat fluxes, and is positive (negative) if it points downwards (upwards) in the ocean. This variable is a 2D field. Net upward water flux Kg m-2 s-1 Surface upward ocean water flux. It includes net precipitation and ice melt water, river input and surface damping fluxes. It is positive (negative) if it points upwards (downwards) to (from) the surface. This variable is a 2D field. Ocean heat content for the total water column J m-2 Energy absorbed by the ocean as computed by the integral product of temperature, sea water density and specific heat capacity from the surface down to bottom of the ocean. This variable is a 2D field. Ocean heat content for the upper 300m J m-2 Energy absorbed by the ocean as computed by the integral product of temperature, sea water density and specific heat capacity from the surface down to 300m depth. This variable is a 2D field. Ocean heat content for the upper 700m J m-2 Energy absorbed by the ocean as computed by the integral product of temperature, sea water density and specific heat capacity from the surface down to 700m depth. This variable is a 2D field. Potential temperature °C The temperature of a parcel of sea water would have if moved adiabatically to sea level pressure. This variable is a 3D field. Rotated meridional velocity m s-1 Northward horizontal surface velocity of a water parcel, as rotated from the model grid to latitude, longitude grid. The horizontal velocity is a vector quantity, which can be decomposed into Northward (meridional) and Eastward (zonal) components. This variable is a 3D field. Rotated zonal velocity m s-1 Eastward horizontal surface velocity of a water parcel, as rotated from the native model grid to latitude, longitude grid. The horizontal velocity is a vector quantity, which can be decomposed into Northward (meridional) and Eastward (zonal) components. This variable is a 3D field. Salinity PSU The salt content of sea water as measured on the practical salinity units (PSU) scale. This variable is a 3D field. Sea ice concentration Dimensionless Fraction of the area of the grid cell containing sea ice. This variable is a 2D field. Sea ice meridional velocity m s-1 Horizontal velocity of the sea ice in the direction of the y axis of the ocean model. The horizontal velocity is a vector quantity, which can be decomposed into zonal (along the model x axis) and meridional (along the model y axis) components. This variable is a 2D field. Sea ice thickness m Mean thickness of the sea ice layer in the area of the grid cell covered by ice. This variable is a 2D field. Sea ice zonal velocity m s-1 Horizontal velocity of the sea ice in the direction of the x axis of the ocean model. The horizontal velocity is a vector quantity, which can be decomposed into zonal (along the model x axis) and meridional (along the model y axis) components. This variable is a 2D field. Sea surface height m Vertical distance between the actual sea surface and a reference surface of constant geopotential with which mean sea level would coincide if the ocean were at rest. This variable is a 2D field. Sea surface salinity PSU Salt concentration close to the ocean surface. This variable is a 2D field. Sea surface temperature °C Water temperature close to the ocean surface. This variable is a 2D field. Zonal velocity m s-1 Horizontal velocity of a water parcel in the direction of x axis of the ocean model. The horizontal velocity is a vector quantity, which can be decomposed into zonal (along the model x axis) and meridional (along the model y axis) components. This variable is a 3D field. Zonal wind stress N m-2 Horizontal shear stress exerted by the sea surface wind in the direction of the x axis of the ocean model. This variable is a 2D field. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Depth of 14°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 14°C. This variable is a 2D field. Depth of 14°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 14°C. This variable is a 2D field. Depth of 17°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 17°C. This variable is a 2D field. Depth of 17°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 17°C. This variable is a 2D field. Depth of 20°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 20°C. This variable is a 2D field. Depth of 20°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 20°C. This variable is a 2D field. Depth of 26°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 26°C. This variable is a 2D field. Depth of 26°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 26°C. This variable is a 2D field. Depth of 28°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 28°C. This variable is a 2D field. Depth of 28°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 28°C. This variable is a 2D field. Meridional velocity m s-1 Horizontal velocity of a water parcel in the direction of y axis of the ocean model. The horizontal velocity is a vector quantity, which can be decomposed into zonal (along the model x axis) and meridional (along the model y axis) components. This variable is a 3D field. Meridional velocity m s-1 Horizontal velocity of a water parcel in the direction of y axis of the ocean model. The horizontal velocity is a vector quantity, which can be decomposed into zonal (along the model x axis) and meridional (along the model y axis) components. This variable is a 3D field. Meridional wind stress N m-2 Horizontal shear stress exerted by the sea surface wind in the direction of the y axis of the ocean model. This variable is a 2D field. Meridional wind stress N m-2 Horizontal shear stress exerted by the sea surface wind in the direction of the y axis of the ocean model. This variable is a 2D field. Mixed layer depth 0.01 m The depth of the ocean where the average sea water density exceeds the near surface density plus 0.01 kg/m3. This variable is a 2D field. Mixed layer depth 0.01 m The depth of the ocean where the average sea water density exceeds the near surface density plus 0.01 kg/m3. This variable is a 2D field. Mixed layer depth 0.03 m The depth of the ocean where the average sea water density exceeds the near surface density plus 0.03 kg/m3. This variable is a 2D field. Mixed layer depth 0.03 m The depth of the ocean where the average sea water density exceeds the near surface density plus 0.03 kg/m3. This variable is a 2D field. Net downward heat flux W m-2 Surface downward energy flux into the ocean. It includes both solar and non-solar heat fluxes, and is positive (negative) if it points downwards (upwards) in the ocean. This variable is a 2D field. Net downward heat flux W m-2 Surface downward energy flux into the ocean. It includes both solar and non-solar heat fluxes, and is positive (negative) if it points downwards (upwards) in the ocean. This variable is a 2D field. Net upward water flux Kg m-2 s-1 Surface upward ocean water flux. It includes net precipitation and ice melt water, river input and surface damping fluxes. It is positive (negative) if it points upwards (downwards) to (from) the surface. This variable is a 2D field. Net upward water flux Kg m-2 s-1 Surface upward ocean water flux. It includes net precipitation and ice melt water, river input and surface damping fluxes. It is positive (negative) if it points upwards (downwards) to (from) the surface. This variable is a 2D field. Ocean heat content for the total water column J m-2 Energy absorbed by the ocean as computed by the integral product of temperature, sea water density and specific heat capacity from the surface down to bottom of the ocean. This variable is a 2D field. Ocean heat content for the total water column J m-2 Energy absorbed by the ocean as computed by the integral product of temperature, sea water density and specific heat capacity from the surface down to bottom of the ocean. This variable is a 2D field. Ocean heat content for the upper 300m J m-2 Energy absorbed by the ocean as computed by the integral product of temperature, sea water density and specific heat capacity from the surface down to 300m depth. This variable is a 2D field. Ocean heat content for the upper 300m J m-2 Energy absorbed by the ocean as computed by the integral product of temperature, sea water density and specific heat capacity from the surface down to 300m depth. This variable is a 2D field. Ocean heat content for the upper 700m J m-2 Energy absorbed by the ocean as computed by the integral product of temperature, sea water density and specific heat capacity from the surface down to 700m depth. This variable is a 2D field. Ocean heat content for the upper 700m J m-2 Energy absorbed by the ocean as computed by the integral product of temperature, sea water density and specific heat capacity from the surface down to 700m depth. This variable is a 2D field. Potential temperature °C The temperature of a parcel of sea water would have if moved adiabatically to sea level pressure. This variable is a 3D field. Potential temperature °C The temperature of a parcel of sea water would have if moved adiabatically to sea level pressure. This variable is a 3D field. Rotated meridional velocity m s-1 Northward horizontal surface velocity of a water parcel, as rotated from the model grid to latitude, longitude grid. The horizontal velocity is a vector quantity, which can be decomposed into Northward (meridional) and Eastward (zonal) components. This variable is a 3D field. Rotated meridional velocity m s-1 Northward horizontal surface velocity of a water parcel, as rotated from the model grid to latitude, longitude grid. The horizontal velocity is a vector quantity, which can be decomposed into Northward (meridional) and Eastward (zonal) components. This variable is a 3D field. Rotated zonal velocity m s-1 Eastward horizontal surface velocity of a water parcel, as rotated from the native model grid to latitude, longitude grid. The horizontal velocity is a vector quantity, which can be decomposed into Northward (meridional) and Eastward (zonal) components. This variable is a 3D field. Rotated zonal velocity m s-1 Eastward horizontal surface velocity of a water parcel, as rotated from the native model grid to latitude, longitude grid. The horizontal velocity is a vector quantity, which can be decomposed into Northward (meridional) and Eastward (zonal) components. This variable is a 3D field. Salinity PSU The salt content of sea water as measured on the practical salinity units (PSU) scale. This variable is a 3D field. Salinity PSU The salt content of sea water as measured on the practical salinity units (PSU) scale. This variable is a 3D field. Sea ice concentration Dimensionless Fraction of the area of the grid cell containing sea ice. This variable is a 2D field. Sea ice concentration Dimensionless Fraction of the area of the grid cell containing sea ice. This variable is a 2D field. Sea ice meridional velocity m s-1 Horizontal velocity of the sea ice in the direction of the y axis of the ocean model. The horizontal velocity is a vector quantity, which can be decomposed into zonal (along the model x axis) and meridional (along the model y axis) components. This variable is a 2D field. Sea ice meridional velocity m s-1 Horizontal velocity of the sea ice in the direction of the y axis of the ocean model. The horizontal velocity is a vector quantity, which can be decomposed into zonal (along the model x axis) and meridional (along the model y axis) components. This variable is a 2D field. Sea ice thickness m Mean thickness of the sea ice layer in the area of the grid cell covered by ice. This variable is a 2D field. Sea ice thickness m Mean thickness of the sea ice layer in the area of the grid cell covered by ice. This variable is a 2D field. Sea ice zonal velocity m s-1 Horizontal velocity of the sea ice in the direction of the x axis of the ocean model. The horizontal velocity is a vector quantity, which can be decomposed into zonal (along the model x axis) and meridional (along the model y axis) components. This variable is a 2D field. Sea ice zonal velocity m s-1 Horizontal velocity of the sea ice in the direction of the x axis of the ocean model. The horizontal velocity is a vector quantity, which can be decomposed into zonal (along the model x axis) and meridional (along the model y axis) components. This variable is a 2D field. Sea surface height m Vertical distance between the actual sea surface and a reference surface of constant geopotential with which mean sea level would coincide if the ocean were at rest. This variable is a 2D field. Sea surface height m Vertical distance between the actual sea surface and a reference surface of constant geopotential with which mean sea level would coincide if the ocean were at rest. This variable is a 2D field. Sea surface salinity PSU Salt concentration close to the ocean surface. This variable is a 2D field. Sea surface salinity PSU Salt concentration close to the ocean surface. This variable is a 2D field. Sea surface temperature °C Water temperature close to the ocean surface. This variable is a 2D field. Sea surface temperature °C Water temperature close to the ocean surface. This variable is a 2D field. Zonal velocity m s-1 Horizontal velocity of a water parcel in the direction of x axis of the ocean model. The horizontal velocity is a vector quantity, which can be decomposed into zonal (along the model x axis) and meridional (along the model y axis) components. This variable is a 3D field. Zonal velocity m s-1 Horizontal velocity of a water parcel in the direction of x axis of the ocean model. The horizontal velocity is a vector quantity, which can be decomposed into zonal (along the model x axis) and meridional (along the model y axis) components. This variable is a 3D field. Zonal wind stress N m-2 Horizontal shear stress exerted by the sea surface wind in the direction of the x axis of the ocean model. This variable is a 2D field. Zonal wind stress N m-2 Horizontal shear stress exerted by the sea surface wind in the direction of the x axis of the ocean model. This variable is a 2D field. 270 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-ocean-colour-plankton-and-transparency http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_MED_BGC_L4_NRT_009_142 Mediterranean Sea Ocean Colour Plankton and Transparency L4 NRT daily and monthly observations Short description: For the Mediterranean Sea Ocean Satellite Observations, the Italian National Research Council (CNR – Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets: * ''plankton'' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated gap-free Chl concentration (to provide a ""cloud free"" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018) * ''transparency'' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for ""multi"" observations achieved via region-specific algorithm, Volpe et al., 2019) Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the ""multi"" products, and OLCI-S3A & S3B for the ""olci"" products Temporal resolutions: monthly and daily (for ""gap-free"" data) Spatial resolutions: 1 km for ""multi"" and 300 meters for ""olci"" To find this product in the catalogue, use the search keyword ""OCEANCOLOUR_MED_BGC_L4_NRT"". DOI (product) :https://doi.org/10.48670/moi-00298 https://doi.org/10.48670/moi-00298 271 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-cerra-single-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-cerra-single-levels reanalysis-cerra-single-levels The Copernicus European Regional ReAnalysis (CERRA) datasets provide spatially and temporally consistent historical reconstructions of meteorological variables in the atmosphere and at the surface. There are four subsets: single levels (atmospheric and surface quantities), height levels (upper-air fields up to 500m), pressure levels (upper-air fields up to 1hPa) and model levels (native levels of the model). This entry provides reanalysis and forecast data on single levels for Europe from 1984 to present. Several atmospheric parameters are common to both reanalysis and forecast (e.g. temperature, wind), whilst others are produced only by the forecast model (e.g. 10m wind gust, radiative fluxes). Reanalysis combines model data with observations into a complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved, reprocessed, versions of the original observations, which all benefit the quality of the reanalysis product. The CERRA dataset was produced using the HARMONIE-ALADIN limited-area numerical weather prediction and data assimilation system, hereafter referred to as the CERRA system. The CERRA system employs a 3-dimensional variational data assimilation scheme of the atmospheric state at every assimilation time. The reanalysis dataset is convenient owing to its provision of atmospheric estimates at each model domain grid point over Europe for each regular output time, over a long period, and always using the same data format. The inputs to CERRA reanalysis are the observational data, lateral boundary conditions from ERA5 global reanalysis as prior estimates of the atmospheric state and physiographic datasets describing the surface characteristics of the model. The observing system has evolved over time, and although the data assimilation system can resolve data holes, the much sparser observational networks in the past periods (for example a reduced amount of satellite data in the 1980s) can impact the quality of analyses leading to less accurate estimates. The uncertainty estimates for reanalysis variables are provided by the CERRA-EDA, a 10-member ensemble of data assimilation system. The added value of the CERRA data with respect to the global reanalysis products is expected to come, for example, with the higher horizontal resolution that permits the usage of a better description of the model topography and physiographic data, and the assimilation of more surface observations. More information about the CERRA dataset can be found in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Lambert conformal conical grid Horizontal coverage Europe. The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Horizontal resolution 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members Vertical coverage From below the surface to the top of the atmosphere Vertical resolution Single level Temporal coverage September 1984 - June 2021 Temporal resolution Analysis data: 3-hourly for high-resolution, 6-hourly for ensemble members Forecast data: hourly for forecast range 1 - 6 (high-resolution and ensemble members), 3-hourly for forecast range 6 - 30 (high-resolution only) File format GRIB2 Update frequency New data will be added towards the end of 2023 DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert conformal conical grid Projection Lambert conformal conical grid Horizontal coverage Europe. The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Horizontal coverage Europe. The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Horizontal resolution 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members Horizontal resolution 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members Vertical coverage From below the surface to the top of the atmosphere Vertical coverage From below the surface to the top of the atmosphere Vertical resolution Single level Vertical resolution Single level Temporal coverage September 1984 - June 2021 Temporal coverage September 1984 - June 2021 Temporal resolution Analysis data: 3-hourly for high-resolution, 6-hourly for ensemble members Forecast data: hourly for forecast range 1 - 6 (high-resolution and ensemble members), 3-hourly for forecast range 6 - 30 (high-resolution only) Temporal resolution Analysis data: 3-hourly for high-resolution, 6-hourly for ensemble members Forecast data: hourly for forecast range 1 - 6 (high-resolution and ensemble members), 3-hourly for forecast range 6 - 30 (high-resolution only) Analysis data: 3-hourly for high-resolution, 6-hourly for ensemble members Forecast data: hourly for forecast range 1 - 6 (high-resolution and ensemble members), 3-hourly for forecast range 6 - 30 (high-resolution only) File format GRIB2 File format GRIB2 Update frequency New data will be added towards the end of 2023 Update frequency New data will be added towards the end of 2023 MAIN VARIABLES Name Units Description 10m wind direction degree The 10m wind direction is the wind direction valid for the grid area determined for a height of 10m above the surface. The parameter is given in degrees ranging from 0-360. Here, 0° means a northerly wind and 90° indicates an easterly wind. Given values are instantaneous. 10m wind gust since previous post-processing m s-1 The 10 metre wind gust speed is the maximum wind speed since the last post-processing at the grid area. It is determined for a height of 10m above the surface. The value is the maximum since the previous post-processing. For instance, for the first saved time step at forecast 1h it is the maximum wind speed, which occurred within the first hour of the forecast. For the second saved time step at forecast 2h, it is the maximum wind speed which happened in the second forecast hour, hence between fc1 and fc2. 10m wind speed m s-1 The 10m wind speed is the wind speed valid for the grid area determined for a height of 10m above the surface. It is computed from both the zonal (u) and the meridional (v) wind components by sqrt( u2 + v2 ). Given values are instantaneous. 2m relative humidity % The 2m relative humidity is the modelled humidity valid for the grid area determined for a height of 2m above the surface. The parameter is the relation between actual humidity and saturation humidity given in %. Values are in the interval [0,100]. 0% means that the air is totally dry whereas 100% indicates that the air is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. Given values are instantaneous. 2m temperature K The 2m temperature is the model temperature valid for the grid area determined for a height of 2m above the surface. Given values are instantaneous. Albedo % The albedo is the amount of radiation which is reflected for the given grid area. It is determined for the surface to the atmosphere, both for ground and water surfaces. Small values mean that large amounts of the radiation are absorbed whereas large values mean that more radiation is reflected. Given values are instantaneous. Evaporation kg m-2 Evaporation is the amount of moisture flux from the surface (ground and water) into the atmosphere. It is given as a mean for the grid area. The mean is a weighted average over all tile types present in the grid point. By model convention downward fluxes are positive. Hence, evaporation is represented by negative values and positive values represent condensation. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). High cloud cover % The high cloud cover is the percentage of sky covert with clouds in high altitude. It is valid for the grid area and high refers to a height above 5000m. Given values are instantaneous. Land-sea mask Dimensionless The land-sea mask is a field that contains, for every grid point, the proportion of land in the grid box. The parameter is dimensionless and the values are between 0 (sea) and 1 (land). The land-sea mask is constant in time and the field is available only at 00, 03, 06, 09, 12, 15, 18, and 21 UTC. Liquid volumetric soil moisture (non-frozen) m3 m-3 The liquid volumetric soil water is the amount of non-frozen water in a cubic metre soil valid for the grid area in the corresponding soil layer. The parameter is available for analysis and forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The soil model has three layers but only data for the top layer, closest to the surface, are provided. Deeper layers are affected by spin-up effects at the seams of the production streams. Users interested in soil parameters are recommended to use CERRA-Land data. Low cloud cover % The low cloud cover is the percentage of sky covert with clouds in low altitude. It is valid for the grid area and low altitude refers to heights below 2500m. Given values are instantaneous. Maximum 2m temperature since previous post-processing K It is the maximum air temperature at the height of 2 m above the surface since the last post-processing. The maximum 2m temperature is only available for the forecast time steps. The value is the maximum since the previous post-processing. For instance, for the first saved time step at forecast 1h it is the maximum surface air temperature, which occurred within the first hour of the forecast. For the second saved time step at forecast 2h, it is the maximum surface air temperature which happened in the second forecast hour, hence between fc1 and fc2. For longer forecasts, the output frequency is reduced. Hence, the maximum over a longer time period is saved. For instance, for the 15h forecast the maximum surface air temperature is identified within the period 12h-15h since the last post-processing happened at 12h (12 hours after the onset of the forecast). Mean sea level pressure Pa The mean sea level pressure is the air pressure reduced to mean sea level valid for the grid area. Given values are instantaneous. Medium cloud cover % The medium cloud cover is the percentage of sky covert with clouds in medium altitude. It is valid for the grid area and medium altitude refers to heights between 2500m through 5000m. Given values are instantaneous. Minimum 2m temperature since previous post-processing K It is the minimum air temperature at the height of 2 m above the surface since the last post-processing. The minimum 2m temperature is only available for the forecast time steps. The value is the minimum since the previous post-processing. For instance, for the first saved time step at forecast 1h it is the minimum surface air temperature, which occurred within the first hour of the forecast. For the second saved time step at forecast 2h, it is the minimum surface air temperature which happened in the second forecast hour, hence between fc1 and fc2. For longer forecasts, the output frequency is reduced. Hence, the minimum over a longer time period is saved. For instance, for the 15h forecast the minimum surface air temperature is identified within the period 12h-15h since the last post-processing happened at 12-h (12 hours after the onset of the forecast). Momentum flux at the surface U-component N m-2 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. Here, the parameter is the sum of all surface stress components, in an eastward direction. Momentum flux components are associated to orographic gravity waves, the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. For instance, the turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. Positive (negative) values denote stress in the eastward (westward) direction. Momentum flux at the surface V-component N m-2 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. Here, the parameter is the sum of all surface stress components, in a northward direction. Momentum flux components are associated to orographic gravity waves, the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. For instance, the turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. Positive (negative) values denote stress in the northward (southward) direction. Orography m The orography is the height of the terrain with respect to the model defined globe. Each grid point has one value representing the mean over the grid point domain. The orography is given as geopotential height in metres. The orography is constant in time and the field is available only at 00, 03, 06, 09, 12, 15, 18, and 21 UTC. Skin temperature K The skin temperature is the model temperature valid for the grid area determined for the boundary surface to the atmosphere, both ground and water surfaces. Given values are instantaneous. Snow density kg m-3 Snow density is the snow mass per unit of volume. It is given as the mean for the grid area. Grid points without snow have missing values. Given values are instantaneous. Snow depth m Snow depth is the average snow height for the grid area. Given values are instantaneous. Snow depth water equivalent kg m-2 Snow depth water equivalent expresses the snow depth in kg of snow over one square metre. The unit corresponds to 1 mm of water equivalent. It is given as the mean for the grid area. Given values are instantaneous. Snowfall water equivalent kg m-2 Snowfall water equivalent expresses the snowfall in kg of snow over one square metre. The unit corresponds to 1 mm of water equivalent. It is given as the mean for the grid area. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Soil temperature K The soil temperature is the model temperature valid for the grid area at the corresponding soil layer. The parameter is available for analysis and forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The soil model has three layers but only data for the top layer, closest to the surface, are provided. Deeper layers are affected by spin-up effects at the seams of the production streams. Users interested in soil parameters are recommended to use CERRA-Land data. Surface latent heat flux J m-2 The surface latent heat flux is the exchange of latent heat (due to phase transitions such as evaporation and condensation) with the surface (ground and water) through turbulent diffusion. It is given as a mean for the grid area. By model convention downward fluxes are positive. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface net solar radiation J m-2 The surface net solar radiation is the amount of solar (short-wave) radiation that is absorbed at the surface (ground and water). It is computed as Surface net solar radiation = surface solar radiation downwards * (1 albedo). It is given as a mean for the grid area. By model convention downward fluxes are positive. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface net solar radiation, clear sky J m-2 This parameter is the amount of short-wave radiation from the sun (also known as solar or direct radiation), which would be absorbed at the surface of the Earth assuming clear-sky (cloudless) conditions. It is computed as Clear-sky net short-wave radiation = Clear-sky short-wave radiation downwards * (1 albedo). It is given as a mean for the grid area. By model convention downward fluxes are positive. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface net thermal radiation J m-2 The surface net thermal radiation is the difference between thermal (long-wave) downward and upward radiation at the surface (ground and water) of the Earth. Thermal radiation is emitted by the atmosphere, clouds and the surface of the Earth. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). The model convention for vertical fluxes is positive downwards. Surface net thermal radiation, clear sky J m-2 The long-wave radiation (also known as thermal or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. The clear-sky net long-wave radiation is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface pressure Pa The surface pressure is the air pressure at the surface (ground and water) valid for the grid area. Given values are instantaneous. Surface roughness m The surface roughness describes the aerodynamic roughness length (over land). Each grid point has one value representing the mean over the grid point. The effective surface roughness is depending on the orographic component (constant part), the snow depth, the evolution of the Leaf Area Index and the fraction of vegetation, which is different for each month. Given values are instantaneous. Surface sensible heat flux J m-2 The surface sensible heat flux is the exchange of heat (no phase transition) with the surface (ground and water) through turbulent diffusion. It is given as a mean for the grid. By model convention downward fluxes are positive. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface solar radiation downwards J m-2 The surface solar radiation downward is the amount of solar (short-wave) radiation reaching the surface (ground and water). It is given as a mean for the grid area. By model convention downward fluxes are positive. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface thermal radiation downwards J m-2 The surface net thermal radiation is the difference between thermal (long-wave) downward and upward radiation at the surface (ground and water) of the Earth. Thermal radiation is emitted by the atmosphere, clouds and the surface of the Earth. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). The model convention for vertical fluxes is positive downwards. Time-integrated surface direct short-wave radiation J m-2 The surface direct short-wave radiation is the amount of direct solar radiation reaching the surface (ground and water). It is given as a mean for the grid area. By model convention downward fluxes are positive. Surface direct solar radiation is only available for forecast time steps. It is an accumulated (time-integrated) parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated radiation over 24 hours. Total cloud cover % Total cloud cover is the percentage of sky covert with clouds. It is valid for the grid area and clouds at any height above the surface are considered. Given values are instantaneous. Total column integrated water vapour kg m-2 The total column integrated water vapour is the vertically integrated water vapour (precipitable water content) valid for the grid area. It is vertically integrated from the surface to the top of the atmosphere. Given values are instantaneous. Total precipitation kg m-2 Total precipitation is the amount of precipitation falling onto the ground/water surface. It includes all kind of precipitation forms such as convective precipitation, large scale precipitation, liquid and solid precipitation. The amount is valid for the grid area. The total precipitation is available only for the forecast time steps. It is an accumulated parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24h-forecast has the accumulated precipitation over 24 hours. Volumetric soil moisture m3 m-3 The volumetric soil moisture is the sum of the liquid and frozen water in a cubic metre soil valid for the grid area in the corresponding soil layer. The parameter is available for analysis and forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The soil model has three layers but only data for the top layer, closest to the surface, are provided. Deeper layers are affected by spin-up effects at the seams of the production streams. Users interested in soil parameters are recommended to use CERRA-Land data. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 10m wind direction degree The 10m wind direction is the wind direction valid for the grid area determined for a height of 10m above the surface. The parameter is given in degrees ranging from 0-360. Here, 0° means a northerly wind and 90° indicates an easterly wind. Given values are instantaneous. 10m wind direction degree The 10m wind direction is the wind direction valid for the grid area determined for a height of 10m above the surface. The parameter is given in degrees ranging from 0-360. Here, 0° means a northerly wind and 90° indicates an easterly wind. Given values are instantaneous. 10m wind gust since previous post-processing m s-1 The 10 metre wind gust speed is the maximum wind speed since the last post-processing at the grid area. It is determined for a height of 10m above the surface. The value is the maximum since the previous post-processing. For instance, for the first saved time step at forecast 1h it is the maximum wind speed, which occurred within the first hour of the forecast. For the second saved time step at forecast 2h, it is the maximum wind speed which happened in the second forecast hour, hence between fc1 and fc2. 10m wind gust since previous post-processing m s-1 The 10 metre wind gust speed is the maximum wind speed since the last post-processing at the grid area. It is determined for a height of 10m above the surface. The value is the maximum since the previous post-processing. For instance, for the first saved time step at forecast 1h it is the maximum wind speed, which occurred within the first hour of the forecast. For the second saved time step at forecast 2h, it is the maximum wind speed which happened in the second forecast hour, hence between fc1 and fc2. 10m wind speed m s-1 The 10m wind speed is the wind speed valid for the grid area determined for a height of 10m above the surface. It is computed from both the zonal (u) and the meridional (v) wind components by sqrt( u2 + v2 ). Given values are instantaneous. 10m wind speed m s-1 The 10m wind speed is the wind speed valid for the grid area determined for a height of 10m above the surface. It is computed from both the zonal (u) and the meridional (v) wind components by sqrt( u2 + v2 ). Given values are instantaneous. 2m relative humidity % The 2m relative humidity is the modelled humidity valid for the grid area determined for a height of 2m above the surface. The parameter is the relation between actual humidity and saturation humidity given in %. Values are in the interval [0,100]. 0% means that the air is totally dry whereas 100% indicates that the air is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. Given values are instantaneous. 2m relative humidity % The 2m relative humidity is the modelled humidity valid for the grid area determined for a height of 2m above the surface. The parameter is the relation between actual humidity and saturation humidity given in %. Values are in the interval [0,100]. 0% means that the air is totally dry whereas 100% indicates that the air is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. Given values are instantaneous. 2m temperature K The 2m temperature is the model temperature valid for the grid area determined for a height of 2m above the surface. Given values are instantaneous. 2m temperature K The 2m temperature is the model temperature valid for the grid area determined for a height of 2m above the surface. Given values are instantaneous. Albedo % The albedo is the amount of radiation which is reflected for the given grid area. It is determined for the surface to the atmosphere, both for ground and water surfaces. Small values mean that large amounts of the radiation are absorbed whereas large values mean that more radiation is reflected. Given values are instantaneous. Albedo % The albedo is the amount of radiation which is reflected for the given grid area. It is determined for the surface to the atmosphere, both for ground and water surfaces. Small values mean that large amounts of the radiation are absorbed whereas large values mean that more radiation is reflected. Given values are instantaneous. Evaporation kg m-2 Evaporation is the amount of moisture flux from the surface (ground and water) into the atmosphere. It is given as a mean for the grid area. The mean is a weighted average over all tile types present in the grid point. By model convention downward fluxes are positive. Hence, evaporation is represented by negative values and positive values represent condensation. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Evaporation kg m-2 Evaporation is the amount of moisture flux from the surface (ground and water) into the atmosphere. It is given as a mean for the grid area. The mean is a weighted average over all tile types present in the grid point. By model convention downward fluxes are positive. Hence, evaporation is represented by negative values and positive values represent condensation. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). High cloud cover % The high cloud cover is the percentage of sky covert with clouds in high altitude. It is valid for the grid area and high refers to a height above 5000m. Given values are instantaneous. High cloud cover % The high cloud cover is the percentage of sky covert with clouds in high altitude. It is valid for the grid area and high refers to a height above 5000m. Given values are instantaneous. Land-sea mask Dimensionless The land-sea mask is a field that contains, for every grid point, the proportion of land in the grid box. The parameter is dimensionless and the values are between 0 (sea) and 1 (land). The land-sea mask is constant in time and the field is available only at 00, 03, 06, 09, 12, 15, 18, and 21 UTC. Land-sea mask Dimensionless The land-sea mask is a field that contains, for every grid point, the proportion of land in the grid box. The parameter is dimensionless and the values are between 0 (sea) and 1 (land). The land-sea mask is constant in time and the field is available only at 00, 03, 06, 09, 12, 15, 18, and 21 UTC. Liquid volumetric soil moisture (non-frozen) m3 m-3 The liquid volumetric soil water is the amount of non-frozen water in a cubic metre soil valid for the grid area in the corresponding soil layer. The parameter is available for analysis and forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The soil model has three layers but only data for the top layer, closest to the surface, are provided. Deeper layers are affected by spin-up effects at the seams of the production streams. Users interested in soil parameters are recommended to use CERRA-Land data. Liquid volumetric soil moisture (non-frozen) m3 m-3 The liquid volumetric soil water is the amount of non-frozen water in a cubic metre soil valid for the grid area in the corresponding soil layer. The parameter is available for analysis and forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The soil model has three layers but only data for the top layer, closest to the surface, are provided. Deeper layers are affected by spin-up effects at the seams of the production streams. Users interested in soil parameters are recommended to use CERRA-Land data. Low cloud cover % The low cloud cover is the percentage of sky covert with clouds in low altitude. It is valid for the grid area and low altitude refers to heights below 2500m. Given values are instantaneous. Low cloud cover % The low cloud cover is the percentage of sky covert with clouds in low altitude. It is valid for the grid area and low altitude refers to heights below 2500m. Given values are instantaneous. Maximum 2m temperature since previous post-processing K It is the maximum air temperature at the height of 2 m above the surface since the last post-processing. The maximum 2m temperature is only available for the forecast time steps. The value is the maximum since the previous post-processing. For instance, for the first saved time step at forecast 1h it is the maximum surface air temperature, which occurred within the first hour of the forecast. For the second saved time step at forecast 2h, it is the maximum surface air temperature which happened in the second forecast hour, hence between fc1 and fc2. For longer forecasts, the output frequency is reduced. Hence, the maximum over a longer time period is saved. For instance, for the 15h forecast the maximum surface air temperature is identified within the period 12h-15h since the last post-processing happened at 12h (12 hours after the onset of the forecast). Maximum 2m temperature since previous post-processing K It is the maximum air temperature at the height of 2 m above the surface since the last post-processing. The maximum 2m temperature is only available for the forecast time steps. The value is the maximum since the previous post-processing. For instance, for the first saved time step at forecast 1h it is the maximum surface air temperature, which occurred within the first hour of the forecast. For the second saved time step at forecast 2h, it is the maximum surface air temperature which happened in the second forecast hour, hence between fc1 and fc2. For longer forecasts, the output frequency is reduced. Hence, the maximum over a longer time period is saved. For instance, for the 15h forecast the maximum surface air temperature is identified within the period 12h-15h since the last post-processing happened at 12h (12 hours after the onset of the forecast). Mean sea level pressure Pa The mean sea level pressure is the air pressure reduced to mean sea level valid for the grid area. Given values are instantaneous. Mean sea level pressure Pa The mean sea level pressure is the air pressure reduced to mean sea level valid for the grid area. Given values are instantaneous. Medium cloud cover % The medium cloud cover is the percentage of sky covert with clouds in medium altitude. It is valid for the grid area and medium altitude refers to heights between 2500m through 5000m. Given values are instantaneous. Medium cloud cover % The medium cloud cover is the percentage of sky covert with clouds in medium altitude. It is valid for the grid area and medium altitude refers to heights between 2500m through 5000m. Given values are instantaneous. Minimum 2m temperature since previous post-processing K It is the minimum air temperature at the height of 2 m above the surface since the last post-processing. The minimum 2m temperature is only available for the forecast time steps. The value is the minimum since the previous post-processing. For instance, for the first saved time step at forecast 1h it is the minimum surface air temperature, which occurred within the first hour of the forecast. For the second saved time step at forecast 2h, it is the minimum surface air temperature which happened in the second forecast hour, hence between fc1 and fc2. For longer forecasts, the output frequency is reduced. Hence, the minimum over a longer time period is saved. For instance, for the 15h forecast the minimum surface air temperature is identified within the period 12h-15h since the last post-processing happened at 12-h (12 hours after the onset of the forecast). Minimum 2m temperature since previous post-processing K It is the minimum air temperature at the height of 2 m above the surface since the last post-processing. The minimum 2m temperature is only available for the forecast time steps. The value is the minimum since the previous post-processing. For instance, for the first saved time step at forecast 1h it is the minimum surface air temperature, which occurred within the first hour of the forecast. For the second saved time step at forecast 2h, it is the minimum surface air temperature which happened in the second forecast hour, hence between fc1 and fc2. For longer forecasts, the output frequency is reduced. Hence, the minimum over a longer time period is saved. For instance, for the 15h forecast the minimum surface air temperature is identified within the period 12h-15h since the last post-processing happened at 12-h (12 hours after the onset of the forecast). Momentum flux at the surface U-component N m-2 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. Here, the parameter is the sum of all surface stress components, in an eastward direction. Momentum flux components are associated to orographic gravity waves, the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. For instance, the turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. Positive (negative) values denote stress in the eastward (westward) direction. Momentum flux at the surface U-component N m-2 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. Here, the parameter is the sum of all surface stress components, in an eastward direction. Momentum flux components are associated to orographic gravity waves, the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. For instance, the turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. Positive (negative) values denote stress in the eastward (westward) direction. Momentum flux at the surface V-component N m-2 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. Here, the parameter is the sum of all surface stress components, in a northward direction. Momentum flux components are associated to orographic gravity waves, the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. For instance, the turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. Positive (negative) values denote stress in the northward (southward) direction. Momentum flux at the surface V-component N m-2 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. Here, the parameter is the sum of all surface stress components, in a northward direction. Momentum flux components are associated to orographic gravity waves, the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. For instance, the turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. Positive (negative) values denote stress in the northward (southward) direction. Orography m The orography is the height of the terrain with respect to the model defined globe. Each grid point has one value representing the mean over the grid point domain. The orography is given as geopotential height in metres. The orography is constant in time and the field is available only at 00, 03, 06, 09, 12, 15, 18, and 21 UTC. Orography m The orography is the height of the terrain with respect to the model defined globe. Each grid point has one value representing the mean over the grid point domain. The orography is given as geopotential height in metres. The orography is constant in time and the field is available only at 00, 03, 06, 09, 12, 15, 18, and 21 UTC. Skin temperature K The skin temperature is the model temperature valid for the grid area determined for the boundary surface to the atmosphere, both ground and water surfaces. Given values are instantaneous. Skin temperature K The skin temperature is the model temperature valid for the grid area determined for the boundary surface to the atmosphere, both ground and water surfaces. Given values are instantaneous. Snow density kg m-3 Snow density is the snow mass per unit of volume. It is given as the mean for the grid area. Grid points without snow have missing values. Given values are instantaneous. Snow density kg m-3 Snow density is the snow mass per unit of volume. It is given as the mean for the grid area. Grid points without snow have missing values. Given values are instantaneous. Snow depth m Snow depth is the average snow height for the grid area. Given values are instantaneous. Snow depth m Snow depth is the average snow height for the grid area. Given values are instantaneous. Snow depth water equivalent kg m-2 Snow depth water equivalent expresses the snow depth in kg of snow over one square metre. The unit corresponds to 1 mm of water equivalent. It is given as the mean for the grid area. Given values are instantaneous. Snow depth water equivalent kg m-2 Snow depth water equivalent expresses the snow depth in kg of snow over one square metre. The unit corresponds to 1 mm of water equivalent. It is given as the mean for the grid area. Given values are instantaneous. Snowfall water equivalent kg m-2 Snowfall water equivalent expresses the snowfall in kg of snow over one square metre. The unit corresponds to 1 mm of water equivalent. It is given as the mean for the grid area. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Snowfall water equivalent kg m-2 Snowfall water equivalent expresses the snowfall in kg of snow over one square metre. The unit corresponds to 1 mm of water equivalent. It is given as the mean for the grid area. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Soil temperature K The soil temperature is the model temperature valid for the grid area at the corresponding soil layer. The parameter is available for analysis and forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The soil model has three layers but only data for the top layer, closest to the surface, are provided. Deeper layers are affected by spin-up effects at the seams of the production streams. Users interested in soil parameters are recommended to use CERRA-Land data. Soil temperature K The soil temperature is the model temperature valid for the grid area at the corresponding soil layer. The parameter is available for analysis and forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The soil model has three layers but only data for the top layer, closest to the surface, are provided. Deeper layers are affected by spin-up effects at the seams of the production streams. Users interested in soil parameters are recommended to use CERRA-Land data. Surface latent heat flux J m-2 The surface latent heat flux is the exchange of latent heat (due to phase transitions such as evaporation and condensation) with the surface (ground and water) through turbulent diffusion. It is given as a mean for the grid area. By model convention downward fluxes are positive. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface latent heat flux J m-2 The surface latent heat flux is the exchange of latent heat (due to phase transitions such as evaporation and condensation) with the surface (ground and water) through turbulent diffusion. It is given as a mean for the grid area. By model convention downward fluxes are positive. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface net solar radiation J m-2 The surface net solar radiation is the amount of solar (short-wave) radiation that is absorbed at the surface (ground and water). It is computed as Surface net solar radiation = surface solar radiation downwards * (1 albedo). It is given as a mean for the grid area. By model convention downward fluxes are positive. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface net solar radiation J m-2 The surface net solar radiation is the amount of solar (short-wave) radiation that is absorbed at the surface (ground and water). It is computed as Surface net solar radiation = surface solar radiation downwards * (1 albedo). It is given as a mean for the grid area. By model convention downward fluxes are positive. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface net solar radiation, clear sky J m-2 This parameter is the amount of short-wave radiation from the sun (also known as solar or direct radiation), which would be absorbed at the surface of the Earth assuming clear-sky (cloudless) conditions. It is computed as Clear-sky net short-wave radiation = Clear-sky short-wave radiation downwards * (1 albedo). It is given as a mean for the grid area. By model convention downward fluxes are positive. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface net solar radiation, clear sky J m-2 This parameter is the amount of short-wave radiation from the sun (also known as solar or direct radiation), which would be absorbed at the surface of the Earth assuming clear-sky (cloudless) conditions. It is computed as Clear-sky net short-wave radiation = Clear-sky short-wave radiation downwards * (1 albedo). It is given as a mean for the grid area. By model convention downward fluxes are positive. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface net thermal radiation J m-2 The surface net thermal radiation is the difference between thermal (long-wave) downward and upward radiation at the surface (ground and water) of the Earth. Thermal radiation is emitted by the atmosphere, clouds and the surface of the Earth. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). The model convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 The surface net thermal radiation is the difference between thermal (long-wave) downward and upward radiation at the surface (ground and water) of the Earth. Thermal radiation is emitted by the atmosphere, clouds and the surface of the Earth. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). The model convention for vertical fluxes is positive downwards. Surface net thermal radiation, clear sky J m-2 The long-wave radiation (also known as thermal or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. The clear-sky net long-wave radiation is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface net thermal radiation, clear sky J m-2 The long-wave radiation (also known as thermal or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. The clear-sky net long-wave radiation is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface pressure Pa The surface pressure is the air pressure at the surface (ground and water) valid for the grid area. Given values are instantaneous. Surface pressure Pa The surface pressure is the air pressure at the surface (ground and water) valid for the grid area. Given values are instantaneous. Surface roughness m The surface roughness describes the aerodynamic roughness length (over land). Each grid point has one value representing the mean over the grid point. The effective surface roughness is depending on the orographic component (constant part), the snow depth, the evolution of the Leaf Area Index and the fraction of vegetation, which is different for each month. Given values are instantaneous. Surface roughness m The surface roughness describes the aerodynamic roughness length (over land). Each grid point has one value representing the mean over the grid point. The effective surface roughness is depending on the orographic component (constant part), the snow depth, the evolution of the Leaf Area Index and the fraction of vegetation, which is different for each month. Given values are instantaneous. Surface sensible heat flux J m-2 The surface sensible heat flux is the exchange of heat (no phase transition) with the surface (ground and water) through turbulent diffusion. It is given as a mean for the grid. By model convention downward fluxes are positive. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface sensible heat flux J m-2 The surface sensible heat flux is the exchange of heat (no phase transition) with the surface (ground and water) through turbulent diffusion. It is given as a mean for the grid. By model convention downward fluxes are positive. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface solar radiation downwards J m-2 The surface solar radiation downward is the amount of solar (short-wave) radiation reaching the surface (ground and water). It is given as a mean for the grid area. By model convention downward fluxes are positive. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface solar radiation downwards J m-2 The surface solar radiation downward is the amount of solar (short-wave) radiation reaching the surface (ground and water). It is given as a mean for the grid area. By model convention downward fluxes are positive. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). Surface thermal radiation downwards J m-2 The surface net thermal radiation is the difference between thermal (long-wave) downward and upward radiation at the surface (ground and water) of the Earth. Thermal radiation is emitted by the atmosphere, clouds and the surface of the Earth. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). The model convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 The surface net thermal radiation is the difference between thermal (long-wave) downward and upward radiation at the surface (ground and water) of the Earth. Thermal radiation is emitted by the atmosphere, clouds and the surface of the Earth. It is an accumulated parameter meaning that it is accumulated since the last analysis. For instance, values extracted for 14 UTC reflect accumulated values since 12 UTC (the previous analysis). The model convention for vertical fluxes is positive downwards. Time-integrated surface direct short-wave radiation J m-2 The surface direct short-wave radiation is the amount of direct solar radiation reaching the surface (ground and water). It is given as a mean for the grid area. By model convention downward fluxes are positive. Surface direct solar radiation is only available for forecast time steps. It is an accumulated (time-integrated) parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated radiation over 24 hours. Time-integrated surface direct short-wave radiation J m-2 The surface direct short-wave radiation is the amount of direct solar radiation reaching the surface (ground and water). It is given as a mean for the grid area. By model convention downward fluxes are positive. Surface direct solar radiation is only available for forecast time steps. It is an accumulated (time-integrated) parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated radiation over 24 hours. Total cloud cover % Total cloud cover is the percentage of sky covert with clouds. It is valid for the grid area and clouds at any height above the surface are considered. Given values are instantaneous. Total cloud cover % Total cloud cover is the percentage of sky covert with clouds. It is valid for the grid area and clouds at any height above the surface are considered. Given values are instantaneous. Total column integrated water vapour kg m-2 The total column integrated water vapour is the vertically integrated water vapour (precipitable water content) valid for the grid area. It is vertically integrated from the surface to the top of the atmosphere. Given values are instantaneous. Total column integrated water vapour kg m-2 The total column integrated water vapour is the vertically integrated water vapour (precipitable water content) valid for the grid area. It is vertically integrated from the surface to the top of the atmosphere. Given values are instantaneous. Total precipitation kg m-2 Total precipitation is the amount of precipitation falling onto the ground/water surface. It includes all kind of precipitation forms such as convective precipitation, large scale precipitation, liquid and solid precipitation. The amount is valid for the grid area. The total precipitation is available only for the forecast time steps. It is an accumulated parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24h-forecast has the accumulated precipitation over 24 hours. Total precipitation kg m-2 Total precipitation is the amount of precipitation falling onto the ground/water surface. It includes all kind of precipitation forms such as convective precipitation, large scale precipitation, liquid and solid precipitation. The amount is valid for the grid area. The total precipitation is available only for the forecast time steps. It is an accumulated parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24h-forecast has the accumulated precipitation over 24 hours. Volumetric soil moisture m3 m-3 The volumetric soil moisture is the sum of the liquid and frozen water in a cubic metre soil valid for the grid area in the corresponding soil layer. The parameter is available for analysis and forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The soil model has three layers but only data for the top layer, closest to the surface, are provided. Deeper layers are affected by spin-up effects at the seams of the production streams. Users interested in soil parameters are recommended to use CERRA-Land data. Volumetric soil moisture m3 m-3 The volumetric soil moisture is the sum of the liquid and frozen water in a cubic metre soil valid for the grid area in the corresponding soil layer. The parameter is available for analysis and forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The soil model has three layers but only data for the top layer, closest to the surface, are provided. Deeper layers are affected by spin-up effects at the seams of the production streams. Users interested in soil parameters are recommended to use CERRA-Land data. 272 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-ocean-colour-plankton-reflectance http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_MED_BGC_L3_NRT_009_141 Mediterranean Sea Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations Short description: For the Mediterranean Sea Ocean Satellite Observations, the Italian National Research Council (CNR – Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets: * ''plankton'' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017) * ''reflectance'' with the spectral Remote Sensing Reflectance (RRS) * ''transparency'' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for ""multi"" observations achieved via region-specific algorithm, Volpe et al., 2019) * ''optics'' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates) Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the ""multi"" products, and OLCI-S3A & S3B for the ""olci"" products Temporal resolution: daily Spatial resolutions: 1 km for ""multi"" and 300 meters for ""olci"" To find this product in the catalogue, use the search keyword ""OCEANCOLOUR_MED_BGC_L3_NRT"". DOI (product) :https://doi.org/10.48670/moi-00297 https://doi.org/10.48670/moi-00297 273 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-ocean-colour-plankton-and-transparency-nrt-l4 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_BLK_BGC_L4_NRT_009_152 Black Sea Ocean Colour Plankton and Transparency NRT L4 daily and monthly observations Short description: For the Black Sea Ocean Satellite Observations, the Italian National Research Council (CNR – Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets: * ''plankton'' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated gap-free Chl concentration (to provide a ""cloud free"" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018) * ''transparency'' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for ""multi"" observations achieved via region-specific algorithm, Volpe et al., 2019) Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the ""multi"" products, and OLCI-S3A & S3B for the ""olci"" products Temporal resolutions: monthly and daily (for ""gap-free"" data) Spatial resolutions: 1 km for ""multi"" and 300 meters for ""olci"" To find this product in the catalogue, use the search keyword ""OCEANCOLOUR_BLK_BGC_L4_NRT"". DOI (product) :https://doi.org/10.48670/moi-00302 https://doi.org/10.48670/moi-00302 274 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-mean-sea-level-extreme-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_VAR_EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs Mediterranean Sea Mean Sea Level extreme from Observations Reprocessing DEFINITION The OMIVAR__EXTREME_SL_MEDSEA_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset medsea_omi_sl_extreme_var_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). CONTEXT Sea level is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990’s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one metre by the end of the century (Vousdoukas et al., 2020). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves. CMEMS KEY FINDINGS The completeness index criteria is fulfilled in this region by 11 stations, 3 more than in 2019, all of them in the Western Mediterranean. The mean 99th percentiles reflect the spatial variability of the tide, a microtidal regime, along the Spanish and French Mediterranean coasts, ranging from 0.23 m above mean sea level in Ibiza (Balearic Islands) to 0.39 m above mean sea level in Málaga, near the Strait of Gibraltar. The standard deviation ranges between 2 cm in Málaga and Motril (South of Spain) to 8 cm in Marseille. Most of the stations present clear negative anomalies of 2020 99th percentiles, increasing northwards in magnitude, up to -12 cm in Marseille. Small positive anomalies (around 2 cm) are observed however in Valencia and Ibiza (Spain). DOI (product):https://doi.org/10.48670/moi-00265 https://doi.org/10.48670/moi-00265 275 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-delayed-mode-wave-product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_GLO_WAV_DISCRETE_MY_013_045 Global Ocean - Delayed Mode Wave product Short description: These products integrate wave observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from National Data Centers (NODCs) and JCOMM global systems (OceanSITES, DBCP) and the Global telecommunication system (GTS) used by the Met Offices. DOI (product) :https://doi.org/10.17882/70345 https://doi.org/10.17882/70345 276 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/insitu-gridded-observations-nordic https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-nordic insitu-gridded-observations-nordic The Nordic Gridded Climate Dataset (NGCD) is a high resolution, observational, gridded dataset of daily minimum, maximum and mean temperatures and daily precipitation totals, covering Finland, Sweden and Norway. The time period covered begins in January 1971 and continues to the present. The dataset is regularly updated every 6 months, in March and in September. In addition, there are daily, provisional updates. Spatial interpolation methods are applied to observational datasets to create gridded datasets. In general, there are three types of such methods: deterministic (type 1), stochastic (type 2) and pure mathematical (type 3). NGCD applies both a deterministic kriging (type 1) interpolation approach and a stochastic Bayesian (type 2) interpolation approach to the same in-situ observational dataset collected by weather stations. For more details on the algorithms, users are advised to read the product user guide. The input data is provided by the National Meteorological and Hydrological Services of Finland, Norway and Sweden. The time-series used for Finland and Sweden are the non-blended time-series from the station network of the European Climate Assessment & Dataset (ECA&D) project. For Norway, time-series are extracted from the climate database of the Norwegian Meteorological Institute. DATA DESCRIPTION Data type Gridded Projection Lambert Azimuthal Equal-Area (ETRS89) Horizontal coverage Fennoscandia (Finland, Norway, Sweden) Horizontal resolution 1km x 1km Vertical coverage Surface Vertical resolution Single layer Temporal coverage Rrom 1971 to present Temporal resolution Daily File format NetCDF-4 Conventions Climate and Forecast Metadata Convention v1.6 (CF-1.6) Versions 22.03, 22.09, 23.03, 23.09 Update frequency Consolidated data: 6 monthly, in March and September Provisional data: daily DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert Azimuthal Equal-Area (ETRS89) Projection Lambert Azimuthal Equal-Area (ETRS89) Horizontal coverage Fennoscandia (Finland, Norway, Sweden) Horizontal coverage Fennoscandia (Finland, Norway, Sweden) Horizontal resolution 1km x 1km Horizontal resolution 1km x 1km Vertical coverage Surface Vertical coverage Surface Vertical resolution Single layer Vertical resolution Single layer Temporal coverage Rrom 1971 to present Temporal coverage Rrom 1971 to present Temporal resolution Daily Temporal resolution Daily File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast Metadata Convention v1.6 (CF-1.6) Conventions Climate and Forecast Metadata Convention v1.6 (CF-1.6) Versions 22.03, 22.09, 23.03, 23.09 Versions 22.03, 22.09, 23.03, 23.09 Update frequency Consolidated data: 6 monthly, in March and September Provisional data: daily Update frequency Consolidated data: 6 monthly, in March and September Provisional data: daily Consolidated data: 6 monthly, in March and September Provisional data: daily MAIN VARIABLES Name Units Description Maximum temperature °C Daily maximum air temperature measured near the surface, usually at a height of 2 meters. Aggregation period, with respect to the day D in the timestamp of the field: 18 UTC previous day to 18 UTC day D. Mean temperature °C Daily mean air temperature measured near the surface, usually at a height of 2 meters. Aggregation period, with respect to the day D in the timestamp of the field: 06 UTC previous day to 06 UTC day D. Minimum temperature °C Daily minimum air temperature measured near the surface, usually at a height of 2 meters. Aggregation period, with respect to the day D in the timestamp of the field: 18 UTC previous day to 18 UTC day D. Precipitation kg m-2 Total daily amount of rain, snow and hail. Aggregation period, with respect to the day D in the timestamp of the field: 06 UTC previous day to 06 UTC day D. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Maximum temperature °C Daily maximum air temperature measured near the surface, usually at a height of 2 meters. Aggregation period, with respect to the day D in the timestamp of the field: 18 UTC previous day to 18 UTC day D. Maximum temperature °C Daily maximum air temperature measured near the surface, usually at a height of 2 meters. Aggregation period, with respect to the day D in the timestamp of the field: 18 UTC previous day to 18 UTC day D. Mean temperature °C Daily mean air temperature measured near the surface, usually at a height of 2 meters. Aggregation period, with respect to the day D in the timestamp of the field: 06 UTC previous day to 06 UTC day D. Mean temperature °C Daily mean air temperature measured near the surface, usually at a height of 2 meters. Aggregation period, with respect to the day D in the timestamp of the field: 06 UTC previous day to 06 UTC day D. Minimum temperature °C Daily minimum air temperature measured near the surface, usually at a height of 2 meters. Aggregation period, with respect to the day D in the timestamp of the field: 18 UTC previous day to 18 UTC day D. Minimum temperature °C Daily minimum air temperature measured near the surface, usually at a height of 2 meters. Aggregation period, with respect to the day D in the timestamp of the field: 18 UTC previous day to 18 UTC day D. Precipitation kg m-2 Total daily amount of rain, snow and hail. Aggregation period, with respect to the day D in the timestamp of the field: 06 UTC previous day to 06 UTC day D. Precipitation kg m-2 Total daily amount of rain, snow and hail. Aggregation period, with respect to the day D in the timestamp of the field: 06 UTC previous day to 06 UTC day D. 277 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/european-north-west-shelfiberia-biscay-irish-seas-high-1 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_ATL_SST_L4_REP_OBSERVATIONS_010_026 European North West Shelf/Iberia Biscay Irish Seas - High Resolution L4 Sea Surface Temperature Reprocessed Short description: For the European North West Shelf Ocean Iberia Biscay Irish Seas. The IFREMER Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.05deg. x 0.05deg. horizontal resolution, over the 1982-2020 period, using satellite data from the European Space Agency Sea Surface Temperature Climate Change Initiative (ESA SST CCI) L3 products (1982-2016) and from the Copernicus Climate Change Service (C3S) L3 product (2017-2020). The gridded SST product is intended to represent a daily-mean SST field at 20 cm depth. DOI (product) :https://doi.org/10.48670/moi-00153 https://doi.org/10.48670/moi-00153 278 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-biodiversity-cmip5-global https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-biodiversity-cmip5-global sis-biodiversity-cmip5-global This dataset provides global bioclimatic indicators derived from CMIP5 climate projections. These bioclimatic indicators describe how the climate affects ecosystems, the services ecosystems deliver and nature’s biodiversity. They are specifically relevant for applications within the biodiversity and ecosystem community. The 78 indicators cover bioclimatic variables for both land and marine environments characterising surface energy, drought, soil moisture and the (near-)surface climate including wind as well as Essential Climate Variables (ECV). The selection of indicators is based on user requirements and consultation with stakeholders, in order to facilitate the direct use of climate information in screening analyses or in diverse downstream applications. The indicators calculated based on daily CMIP5 climate projections from 10 Global Circulation Models for two future climate scenarios, Representative Concentration Pathway (RCP) 4.5 & RCP 8.5. The data have been additionally bias-adjusted against ERA5 reanalysis data. The temporal resolution differs depending on the indicator, varying between monthly, annual and multi-annual averages. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 0.5° x 0.5° Vertical coverage Surface Vertical resolution Single level Temporal coverage 1950-2100 Temporal resolution Monthly, annual, and 20-year averages File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.5° x 0.5° Horizontal resolution 0.5° x 0.5° Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage 1950-2100 Temporal coverage 1950-2100 Temporal resolution Monthly, annual, and 20-year averages Temporal resolution Monthly, annual, and 20-year averages File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Versions 1.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Annual mean temperature (BIO01) K Annual mean of the daily mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Annual precipitation (BIO12) m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Aridity annual mean Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the year. Aridity coldest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the coldest quarter. Aridity driest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the driest quarter. Aridity warmest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the warmest quarter. Aridity wettest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the wettest quarter. Cloud cover Dimensionless Fraction of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Evaporative fraction annual mean Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the year. Evaporative fraction coldest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the coldest quarter. Evaporative fraction driest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the driest quarter. Evaporative fraction warmest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the warmest quarter. Evaporative fraction wettest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the wettest quarter. Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). The data is aggregated over the months. Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). The data is aggregated over the months. Growing degree days during growing season length K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC) during the period between the growing season start and end. Growing season end of season day of year The first day of a period of 5 consecutive days in the second half of the year with a mean daily temperature below 278 K (5oC). Growing season length day Number of days between the start and the end of the growing season. Growing season start of season day of year The first day of the year of a period of 5 consecutive days with a mean daily temperature above 278 K (5 oC). Isothermality (BIO03) % Monthly mean diurnal range divided by temperature annual range multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO03. Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds. Maximum 2m temperature K Mean of the daily maximum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Maximum length of dry spells day Maximum number of consecutive dry spell days within a year. Maximum precipitation m s-1 Maximum of the daily mean precipitation. The data is aggregated over the year. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000. Maximum temperature of the warmest month (BIO05) K Maximum daily temperature of the month with the highest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO05. Mean diurnal range (BIO02) K Mean of the daily maximum temperature minus the daily minimum temperature. The data is aggregated over the months. This indicator corresponds to the official BIOCLIM variable BIO02. Mean intensity of dry spells day Determine the consecutive dry days at each day in a year, then take the average of these daily values over the year. Mean length of dry spells day Mean length of dry spell days with a minimum of 5 days within a year. Mean precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Mean temperature of coldest quarter (BIO11) K The mean of monthly mean temperature during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO11. Mean temperature of driest quarter (BIO09) K The mean of monthly mean temperature during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO09. Mean temperature of warmest quarter (BIO10) K The mean of monthly mean temperature during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO10. Mean temperature of wettest quarter (BIO08) K The mean of monthly mean temperature during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO08. Meridional wind speed m s-1 Monthly mean of the northward component of the two-dimensional horizontal air velocity near the surface. Minimum temperature K Mean of the daily minimum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Minimum temperature of the coldest month (BIO06) K Minimum daily temperature of the month with the lowest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO06. Number of dry spells Dimensionless Number of dry spells with a minimum of 5 days that occur in a year. Potential evaporation annual mean m s-1 Annual averaged amount of water that would evaporate and transpire if there is unlimited water supply. Potential evaporation coldest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the coldest quarter. Potential evaporation driest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the driest quarter. Potential evaporation warmest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the warmest quarter. Potential evaporation wettest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the wettest quarter. Precipitation in coldest quarter (BIO19) m s-1 The mean of monthly mean precipitation during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO19. Precipitation in driest quarter (BIO17) m s-1 The mean of monthly mean precipitation during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO17. Precipitation in warmest quarter (BIO18) m s-1 The mean of monthly mean precipitation during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO18. Precipitation in wettest quarter (BIO16) m s-1 The mean of monthly mean precipitation during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO16. Precipitation of driest month (BIO14) m s-1 Minimum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO14. Precipitation of wettest month (BIO13) m s-1 Maximum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO13. Precipitation seasonality (BIO15) % Annual coefficient of variation of the monthly precipitation sums. This indicator corresponds to the official BIOCLIM variable BIO15. Sea ice concentration Dimensionless The fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land sea mask and lake cover, at the resolution being used. The data is available per month. Sea surface temperature K Temperature of sea water near the surface. The data is available per month. Summer days day Number of days in a year for which the daily maximum temperature is not lower than 298.15 K (25 oC). Surface latent heat flux annual mean W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface latent heat flux coldest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the through the effects of turbulent air motion, averaged over the coldest quarter. Surface latent heat flux driest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface latent heat flux warmest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface latent heat flux wettest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the wettest quarter. Surface sensible heat flux annual mean W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface sensible heat flux coldest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the coldest quarter. Surface sensible heat flux driest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface sensible heat flux warmest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface sensible heat flux wettest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere the effects of turbulent air motion, averaged over the wettest quarter. Temperature annual range (BIO07) K Maximum temperature of the warmest month minus minimum temperature of the coldest month. This indicator corresponds to the official BIOCLIM variable BIO07. Temperature seasonality (BIO04) K Standard deviation of the monthly mean temperature multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO04. Volumetric soil water layer 1 annual mean m3 m-3 The volume of water in soil layer 1 (0-7cm, the surface is at 0 cm) averaged over the year. The ECMWF Integrated Forecasting System model has a four-layer representation of soil; Layer 1: 0-7 cm; Layer 2: 7-28 cm; Layer 3: 28-100 cm; Layer 4: 100-289 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 coldest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the coldest quarter. Volumetric soil water layer 1 driest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the driest quarter. Volumetric soil water layer 1 warmest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the warmest quarter. Volumetric soil water layer 1 wettest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the wettest quarter. Water vapor pressure Pa Contribution to the total atmospheric pressure provided by the water vapor over the period 00-24h local time per unit of time. Indicator offered as a monthly or annual mean. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Zonal wind speed m s-1 Monthly mean of the eastward component of the two-dimensional horizontal air velocity near the surface. Indicator offered as a monthly or annual mean. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Annual mean temperature (BIO01) K Annual mean of the daily mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Annual mean temperature (BIO01) K Annual mean of the daily mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Annual precipitation (BIO12) m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Annual precipitation (BIO12) m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Aridity annual mean Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the year. Aridity annual mean Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the year. Aridity coldest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the coldest quarter. Aridity coldest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the coldest quarter. Aridity driest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the driest quarter. Aridity driest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the driest quarter. Aridity warmest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the warmest quarter. Aridity warmest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the warmest quarter. Aridity wettest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the wettest quarter. Aridity wettest quarter Dimensionless Monthly potential evaporation divided by the monthly mean precipitation, averaged over the wettest quarter. Cloud cover Dimensionless Fraction of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. Cloud cover Dimensionless Fraction of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Evaporative fraction annual mean Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the year. Evaporative fraction annual mean Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the year. Evaporative fraction coldest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the coldest quarter. Evaporative fraction coldest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the coldest quarter. Evaporative fraction driest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the driest quarter. Evaporative fraction driest quarter Dimensionless Monthly surface latent heat flux divided by the monthly total sensible and latent heat flux, averaged over the driest quarter. Evaporative fraction warmest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the warmest quarter. Evaporative fraction warmest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the warmest quarter. Evaporative fraction wettest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the wettest quarter. Evaporative fraction wettest quarter Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the wettest quarter. Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). The data is aggregated over the months. Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). The data is aggregated over the months. Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). The data is aggregated over the months. Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). The data is aggregated over the months. Growing degree days during growing season length K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC) during the period between the growing season start and end. Growing degree days during growing season length K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC) during the period between the growing season start and end. Growing season end of season day of year The first day of a period of 5 consecutive days in the second half of the year with a mean daily temperature below 278 K (5oC). Growing season end of season day of year The first day of a period of 5 consecutive days in the second half of the year with a mean daily temperature below 278 K (5oC). Growing season length day Number of days between the start and the end of the growing season. Growing season length day Number of days between the start and the end of the growing season. Growing season start of season day of year The first day of the year of a period of 5 consecutive days with a mean daily temperature above 278 K (5 oC). Growing season start of season day of year The first day of the year of a period of 5 consecutive days with a mean daily temperature above 278 K (5 oC). Isothermality (BIO03) % Monthly mean diurnal range divided by temperature annual range multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO03. Isothermality (BIO03) % Monthly mean diurnal range divided by temperature annual range multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO03. Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds. Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds. Maximum 2m temperature K Mean of the daily maximum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Maximum 2m temperature K Mean of the daily maximum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Maximum length of dry spells day Maximum number of consecutive dry spell days within a year. Maximum length of dry spells day Maximum number of consecutive dry spell days within a year. Maximum precipitation m s-1 Maximum of the daily mean precipitation. The data is aggregated over the year. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000. Maximum precipitation m s-1 Maximum of the daily mean precipitation. The data is aggregated over the year. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000. Maximum temperature of the warmest month (BIO05) K Maximum daily temperature of the month with the highest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO05. Maximum temperature of the warmest month (BIO05) K Maximum daily temperature of the month with the highest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO05. Mean diurnal range (BIO02) K Mean of the daily maximum temperature minus the daily minimum temperature. The data is aggregated over the months. This indicator corresponds to the official BIOCLIM variable BIO02. Mean diurnal range (BIO02) K Mean of the daily maximum temperature minus the daily minimum temperature. The data is aggregated over the months. This indicator corresponds to the official BIOCLIM variable BIO02. Mean intensity of dry spells day Determine the consecutive dry days at each day in a year, then take the average of these daily values over the year. Mean intensity of dry spells day Determine the consecutive dry days at each day in a year, then take the average of these daily values over the year. Mean length of dry spells day Mean length of dry spell days with a minimum of 5 days within a year. Mean length of dry spells day Mean length of dry spell days with a minimum of 5 days within a year. Mean precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Mean precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Mean temperature of coldest quarter (BIO11) K The mean of monthly mean temperature during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO11. Mean temperature of coldest quarter (BIO11) K The mean of monthly mean temperature during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO11. Mean temperature of driest quarter (BIO09) K The mean of monthly mean temperature during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO09. Mean temperature of driest quarter (BIO09) K The mean of monthly mean temperature during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO09. Mean temperature of warmest quarter (BIO10) K The mean of monthly mean temperature during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO10. Mean temperature of warmest quarter (BIO10) K The mean of monthly mean temperature during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO10. Mean temperature of wettest quarter (BIO08) K The mean of monthly mean temperature during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO08. Mean temperature of wettest quarter (BIO08) K The mean of monthly mean temperature during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO08. Meridional wind speed m s-1 Monthly mean of the northward component of the two-dimensional horizontal air velocity near the surface. Meridional wind speed m s-1 Monthly mean of the northward component of the two-dimensional horizontal air velocity near the surface. Minimum temperature K Mean of the daily minimum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Minimum temperature K Mean of the daily minimum temperature near the surface. The data is aggregated as an average over the months and as an average and a maximum over the year. Minimum temperature of the coldest month (BIO06) K Minimum daily temperature of the month with the lowest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO06. Minimum temperature of the coldest month (BIO06) K Minimum daily temperature of the month with the lowest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO06. Number of dry spells Dimensionless Number of dry spells with a minimum of 5 days that occur in a year. Number of dry spells Dimensionless Number of dry spells with a minimum of 5 days that occur in a year. Potential evaporation annual mean m s-1 Annual averaged amount of water that would evaporate and transpire if there is unlimited water supply. Potential evaporation annual mean m s-1 Annual averaged amount of water that would evaporate and transpire if there is unlimited water supply. Potential evaporation coldest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the coldest quarter. Potential evaporation coldest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the coldest quarter. Potential evaporation driest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the driest quarter. Potential evaporation driest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the driest quarter. Potential evaporation warmest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the warmest quarter. Potential evaporation warmest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the warmest quarter. Potential evaporation wettest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the wettest quarter. Potential evaporation wettest quarter m s-1 The amount of water that would evaporate and transpire if there is unlimited water supply, averaged for the wettest quarter. Precipitation in coldest quarter (BIO19) m s-1 The mean of monthly mean precipitation during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO19. Precipitation in coldest quarter (BIO19) m s-1 The mean of monthly mean precipitation during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO19. Precipitation in driest quarter (BIO17) m s-1 The mean of monthly mean precipitation during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO17. Precipitation in driest quarter (BIO17) m s-1 The mean of monthly mean precipitation during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO17. Precipitation in warmest quarter (BIO18) m s-1 The mean of monthly mean precipitation during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO18. Precipitation in warmest quarter (BIO18) m s-1 The mean of monthly mean precipitation during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO18. Precipitation in wettest quarter (BIO16) m s-1 The mean of monthly mean precipitation during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO16. Precipitation in wettest quarter (BIO16) m s-1 The mean of monthly mean precipitation during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO16. Precipitation of driest month (BIO14) m s-1 Minimum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO14. Precipitation of driest month (BIO14) m s-1 Minimum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO14. Precipitation of wettest month (BIO13) m s-1 Maximum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO13. Precipitation of wettest month (BIO13) m s-1 Maximum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO13. Precipitation seasonality (BIO15) % Annual coefficient of variation of the monthly precipitation sums. This indicator corresponds to the official BIOCLIM variable BIO15. Precipitation seasonality (BIO15) % Annual coefficient of variation of the monthly precipitation sums. This indicator corresponds to the official BIOCLIM variable BIO15. Sea ice concentration Dimensionless The fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land sea mask and lake cover, at the resolution being used. The data is available per month. Sea ice concentration Dimensionless The fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land sea mask and lake cover, at the resolution being used. The data is available per month. Sea surface temperature K Temperature of sea water near the surface. The data is available per month. Sea surface temperature K Temperature of sea water near the surface. The data is available per month. Summer days day Number of days in a year for which the daily maximum temperature is not lower than 298.15 K (25 oC). Summer days day Number of days in a year for which the daily maximum temperature is not lower than 298.15 K (25 oC). Surface latent heat flux annual mean W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface latent heat flux annual mean W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface latent heat flux coldest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the through the effects of turbulent air motion, averaged over the coldest quarter. Surface latent heat flux coldest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the through the effects of turbulent air motion, averaged over the coldest quarter. Surface latent heat flux driest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface latent heat flux driest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface latent heat flux warmest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface latent heat flux warmest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface latent heat flux wettest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the wettest quarter. Surface latent heat flux wettest quarter W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the wettest quarter. Surface sensible heat flux annual mean W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface sensible heat flux annual mean W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Surface sensible heat flux coldest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the coldest quarter. Surface sensible heat flux coldest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the coldest quarter. Surface sensible heat flux driest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface sensible heat flux driest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the driest quarter. Surface sensible heat flux warmest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface sensible heat flux warmest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the warmest quarter. Surface sensible heat flux wettest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere the effects of turbulent air motion, averaged over the wettest quarter. Surface sensible heat flux wettest quarter W m-2 The transfer of heat between the Earths surface and the atmosphere the effects of turbulent air motion, averaged over the wettest quarter. Temperature annual range (BIO07) K Maximum temperature of the warmest month minus minimum temperature of the coldest month. This indicator corresponds to the official BIOCLIM variable BIO07. Temperature annual range (BIO07) K Maximum temperature of the warmest month minus minimum temperature of the coldest month. This indicator corresponds to the official BIOCLIM variable BIO07. Temperature seasonality (BIO04) K Standard deviation of the monthly mean temperature multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO04. Temperature seasonality (BIO04) K Standard deviation of the monthly mean temperature multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO04. Volumetric soil water layer 1 annual mean m3 m-3 The volume of water in soil layer 1 (0-7cm, the surface is at 0 cm) averaged over the year. The ECMWF Integrated Forecasting System model has a four-layer representation of soil; Layer 1: 0-7 cm; Layer 2: 7-28 cm; Layer 3: 28-100 cm; Layer 4: 100-289 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 annual mean m3 m-3 The volume of water in soil layer 1 (0-7cm, the surface is at 0 cm) averaged over the year. The ECMWF Integrated Forecasting System model has a four-layer representation of soil; Layer 1: 0-7 cm; Layer 2: 7-28 cm; Layer 3: 28-100 cm; Layer 4: 100-289 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 coldest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the coldest quarter. Volumetric soil water layer 1 coldest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the coldest quarter. Volumetric soil water layer 1 driest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the driest quarter. Volumetric soil water layer 1 driest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the driest quarter. Volumetric soil water layer 1 warmest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the warmest quarter. Volumetric soil water layer 1 warmest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the warmest quarter. Volumetric soil water layer 1 wettest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the wettest quarter. Volumetric soil water layer 1 wettest quarter m3 m-3 The volume of water in soil layer 1 (0-7 cm, the surface is at 0 cm) averaged over the wettest quarter. Water vapor pressure Pa Contribution to the total atmospheric pressure provided by the water vapor over the period 00-24h local time per unit of time. Indicator offered as a monthly or annual mean. Water vapor pressure Pa Contribution to the total atmospheric pressure provided by the water vapor over the period 00-24h local time per unit of time. Indicator offered as a monthly or annual mean. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Zonal wind speed m s-1 Monthly mean of the eastward component of the two-dimensional horizontal air velocity near the surface. Indicator offered as a monthly or annual mean. Zonal wind speed m s-1 Monthly mean of the eastward component of the two-dimensional horizontal air velocity near the surface. Indicator offered as a monthly or annual mean. 279 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-water-level-change-timeseries-cmip6 https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-water-level-change-timeseries-cmip6 sis-water-level-change-timeseries-cmip6 This dataset provides time series of global sea level related variables including tides, storm surges and sea level rise from 1950 to 2050 based on hydrodynamic modelling. The dataset provides a basis for studies, for instance, aiming to evaluate sea level variability, coastal flooding, coastal erosion, and accessibility of ports. The time series are computed using the Deltares Global Tide and Surge Model (GTSM) version 3.0, a hydrodynamic model that dynamically simulates water levels at 10-minute intervals and capable of using input forcing from reanalysis and climate models. The dataset is based on climate forcing from ERA5 global reanalysis and 5 Global Climate Models (GCMs) of the high resolution Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate projection dataset from the High Resolution Model Intercomparison Project (HighResMIP) multi-model ensemble. By making use of the HighResMIP multi-model ensemble, it is possible to quantify the uncertainties associated with the input climate forcing in this dataset. The simulations for the historical period may be selected from either ERA5 reanalysis or historical simulations from the GCMs. The future period simulations are from the GCMs and are based on global climate projections using the high-emissions scenario SSP5-8.5 (Shared Socioeconomic Pathways). An exception to this is the tidal elevation variable that is not based on any future climate scenarios, since the tide-generating forces considered in GTSM are not related to any climatological nor meteorological conditions. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded with variable grid step Projection Latitude-longitude grid Horizontal coverage Global Horizontal resolution Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Vertical coverage Surface Vertical resolution Single level Temporal coverage ERA5 reanalysis: 1979 to 2018 Climate projections historical: 1950 to 2014 Climate projections future: 2015 to 2050 Temporal resolution Reanalysis: 10-minute, hourly and daily maximum Climate projections historical and future: 10-minute, annual File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions Dataset version 1.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded with variable grid step Data type Gridded with variable grid step Projection Latitude-longitude grid Projection Latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Horizontal resolution Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage ERA5 reanalysis: 1979 to 2018 Climate projections historical: 1950 to 2014 Climate projections future: 2015 to 2050 Temporal coverage ERA5 reanalysis: 1979 to 2018 Climate projections historical: 1950 to 2014 Climate projections future: 2015 to 2050 ERA5 reanalysis: 1979 to 2018 Climate projections historical: 1950 to 2014 Climate projections future: 2015 to 2050 Temporal resolution Reanalysis: 10-minute, hourly and daily maximum Climate projections historical and future: 10-minute, annual Temporal resolution Reanalysis: 10-minute, hourly and daily maximum Climate projections historical and future: 10-minute, annual Reanalysis: 10-minute, hourly and daily maximum Climate projections historical and future: 10-minute, annual File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions Dataset version 1.0 Versions Dataset version 1.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Mean sea level m The annual mean sea level relative to the 1986-2005 reference period. Contributions to sea level rise include thermal expansion of the ocean, changes in ocean circulation, ice sheet contributions, and glacio-isostatic adjustment (but not subsidence or tectonics). Please refer to the appendix in the user documentation for details on the vertical reference level and sea level rise contribution. Storm surge residual m The storm surge residual is calculated as the difference between the total water level and the tidal elevation simulations. The effect of changes in annual mean sea level is included in the simulation in both the historical and future periods. Tidal elevation m The tidal elevation is derived from the GTSM simulation forced with the celestial tide generating forces only and no meteorological forcing (i.e. wind and pressure at mean sea level). This results in the pure tide with no storm surge effects. The effect of changes in annual mean sea level is included in the simulation in both the historical and future periods. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level and sea level rise contribution. Total water level m The total water level consists of the pure tide, storm surge and changes in annual mean sea level. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level and sea level rise contribution. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Mean sea level m The annual mean sea level relative to the 1986-2005 reference period. Contributions to sea level rise include thermal expansion of the ocean, changes in ocean circulation, ice sheet contributions, and glacio-isostatic adjustment (but not subsidence or tectonics). Please refer to the appendix in the user documentation for details on the vertical reference level and sea level rise contribution. Mean sea level m The annual mean sea level relative to the 1986-2005 reference period. Contributions to sea level rise include thermal expansion of the ocean, changes in ocean circulation, ice sheet contributions, and glacio-isostatic adjustment (but not subsidence or tectonics). Please refer to the appendix in the user documentation for details on the vertical reference level and sea level rise contribution. Storm surge residual m The storm surge residual is calculated as the difference between the total water level and the tidal elevation simulations. The effect of changes in annual mean sea level is included in the simulation in both the historical and future periods. Storm surge residual m The storm surge residual is calculated as the difference between the total water level and the tidal elevation simulations. The effect of changes in annual mean sea level is included in the simulation in both the historical and future periods. Tidal elevation m The tidal elevation is derived from the GTSM simulation forced with the celestial tide generating forces only and no meteorological forcing (i.e. wind and pressure at mean sea level). This results in the pure tide with no storm surge effects. The effect of changes in annual mean sea level is included in the simulation in both the historical and future periods. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level and sea level rise contribution. Tidal elevation m The tidal elevation is derived from the GTSM simulation forced with the celestial tide generating forces only and no meteorological forcing (i.e. wind and pressure at mean sea level). This results in the pure tide with no storm surge effects. The effect of changes in annual mean sea level is included in the simulation in both the historical and future periods. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level and sea level rise contribution. Total water level m The total water level consists of the pure tide, storm surge and changes in annual mean sea level. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level and sea level rise contribution. Total water level m The total water level consists of the pure tide, storm surge and changes in annual mean sea level. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level and sea level rise contribution. RELATED VARIABLES The longitude and latitude coordinates for the stations are provided in the variables named as station_x_coordinates and station_y_coordinates respectively. RELATED VARIABLES RELATED VARIABLES The longitude and latitude coordinates for the stations are provided in the variables named as station_x_coordinates and station_y_coordinates respectively. The longitude and latitude coordinates for the stations are provided in the variables named as station_x_coordinates and station_y_coordinates respectively. 280 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/urban-atlas-building-height-2012-raster-10-m-europe https://land.copernicus.eu/local/urban-atlas/building-height-2012?tab=download Urban Atlas Building Height 2012 (raster 10 m), Europe - version 3, Oct. 2022 This metadata refers to the Copernicus Building Height 2012 third version. The dataset is a 10m high resolution raster layer containing height information generated for selected cities and urban areas in the EEA38 member countries and United Kingdom as part of the Urban atlas suite of products. Height information is based on satellite data and derived datasets like the digital surface model (DSM), the digital terrain model (DTM) and the normalized DSM. The satellite data sources are IRS-P5 stereo images for the capital cities and VHR false stereo pairs extracted from the MAXAR catalogue (WV-01, WV-02, GE-01 and IK) for the remaining areas supplemented by LiDAR data as additional option. 281 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-european-wind-storm-indicators https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-wind-storm-indicators sis-european-wind-storm-indicators This dataset provides climatological indicators on European winter windstorms and their economic impact derived from ERA5 reanalysis. Also provided are risk indicators from a synthetically derived set of physically realistic windstorm events based on modelled climatic conditions. The primary users include the insurance sector, reinsurers and insurance industry service providers in response to their requirements for a catalogue of historic windstorm events within Europe. It is important for this sector to be able to characterise the temporal and geographic distribution of potentially destructive windstorm events over Europe. These data can also support sectors such as energy, transport, civil engineering and government. The indicators available in this dataset are categorised as follows: Windstorm tracks – the spatial track centred on the location of maximum relative vorticity above a threshold defining a windstorm as it moves over the North Atlantic Ocean and Europe. Windstorm footprints – the maximum wind speed of gusts for each grid point of the domain during the passage of the storm over a period of 72-hours since the selected storm is first identified. Summary indicators (Tier 1) – uses the windstorm tracks and maximum 10m wind speed to produce annual and decadal summary statistics for the land regions in Europe. Loss and risk indicators (Tier 3) – describing the socio-economic impact of windstorms for various land cover and building types in Europe. Windstorm tracks – the spatial track centred on the location of maximum relative vorticity above a threshold defining a windstorm as it moves over the North Atlantic Ocean and Europe. Windstorm tracks – the spatial track centred on the location of maximum relative vorticity above a threshold defining a windstorm as it moves over the North Atlantic Ocean and Europe. Windstorm footprints – the maximum wind speed of gusts for each grid point of the domain during the passage of the storm over a period of 72-hours since the selected storm is first identified. Windstorm footprints – the maximum wind speed of gusts for each grid point of the domain during the passage of the storm over a period of 72-hours since the selected storm is first identified. Summary indicators (Tier 1) – uses the windstorm tracks and maximum 10m wind speed to produce annual and decadal summary statistics for the land regions in Europe. Summary indicators (Tier 1) – uses the windstorm tracks and maximum 10m wind speed to produce annual and decadal summary statistics for the land regions in Europe. Loss and risk indicators (Tier 3) – describing the socio-economic impact of windstorms for various land cover and building types in Europe. Loss and risk indicators (Tier 3) – describing the socio-economic impact of windstorms for various land cover and building types in Europe. This dataset was produced on behalf of the Copernicus Climate Change Service. 282 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-monthly-mean-sea-ice-extent-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=ARCTIC_OMI_SI_extent_obs Arctic Monthly Mean Sea Ice Extent from Observations Reprocessing DEFINITION Sea Ice Extent (SIE) is defined as the area covered by sufficient sea ice, that is the area of ocean having more than 15% Sea Ice Concentration (SIC). SIC is the fractional area of ocean that is covered with sea ice. It is computed from Passive Microwave satellite observations since 1979. SIE is often reported with units of 106 km2 (millions square kilometers). The change in sea ice extent (trend) is expressed in millions of km squared per decade (106 km2/decade). In addition, trends are expressed relative to the 1979-2021 period in % per decade. These trends are calculated (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss). The annual mean trend is reported on the key figure, the March and September values are reported in the text below. SIE includes all sea ice, but not lake or river ice. See also section 1.7 in Samuelsen et al. (2016) for an introduction to this Ocean Monitoring Indicator (OMI). CONTEXT Sea ice is frozen seawater that floats at the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and by how much the sea ice cover is changing is essential for monitoring the health of the Earth. Sea ice has a significant impact on ecosystems and Arctic communities, as well as economic activities such as fisheries, tourism, and transport (Meredith et al. 2019). CMEMS KEY FINDINGS Since 1979, the Northern Hemisphere sea ice extent has decreased at an annual rate of -0.51 +/- 0.02 106 km2 per decade (-4.36 % per decade). Loss of sea ice extent during summer exceeds the loss observed during winter periods: Summer (September) sea ice extent loss amounts to -0.81 +/- 0.06 106 km2 per decade (-12.50% per decade). Winter (March) sea ice extent loss amounts to -0.39 +/- 0.03 106 km2 per decade (-2.54% per decade). These values are in agreement with those assessed in the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) (Meredith et al. 2019, with estimates up until year 2018). July 2021 had the 2nd lowest mean July sea ice extent . Sea ice extent in September 2012 is to date the record minimum Northern Hemisphere sea ice extent value since the beginning of the satellite record, followed by September values in 2020. DOI (product):https://doi.org/10.48670/moi-00191 https://doi.org/10.48670/moi-00191 283 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/viewer-sis-fisheries-ocean-fronts https://cds.climate.copernicus.eu/cdsapp#!/dataset/viewer-sis-fisheries-ocean-fronts viewer-sis-fisheries-ocean-fronts Viewer application for dataset More details about the products are given in the Documentation section. INPUT VARIABLES Name Units Description Change in distance to nearest major front km The change in ocean front location, defined as the monthly mean change in the distance to the nearest major front relative to the baseline. The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Change in frontal gradient magnitude °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) Monthly mean change in thermal or chlorophyll-a frontal gradient magnitude relative to the baseline (depending on the selected indicator). The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Change in frontal persistence % The monthly mean change in ocean front persistence relative to the baseline. The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Distance to nearest major front km Distance to the nearest major ocean front using a simplified version of the frontal map. Frontal gradient magnitude °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) The monthly mean thermal or chlorophyll-a frontal gradient magnitude (depending on the selected indicator). Frontal gradient magnitude can also be referred to as ocean front strength. Generally, as the strength of an ocean front increases, so does the productivity associated with it. Frontal persistence % The fraction of cloud-free observations of a pixel for which a front was detected per month. INPUT VARIABLES INPUT VARIABLES Name Units Description Name Units Description Change in distance to nearest major front km The change in ocean front location, defined as the monthly mean change in the distance to the nearest major front relative to the baseline. The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Change in distance to nearest major front km The change in ocean front location, defined as the monthly mean change in the distance to the nearest major front relative to the baseline. The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Change in frontal gradient magnitude °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) Monthly mean change in thermal or chlorophyll-a frontal gradient magnitude relative to the baseline (depending on the selected indicator). The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Change in frontal gradient magnitude °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) Monthly mean change in thermal or chlorophyll-a frontal gradient magnitude relative to the baseline (depending on the selected indicator). The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Change in frontal persistence % The monthly mean change in ocean front persistence relative to the baseline. The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Change in frontal persistence % The monthly mean change in ocean front persistence relative to the baseline. The baseline is from a historical run of the same model from 1990-2005, or in the case of the satellite datasets from the available Climate Change Initiative data up to 2005. Distance to nearest major front km Distance to the nearest major ocean front using a simplified version of the frontal map. Distance to nearest major front km Distance to the nearest major ocean front using a simplified version of the frontal map. Frontal gradient magnitude °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) The monthly mean thermal or chlorophyll-a frontal gradient magnitude (depending on the selected indicator). Frontal gradient magnitude can also be referred to as ocean front strength. Generally, as the strength of an ocean front increases, so does the productivity associated with it. Frontal gradient magnitude °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) °C km-1 (for sea surface temperature indicator) log Chl mg m-3 km-1 (for chlorophyll-a indicator) The monthly mean thermal or chlorophyll-a frontal gradient magnitude (depending on the selected indicator). Frontal gradient magnitude can also be referred to as ocean front strength. Generally, as the strength of an ocean front increases, so does the productivity associated with it. Frontal persistence % The fraction of cloud-free observations of a pixel for which a front was detected per month. Frontal persistence % The fraction of cloud-free observations of a pixel for which a front was detected per month. 284 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/water-and-wetness-2018-raster-10-m-europe-3-yearly https://land.copernicus.eu/pan-european/high-resolution-layers/water-wetness/status-maps/water-wetness-2018 Water and Wetness 2018 (raster 10 m), Europe, 3-yearly - version 2, Nov. 2020 The Copernicus High Resolution Water and Wetness (WAW) 2018 layer is a thematic product showing the occurrence of water and wet surfaces over the period from 2012 to 2018 for the EEA38 area and the United Kingdom. Two products are available: - The main Water and Wetness (WAW) product, with defined classes of (1) permanent water, (2) temporary water, (3) permanent wetness and (4) temporary wetness. - The additional expert product: Water and Wetness Probability Index (WWPI). The products show the occurrence of water and indicate the degree of wetness in a physical sense, assessed independently of the actual vegetation cover and are thus not limited to a specific land cover class and their relative frequencies. The production of the High Resolution Water and Wetness layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. The dataset is provided as 10 meter rasters in 100 x 100 km tiles grouped according to the EEA38 countries and the United Kingdom (fully conformant with the EEA reference grid). 285 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-era5-land https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land reanalysis-era5-land ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'. The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. The temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 0.1° x 0.1°; Native resolution is 9 km. Vertical coverage From 2 m above the surface level, to a soil depth of 289 cm. Vertical resolution 4 levels of the ECMWF surface model: Layer 1: 0 -7cm, Layer 2: 7 -28cm, Layer 3: 28-100cm, Layer 4: 100-289cm Some parameters are defined at 2 m over the surface. Temporal coverage January 1950 to present Temporal resolution Hourly File format GRIB Update frequency Monthly with a delay of about three months relatively to actual date. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.1° x 0.1°; Native resolution is 9 km. Horizontal resolution 0.1° x 0.1°; Native resolution is 9 km. Vertical coverage From 2 m above the surface level, to a soil depth of 289 cm. Vertical coverage From 2 m above the surface level, to a soil depth of 289 cm. Vertical resolution 4 levels of the ECMWF surface model: Layer 1: 0 -7cm, Layer 2: 7 -28cm, Layer 3: 28-100cm, Layer 4: 100-289cm Some parameters are defined at 2 m over the surface. Vertical resolution 4 levels of the ECMWF surface model: Layer 1: 0 -7cm, Layer 2: 7 -28cm, Layer 3: 28-100cm, Layer 4: 100-289cm Some parameters are defined at 2 m over the surface. Temporal coverage January 1950 to present Temporal coverage January 1950 to present Temporal resolution Hourly Temporal resolution Hourly File format GRIB File format GRIB Update frequency Monthly with a delay of about three months relatively to actual date. Update frequency Monthly with a delay of about three months relatively to actual date. MAIN VARIABLES Name Units Description 10m u-component of wind m s-1 Eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System. This variable can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m v-component of wind m s-1 Northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System. This variable can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind. 2m dewpoint temperature K Temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K Temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Evaporation from bare soil m of water equivalent The amount of evaporation from bare soil at the top of the land surface. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from open water surfaces excluding oceans m of water equivalent Amount of evaporation from surface water storage like lakes and inundated areas but excluding oceans. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from the top of canopy m of water equivalent The amount of evaporation from the canopy interception reservoir at the top of the canopy. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from vegetation transpiration m of water equivalent Amount of evaporation from vegetation transpiration. This has the same meaning as root extraction i.e. the amount of water extracted from the different soil layers. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Forecast albedo dimensionless Is a measure of the reflectivity of the Earth's surface. It is the fraction of solar (shortwave) radiation reflected by Earth's surface, across the solar spectrum, for both direct and diffuse radiation. Values are between 0 and 1. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Lake bottom temperature K Temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers) and coastal waters. ECMWF implemented a lake model in May 2015 to represent the water temperature and lake ice of all the world’s major inland water bodies in the Integrated Forecasting System. The model keeps lake depth and surface area (or fractional cover) constant in time. Lake ice depth m The thickness of ice on inland water bodies (lakes, reservoirs and rivers) and coastal waters. The ECMWF Integrated Forecasting System (IFS) represents the formation and melting of ice on inland water bodies (lakes, reservoirs and rivers) and coastal water. A single ice layer is represented. This parameter is the thickness of that ice layer. Lake ice temperature K The temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers) and coastal waters. The ECMWF Integrated Forecasting System represents the formation and melting of ice on lakes. A single ice layer is represented. The temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer depth m The thickness of the upper most layer of an inland water body (lake, reservoirs, and rivers) or coastal waters that is well mixed and has a near constant temperature with depth (uniform distribution of temperature). The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. Lake mix-layer temperature K The temperature of the upper most layer of inland water bodies (lakes, reservoirs and rivers) or coastal waters) that is well mixed. The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake shape factor dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs and rivers) and coastal waters. It is used to calculate the lake bottom temperature and other lake-related parameters. The ECMWF Integrated Forecasting System represents inland and coastal water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. Lake total layer temperature K The mean temperature of total water column in inland water bodies (lakes, reservoirs and rivers) and coastal waters. The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. This parameter is the mean over the two layers. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Leaf area index, high vegetation m2 m-2 One-half of the total green leaf area per unit horizontal ground surface area for high vegetation type. Leaf area index, low vegetation m2 m-2 One-half of the total green leaf area per unit horizontal ground surface area for low vegetation type. Potential evaporation m Potential evaporation (pev) in the current ECMWF model is computed, by making a second call to the surface energy balance routine with the vegetation variables set to "crops/mixed farming" and assuming no stress from soil moisture. In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although pev is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. Note that in ERA5-Land pev is computed as an open water evaporation (Pan evaporation) and assuming that the atmosphere is not affected by this artificial surface condition. The latter is different from the way pev is computed in ERA5. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Skin reservoir content m of water equivalent Amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of 'skin reservoir content' a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the 'skin reservoir' by evaporation. Skin temperature K Temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Snow albedo dimensionless It is defined as the fraction of solar (shortwave) radiation reflected by the snow, across the solar spectrum, for both direct and diffuse radiation. It is a measure of the reflectivity of the snow covered grid cells. Values vary between 0 and 1. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above. Snow cover % It represents the fraction (0-1) of the cell / grid-box occupied by snow (similar to the cloud cover fields of ERA5). Snow density kg m-3 Mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow depth m Instantaneous grib-box average of the snow thickness on the ground (excluding snow on canopy). Snow depth water equivalent m of water equivalent Depth of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow evaporation m of water equivalent Evaporation from snow averaged over the grid box (to find flux over snow, divide by snow fraction). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Snowfall m of water equivalent Accumulated total snow that has fallen to the Earth's surface. It consists of snow due to the large-scale atmospheric flow (horizontal scales greater than around a few hundred metres) and convection where smaller scale areas (around 5km to a few hundred kilometres) of warm air rise. If snow has melted during the period over which this variable was accumulated, then it will be higher than the snow depth. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units given measure the depth the water would have if the snow melted and was spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Snowmelt m of water equivalent Melting of snow averaged over the grid box (to find melt over snow, divide by snow fraction). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Soil temperature level 1 K Temperature of the soil in layer 1 (0 - 7 cm) of the ECMWF Integrated Forecasting System. The surface is at 0 cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K Temperature of the soil in layer 2 (7 -28cm) of the ECMWF Integrated Forecasting System. Soil temperature level 3 K Temperature of the soil in layer 3 (28-100cm) of the ECMWF Integrated Forecasting System. Soil temperature level 4 K Temperature of the soil in layer 4 (100-289 cm) of the ECMWF Integrated Forecasting System. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Surface latent heat flux J m-2 Exchange of latent heat with the surface through turbulent diffusion. This variables is accumulated from the beginning of the forecast time to the end of the forecast step. By model convention, downward fluxes are positive. Surface net solar radiation J m-2 Amount of solar radiation (also known as shortwave radiation) reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Net thermal radiation at the surface. Accumulated field from the beginning of the forecast time to the end of the forecast step. By model convention downward fluxes are positive. Surface pressure Pa Pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water. It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this variable are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Surface sensible heat flux J m-2 Transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This is a single level variable and it is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 Amount of solar radiation (also known as shortwave radiation) reaching the surface of the Earth. This variable comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this variable). To a reasonably good approximation, this variable is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 Amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches the Earth's surface. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this variable). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Temperature of snow layer K This variable gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Total evaporation m of water equivalent Accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This variable is accumulated from the beginning of the forecast to the end of the forecast step. The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Total precipitation m Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Volumetric soil water layer 1 m3 m-3 Volume of water in soil layer 1 (0 - 7 cm) of the ECMWF Integrated Forecasting System. The surface is at 0 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 Volume of water in soil layer 2 (7 -28 cm) of the ECMWF Integrated Forecasting System. Volumetric soil water layer 3 m3 m-3 Volume of water in soil layer 3 (28-100 cm) of the ECMWF Integrated Forecasting System. Volumetric soil water layer 4 m3 m-3 Volume of water in soil layer 4 (100-289 cm) of the ECMWF Integrated Forecasting System. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 10m u-component of wind m s-1 Eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System. This variable can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m u-component of wind m s-1 Eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System. This variable can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m v-component of wind m s-1 Northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System. This variable can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m v-component of wind m s-1 Northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System. This variable can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind. 2m dewpoint temperature K Temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m dewpoint temperature K Temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K Temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K Temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Evaporation from bare soil m of water equivalent The amount of evaporation from bare soil at the top of the land surface. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from bare soil m of water equivalent The amount of evaporation from bare soil at the top of the land surface. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from open water surfaces excluding oceans m of water equivalent Amount of evaporation from surface water storage like lakes and inundated areas but excluding oceans. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from open water surfaces excluding oceans m of water equivalent Amount of evaporation from surface water storage like lakes and inundated areas but excluding oceans. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from the top of canopy m of water equivalent The amount of evaporation from the canopy interception reservoir at the top of the canopy. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from the top of canopy m of water equivalent The amount of evaporation from the canopy interception reservoir at the top of the canopy. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from vegetation transpiration m of water equivalent Amount of evaporation from vegetation transpiration. This has the same meaning as root extraction i.e. the amount of water extracted from the different soil layers. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from vegetation transpiration m of water equivalent Amount of evaporation from vegetation transpiration. This has the same meaning as root extraction i.e. the amount of water extracted from the different soil layers. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Forecast albedo dimensionless Is a measure of the reflectivity of the Earth's surface. It is the fraction of solar (shortwave) radiation reflected by Earth's surface, across the solar spectrum, for both direct and diffuse radiation. Values are between 0 and 1. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Forecast albedo dimensionless Is a measure of the reflectivity of the Earth's surface. It is the fraction of solar (shortwave) radiation reflected by Earth's surface, across the solar spectrum, for both direct and diffuse radiation. Values are between 0 and 1. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Lake bottom temperature K Temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers) and coastal waters. ECMWF implemented a lake model in May 2015 to represent the water temperature and lake ice of all the world’s major inland water bodies in the Integrated Forecasting System. The model keeps lake depth and surface area (or fractional cover) constant in time. Lake bottom temperature K Temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers) and coastal waters. ECMWF implemented a lake model in May 2015 to represent the water temperature and lake ice of all the world’s major inland water bodies in the Integrated Forecasting System. The model keeps lake depth and surface area (or fractional cover) constant in time. Lake ice depth m The thickness of ice on inland water bodies (lakes, reservoirs and rivers) and coastal waters. The ECMWF Integrated Forecasting System (IFS) represents the formation and melting of ice on inland water bodies (lakes, reservoirs and rivers) and coastal water. A single ice layer is represented. This parameter is the thickness of that ice layer. Lake ice depth m The thickness of ice on inland water bodies (lakes, reservoirs and rivers) and coastal waters. The ECMWF Integrated Forecasting System (IFS) represents the formation and melting of ice on inland water bodies (lakes, reservoirs and rivers) and coastal water. A single ice layer is represented. This parameter is the thickness of that ice layer. Lake ice temperature K The temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers) and coastal waters. The ECMWF Integrated Forecasting System represents the formation and melting of ice on lakes. A single ice layer is represented. The temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake ice temperature K The temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers) and coastal waters. The ECMWF Integrated Forecasting System represents the formation and melting of ice on lakes. A single ice layer is represented. The temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer depth m The thickness of the upper most layer of an inland water body (lake, reservoirs, and rivers) or coastal waters that is well mixed and has a near constant temperature with depth (uniform distribution of temperature). The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. Lake mix-layer depth m The thickness of the upper most layer of an inland water body (lake, reservoirs, and rivers) or coastal waters that is well mixed and has a near constant temperature with depth (uniform distribution of temperature). The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. Lake mix-layer temperature K The temperature of the upper most layer of inland water bodies (lakes, reservoirs and rivers) or coastal waters) that is well mixed. The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer temperature K The temperature of the upper most layer of inland water bodies (lakes, reservoirs and rivers) or coastal waters) that is well mixed. The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake shape factor dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs and rivers) and coastal waters. It is used to calculate the lake bottom temperature and other lake-related parameters. The ECMWF Integrated Forecasting System represents inland and coastal water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. Lake shape factor dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs and rivers) and coastal waters. It is used to calculate the lake bottom temperature and other lake-related parameters. The ECMWF Integrated Forecasting System represents inland and coastal water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. Lake total layer temperature K The mean temperature of total water column in inland water bodies (lakes, reservoirs and rivers) and coastal waters. The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. This parameter is the mean over the two layers. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake total layer temperature K The mean temperature of total water column in inland water bodies (lakes, reservoirs and rivers) and coastal waters. The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. This parameter is the mean over the two layers. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Leaf area index, high vegetation m2 m-2 One-half of the total green leaf area per unit horizontal ground surface area for high vegetation type. Leaf area index, high vegetation m2 m-2 One-half of the total green leaf area per unit horizontal ground surface area for high vegetation type. Leaf area index, low vegetation m2 m-2 One-half of the total green leaf area per unit horizontal ground surface area for low vegetation type. Leaf area index, low vegetation m2 m-2 One-half of the total green leaf area per unit horizontal ground surface area for low vegetation type. Potential evaporation m Potential evaporation (pev) in the current ECMWF model is computed, by making a second call to the surface energy balance routine with the vegetation variables set to "crops/mixed farming" and assuming no stress from soil moisture. In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although pev is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. Note that in ERA5-Land pev is computed as an open water evaporation (Pan evaporation) and assuming that the atmosphere is not affected by this artificial surface condition. The latter is different from the way pev is computed in ERA5. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Potential evaporation m Potential evaporation (pev) in the current ECMWF model is computed, by making a second call to the surface energy balance routine with the vegetation variables set to "crops/mixed farming" and assuming no stress from soil moisture. In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although pev is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. Note that in ERA5-Land pev is computed as an open water evaporation (Pan evaporation) and assuming that the atmosphere is not affected by this artificial surface condition. The latter is different from the way pev is computed in ERA5. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Skin reservoir content m of water equivalent Amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of 'skin reservoir content' a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the 'skin reservoir' by evaporation. Skin reservoir content m of water equivalent Amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of 'skin reservoir content' a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the 'skin reservoir' by evaporation. Skin temperature K Temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Skin temperature K Temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Snow albedo dimensionless It is defined as the fraction of solar (shortwave) radiation reflected by the snow, across the solar spectrum, for both direct and diffuse radiation. It is a measure of the reflectivity of the snow covered grid cells. Values vary between 0 and 1. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above. Snow albedo dimensionless It is defined as the fraction of solar (shortwave) radiation reflected by the snow, across the solar spectrum, for both direct and diffuse radiation. It is a measure of the reflectivity of the snow covered grid cells. Values vary between 0 and 1. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above. Snow cover % It represents the fraction (0-1) of the cell / grid-box occupied by snow (similar to the cloud cover fields of ERA5). Snow cover % It represents the fraction (0-1) of the cell / grid-box occupied by snow (similar to the cloud cover fields of ERA5). Snow density kg m-3 Mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow density kg m-3 Mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow depth m Instantaneous grib-box average of the snow thickness on the ground (excluding snow on canopy). Snow depth m Instantaneous grib-box average of the snow thickness on the ground (excluding snow on canopy). Snow depth water equivalent m of water equivalent Depth of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow depth water equivalent m of water equivalent Depth of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow evaporation m of water equivalent Evaporation from snow averaged over the grid box (to find flux over snow, divide by snow fraction). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Snow evaporation m of water equivalent Evaporation from snow averaged over the grid box (to find flux over snow, divide by snow fraction). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Snowfall m of water equivalent Accumulated total snow that has fallen to the Earth's surface. It consists of snow due to the large-scale atmospheric flow (horizontal scales greater than around a few hundred metres) and convection where smaller scale areas (around 5km to a few hundred kilometres) of warm air rise. If snow has melted during the period over which this variable was accumulated, then it will be higher than the snow depth. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units given measure the depth the water would have if the snow melted and was spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Snowfall m of water equivalent Accumulated total snow that has fallen to the Earth's surface. It consists of snow due to the large-scale atmospheric flow (horizontal scales greater than around a few hundred metres) and convection where smaller scale areas (around 5km to a few hundred kilometres) of warm air rise. If snow has melted during the period over which this variable was accumulated, then it will be higher than the snow depth. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units given measure the depth the water would have if the snow melted and was spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Snowmelt m of water equivalent Melting of snow averaged over the grid box (to find melt over snow, divide by snow fraction). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Snowmelt m of water equivalent Melting of snow averaged over the grid box (to find melt over snow, divide by snow fraction). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Soil temperature level 1 K Temperature of the soil in layer 1 (0 - 7 cm) of the ECMWF Integrated Forecasting System. The surface is at 0 cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 1 K Temperature of the soil in layer 1 (0 - 7 cm) of the ECMWF Integrated Forecasting System. The surface is at 0 cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K Temperature of the soil in layer 2 (7 -28cm) of the ECMWF Integrated Forecasting System. Soil temperature level 2 K Temperature of the soil in layer 2 (7 -28cm) of the ECMWF Integrated Forecasting System. Soil temperature level 3 K Temperature of the soil in layer 3 (28-100cm) of the ECMWF Integrated Forecasting System. Soil temperature level 3 K Temperature of the soil in layer 3 (28-100cm) of the ECMWF Integrated Forecasting System. Soil temperature level 4 K Temperature of the soil in layer 4 (100-289 cm) of the ECMWF Integrated Forecasting System. Soil temperature level 4 K Temperature of the soil in layer 4 (100-289 cm) of the ECMWF Integrated Forecasting System. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Surface latent heat flux J m-2 Exchange of latent heat with the surface through turbulent diffusion. This variables is accumulated from the beginning of the forecast time to the end of the forecast step. By model convention, downward fluxes are positive. Surface latent heat flux J m-2 Exchange of latent heat with the surface through turbulent diffusion. This variables is accumulated from the beginning of the forecast time to the end of the forecast step. By model convention, downward fluxes are positive. Surface net solar radiation J m-2 Amount of solar radiation (also known as shortwave radiation) reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation J m-2 Amount of solar radiation (also known as shortwave radiation) reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Net thermal radiation at the surface. Accumulated field from the beginning of the forecast time to the end of the forecast step. By model convention downward fluxes are positive. Surface net thermal radiation J m-2 Net thermal radiation at the surface. Accumulated field from the beginning of the forecast time to the end of the forecast step. By model convention downward fluxes are positive. Surface pressure Pa Pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water. It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this variable are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Surface pressure Pa Pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water. It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this variable are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Surface sensible heat flux J m-2 Transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This is a single level variable and it is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface sensible heat flux J m-2 Transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This is a single level variable and it is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 Amount of solar radiation (also known as shortwave radiation) reaching the surface of the Earth. This variable comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this variable). To a reasonably good approximation, this variable is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 Amount of solar radiation (also known as shortwave radiation) reaching the surface of the Earth. This variable comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this variable). To a reasonably good approximation, this variable is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 Amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches the Earth's surface. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this variable). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 Amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches the Earth's surface. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this variable). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Temperature of snow layer K This variable gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Temperature of snow layer K This variable gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Total evaporation m of water equivalent Accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This variable is accumulated from the beginning of the forecast to the end of the forecast step. The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Total evaporation m of water equivalent Accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This variable is accumulated from the beginning of the forecast to the end of the forecast step. The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Total precipitation m Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Total precipitation m Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Volumetric soil water layer 1 m3 m-3 Volume of water in soil layer 1 (0 - 7 cm) of the ECMWF Integrated Forecasting System. The surface is at 0 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 m3 m-3 Volume of water in soil layer 1 (0 - 7 cm) of the ECMWF Integrated Forecasting System. The surface is at 0 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 Volume of water in soil layer 2 (7 -28 cm) of the ECMWF Integrated Forecasting System. Volumetric soil water layer 2 m3 m-3 Volume of water in soil layer 2 (7 -28 cm) of the ECMWF Integrated Forecasting System. Volumetric soil water layer 3 m3 m-3 Volume of water in soil layer 3 (28-100 cm) of the ECMWF Integrated Forecasting System. Volumetric soil water layer 3 m3 m-3 Volume of water in soil layer 3 (28-100 cm) of the ECMWF Integrated Forecasting System. Volumetric soil water layer 4 m3 m-3 Volume of water in soil layer 4 (100-289 cm) of the ECMWF Integrated Forecasting System. Volumetric soil water layer 4 m3 m-3 Volume of water in soil layer 4 (100-289 cm) of the ECMWF Integrated Forecasting System. 286 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-era5-land-monthly-means https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means reanalysis-era5-land-monthly-means ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land provides a consistent view of the water and energy cycles at surface level during several decades. It contains a detailed record from 1950 onwards, with a temporal resolution of 1 hour. The native spatial resolution of the ERA5-Land reanalysis dataset is 9km on a reduced Gaussian grid (TCo1279). The data in the CDS has been regridded to a regular lat-lon grid of 0.1x0.1 degrees. The data presented here is a post-processed subset of the full ERA5-Land dataset. Monthly-mean averages have been pre-calculated to facilitate many applications requiring easy and fast access to the data, when sub-monthly fields are not required. Hourly fields can be found in the ERA5-Land hourly fields CDS page. Documentation can be found in the online ERA5-Land documentation. ERA5-Land hourly fields CDS page online ERA5-Land documentation DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 0.1° x 0.1°; Native resolution is 9 km. Vertical coverage From 2 m above the surface level, to a soil depth of 289 cm. Vertical resolution 4 levels of the ECMWF surface model: Layer 1: 0 -7cm, Layer 2: 7 -28cm, Layer 3: 28-100cm, Layer 4: 100-289cm Some parameters are defined at 2 m over the surface. Temporal coverage January 1950 to present Temporal resolution Monthly File format GRIB Update frequency Monthly with a delay of 2-3 months relatively to the actual date. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.1° x 0.1°; Native resolution is 9 km. Horizontal resolution 0.1° x 0.1°; Native resolution is 9 km. Vertical coverage From 2 m above the surface level, to a soil depth of 289 cm. Vertical coverage From 2 m above the surface level, to a soil depth of 289 cm. Vertical resolution 4 levels of the ECMWF surface model: Layer 1: 0 -7cm, Layer 2: 7 -28cm, Layer 3: 28-100cm, Layer 4: 100-289cm Some parameters are defined at 2 m over the surface. Vertical resolution 4 levels of the ECMWF surface model: Layer 1: 0 -7cm, Layer 2: 7 -28cm, Layer 3: 28-100cm, Layer 4: 100-289cm Some parameters are defined at 2 m over the surface. Temporal coverage January 1950 to present Temporal coverage January 1950 to present Temporal resolution Monthly Temporal resolution Monthly File format GRIB File format GRIB Update frequency Monthly with a delay of 2-3 months relatively to the actual date. Update frequency Monthly with a delay of 2-3 months relatively to the actual date. MAIN VARIABLES Name Units Description 10m u-component of wind m s-1 Eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System. This variable can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m v-component of wind m s-1 Northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System. This variable can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind. 2m dewpoint temperature K Temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K Temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Evaporation from bare soil m of water equivalent The amount of evaporation from bare soil at the top of the land surface. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from open water surfaces excluding oceans m of water equivalent Amount of evaporation from surface water storage like lakes and inundated areas but excluding oceans. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from the top of canopy m of water equivalent The amount of evaporation from the canopy interception reservoir at the top of the canopy. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from vegetation transpiration m of water equivalent Amount of evaporation from vegetation transpiration. This has the same meaning as root extraction i.e. the amount of water extracted from the different soil layers. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Forecast albedo dimensionless Is a measure of the reflectivity of the Earth's surface. It is the fraction of solar (shortwave) radiation reflected by Earth's surface, across the solar spectrum, for both direct and diffuse radiation. Values are between 0 and 1. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Lake bottom temperature K Temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers) and coastal waters. ECMWF implemented a lake model in May 2015 to represent the water temperature and lake ice of all the world’s major inland water bodies in the Integrated Forecasting System. The model keeps lake depth and surface area (or fractional cover) constant in time. Lake ice depth m The thickness of ice on inland water bodies (lakes, reservoirs and rivers) and coastal waters. The ECMWF Integrated Forecasting System (IFS) represents the formation and melting of ice on inland water bodies (lakes, reservoirs and rivers) and coastal water. A single ice layer is represented. This parameter is the thickness of that ice layer. Lake ice temperature K The temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers) and coastal waters. The ECMWF Integrated Forecasting System represents the formation and melting of ice on lakes. A single ice layer is represented. The temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer depth m The thickness of the upper most layer of an inland water body (lake, reservoirs, and rivers) or coastal waters that is well mixed and has a near constant temperature with depth (uniform distribution of temperature). The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. Lake mix-layer temperature K The temperature of the upper most layer of inland water bodies (lakes, reservoirs and rivers) or coastal waters) that is well mixed. The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake shape factor dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs and rivers) and coastal waters. It is used to calculate the lake bottom temperature and other lake-related parameters. The ECMWF Integrated Forecasting System represents inland and coastal water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. Lake total layer temperature K The mean temperature of total water column in inland water bodies (lakes, reservoirs and rivers) and coastal waters. The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. This parameter is the mean over the two layers. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Leaf area index, high vegetation m2 m-2 One-half of the total green leaf area per unit horizontal ground surface area for high vegetation type. Leaf area index, low vegetation m2 m-2 One-half of the total green leaf area per unit horizontal ground surface area for low vegetation type. Potential evaporation m Potential evaporation (pev) in the current ECMWF model is computed, by making a second call to the surface energy balance routine with the vegetation variables set to "crops/mixed farming" and assuming no stress from soil moisture. In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although pev is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. Note that in ERA5-Land pev is computed as an open water evaporation (Pan evaporation) and assuming that the atmosphere is not affected by this artificial surface condition. The latter is different from the way pev is computed in ERA5. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Skin reservoir content m of water equivalent Amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of 'skin reservoir content' a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the 'skin reservoir' by evaporation. Skin temperature K Temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Snow albedo dimensionless It is defined as the fraction of solar (shortwave) radiation reflected by the snow, across the solar spectrum, for both direct and diffuse radiation. It is a measure of the reflectivity of the snow covered grid cells. Values vary between 0 and 1. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above. Snow cover % It represents the fraction (0-1) of the cell / grid-box occupied by snow (similar to the cloud cover fields of ERA5). Snow density kg m-3 Mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow depth m Instantaneous grib-box average of the snow thickness on the ground (excluding snow on canopy). Snow depth water equivalent m of water equivalent Depth of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow evaporation m of water equivalent Evaporation from snow averaged over the grid box (to find flux over snow, divide by snow fraction). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Snowfall m of water equivalent Accumulated total snow that has fallen to the Earth's surface. It consists of snow due to the large-scale atmospheric flow (horizontal scales greater than around a few hundred metres) and convection where smaller scale areas (around 5km to a few hundred kilometres) of warm air rise. If snow has melted during the period over which this variable was accumulated, then it will be higher than the snow depth. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units given measure the depth the water would have if the snow melted and was spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Snowmelt m of water equivalent Melting of snow averaged over the grid box (to find melt over snow, divide by snow fraction). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Soil temperature level 1 K Temperature of the soil in layer 1 (0 - 7 cm) of the ECMWF Integrated Forecasting System. The surface is at 0 cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K Temperature of the soil in layer 2 (7 -28cm) of the ECMWF Integrated Forecasting System. Soil temperature level 3 K Temperature of the soil in layer 3 (28-100cm) of the ECMWF Integrated Forecasting System. Soil temperature level 4 K Temperature of the soil in layer 4 (100-289 cm) of the ECMWF Integrated Forecasting System. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Surface latent heat flux J m-2 Exchange of latent heat with the surface through turbulent diffusion. This variables is accumulated from the beginning of the forecast time to the end of the forecast step. By model convention, downward fluxes are positive. Surface net solar radiation J m-2 Amount of solar radiation (also known as shortwave radiation) reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Net thermal radiation at the surface. Accumulated field from the beginning of the forecast time to the end of the forecast step. By model convention downward fluxes are positive. Surface pressure Pa Pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water. It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this variable are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Surface sensible heat flux J m-2 Transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This is a single level variable and it is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 Amount of solar radiation (also known as shortwave radiation) reaching the surface of the Earth. This variable comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this variable). To a reasonably good approximation, this variable is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 Amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches the Earth's surface. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this variable). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Temperature of snow layer K This variable gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Total evaporation m of water equivalent Accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This variable is accumulated from the beginning of the forecast to the end of the forecast step. The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Total precipitation m Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Volumetric soil water layer 1 m3 m-3 Volume of water in soil layer 1 (0 - 7 cm) of the ECMWF Integrated Forecasting System. The surface is at 0 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 Volume of water in soil layer 2 (7 -28 cm) of the ECMWF Integrated Forecasting System. Volumetric soil water layer 3 m3 m-3 Volume of water in soil layer 3 (28-100 cm) of the ECMWF Integrated Forecasting System. Volumetric soil water layer 4 m3 m-3 Volume of water in soil layer 4 (100-289 cm) of the ECMWF Integrated Forecasting System. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 10m u-component of wind m s-1 Eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System. This variable can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m u-component of wind m s-1 Eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System. This variable can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m v-component of wind m s-1 Northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System. This variable can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m v-component of wind m s-1 Northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System. This variable can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind. 2m dewpoint temperature K Temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m dewpoint temperature K Temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K Temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K Temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Evaporation from bare soil m of water equivalent The amount of evaporation from bare soil at the top of the land surface. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from bare soil m of water equivalent The amount of evaporation from bare soil at the top of the land surface. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from open water surfaces excluding oceans m of water equivalent Amount of evaporation from surface water storage like lakes and inundated areas but excluding oceans. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from open water surfaces excluding oceans m of water equivalent Amount of evaporation from surface water storage like lakes and inundated areas but excluding oceans. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from the top of canopy m of water equivalent The amount of evaporation from the canopy interception reservoir at the top of the canopy. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from the top of canopy m of water equivalent The amount of evaporation from the canopy interception reservoir at the top of the canopy. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from vegetation transpiration m of water equivalent Amount of evaporation from vegetation transpiration. This has the same meaning as root extraction i.e. the amount of water extracted from the different soil layers. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Evaporation from vegetation transpiration m of water equivalent Amount of evaporation from vegetation transpiration. This has the same meaning as root extraction i.e. the amount of water extracted from the different soil layers. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Forecast albedo dimensionless Is a measure of the reflectivity of the Earth's surface. It is the fraction of solar (shortwave) radiation reflected by Earth's surface, across the solar spectrum, for both direct and diffuse radiation. Values are between 0 and 1. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Forecast albedo dimensionless Is a measure of the reflectivity of the Earth's surface. It is the fraction of solar (shortwave) radiation reflected by Earth's surface, across the solar spectrum, for both direct and diffuse radiation. Values are between 0 and 1. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Lake bottom temperature K Temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers) and coastal waters. ECMWF implemented a lake model in May 2015 to represent the water temperature and lake ice of all the world’s major inland water bodies in the Integrated Forecasting System. The model keeps lake depth and surface area (or fractional cover) constant in time. Lake bottom temperature K Temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers) and coastal waters. ECMWF implemented a lake model in May 2015 to represent the water temperature and lake ice of all the world’s major inland water bodies in the Integrated Forecasting System. The model keeps lake depth and surface area (or fractional cover) constant in time. Lake ice depth m The thickness of ice on inland water bodies (lakes, reservoirs and rivers) and coastal waters. The ECMWF Integrated Forecasting System (IFS) represents the formation and melting of ice on inland water bodies (lakes, reservoirs and rivers) and coastal water. A single ice layer is represented. This parameter is the thickness of that ice layer. Lake ice depth m The thickness of ice on inland water bodies (lakes, reservoirs and rivers) and coastal waters. The ECMWF Integrated Forecasting System (IFS) represents the formation and melting of ice on inland water bodies (lakes, reservoirs and rivers) and coastal water. A single ice layer is represented. This parameter is the thickness of that ice layer. Lake ice temperature K The temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers) and coastal waters. The ECMWF Integrated Forecasting System represents the formation and melting of ice on lakes. A single ice layer is represented. The temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake ice temperature K The temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers) and coastal waters. The ECMWF Integrated Forecasting System represents the formation and melting of ice on lakes. A single ice layer is represented. The temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer depth m The thickness of the upper most layer of an inland water body (lake, reservoirs, and rivers) or coastal waters that is well mixed and has a near constant temperature with depth (uniform distribution of temperature). The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. Lake mix-layer depth m The thickness of the upper most layer of an inland water body (lake, reservoirs, and rivers) or coastal waters that is well mixed and has a near constant temperature with depth (uniform distribution of temperature). The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. Lake mix-layer temperature K The temperature of the upper most layer of inland water bodies (lakes, reservoirs and rivers) or coastal waters) that is well mixed. The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer temperature K The temperature of the upper most layer of inland water bodies (lakes, reservoirs and rivers) or coastal waters) that is well mixed. The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake shape factor dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs and rivers) and coastal waters. It is used to calculate the lake bottom temperature and other lake-related parameters. The ECMWF Integrated Forecasting System represents inland and coastal water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. Lake shape factor dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs and rivers) and coastal waters. It is used to calculate the lake bottom temperature and other lake-related parameters. The ECMWF Integrated Forecasting System represents inland and coastal water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. Lake total layer temperature K The mean temperature of total water column in inland water bodies (lakes, reservoirs and rivers) and coastal waters. The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. This parameter is the mean over the two layers. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake total layer temperature K The mean temperature of total water column in inland water bodies (lakes, reservoirs and rivers) and coastal waters. The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. This parameter is the mean over the two layers. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Leaf area index, high vegetation m2 m-2 One-half of the total green leaf area per unit horizontal ground surface area for high vegetation type. Leaf area index, high vegetation m2 m-2 One-half of the total green leaf area per unit horizontal ground surface area for high vegetation type. Leaf area index, low vegetation m2 m-2 One-half of the total green leaf area per unit horizontal ground surface area for low vegetation type. Leaf area index, low vegetation m2 m-2 One-half of the total green leaf area per unit horizontal ground surface area for low vegetation type. Potential evaporation m Potential evaporation (pev) in the current ECMWF model is computed, by making a second call to the surface energy balance routine with the vegetation variables set to "crops/mixed farming" and assuming no stress from soil moisture. In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although pev is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. Note that in ERA5-Land pev is computed as an open water evaporation (Pan evaporation) and assuming that the atmosphere is not affected by this artificial surface condition. The latter is different from the way pev is computed in ERA5. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Potential evaporation m Potential evaporation (pev) in the current ECMWF model is computed, by making a second call to the surface energy balance routine with the vegetation variables set to "crops/mixed farming" and assuming no stress from soil moisture. In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although pev is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. Note that in ERA5-Land pev is computed as an open water evaporation (Pan evaporation) and assuming that the atmosphere is not affected by this artificial surface condition. The latter is different from the way pev is computed in ERA5. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Skin reservoir content m of water equivalent Amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of 'skin reservoir content' a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the 'skin reservoir' by evaporation. Skin reservoir content m of water equivalent Amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of 'skin reservoir content' a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the 'skin reservoir' by evaporation. Skin temperature K Temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Skin temperature K Temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Snow albedo dimensionless It is defined as the fraction of solar (shortwave) radiation reflected by the snow, across the solar spectrum, for both direct and diffuse radiation. It is a measure of the reflectivity of the snow covered grid cells. Values vary between 0 and 1. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above. Snow albedo dimensionless It is defined as the fraction of solar (shortwave) radiation reflected by the snow, across the solar spectrum, for both direct and diffuse radiation. It is a measure of the reflectivity of the snow covered grid cells. Values vary between 0 and 1. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above. Snow cover % It represents the fraction (0-1) of the cell / grid-box occupied by snow (similar to the cloud cover fields of ERA5). Snow cover % It represents the fraction (0-1) of the cell / grid-box occupied by snow (similar to the cloud cover fields of ERA5). Snow density kg m-3 Mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow density kg m-3 Mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow depth m Instantaneous grib-box average of the snow thickness on the ground (excluding snow on canopy). Snow depth m Instantaneous grib-box average of the snow thickness on the ground (excluding snow on canopy). Snow depth water equivalent m of water equivalent Depth of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow depth water equivalent m of water equivalent Depth of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow evaporation m of water equivalent Evaporation from snow averaged over the grid box (to find flux over snow, divide by snow fraction). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Snow evaporation m of water equivalent Evaporation from snow averaged over the grid box (to find flux over snow, divide by snow fraction). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Snowfall m of water equivalent Accumulated total snow that has fallen to the Earth's surface. It consists of snow due to the large-scale atmospheric flow (horizontal scales greater than around a few hundred metres) and convection where smaller scale areas (around 5km to a few hundred kilometres) of warm air rise. If snow has melted during the period over which this variable was accumulated, then it will be higher than the snow depth. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units given measure the depth the water would have if the snow melted and was spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Snowfall m of water equivalent Accumulated total snow that has fallen to the Earth's surface. It consists of snow due to the large-scale atmospheric flow (horizontal scales greater than around a few hundred metres) and convection where smaller scale areas (around 5km to a few hundred kilometres) of warm air rise. If snow has melted during the period over which this variable was accumulated, then it will be higher than the snow depth. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units given measure the depth the water would have if the snow melted and was spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Snowmelt m of water equivalent Melting of snow averaged over the grid box (to find melt over snow, divide by snow fraction). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Snowmelt m of water equivalent Melting of snow averaged over the grid box (to find melt over snow, divide by snow fraction). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. Soil temperature level 1 K Temperature of the soil in layer 1 (0 - 7 cm) of the ECMWF Integrated Forecasting System. The surface is at 0 cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 1 K Temperature of the soil in layer 1 (0 - 7 cm) of the ECMWF Integrated Forecasting System. The surface is at 0 cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K Temperature of the soil in layer 2 (7 -28cm) of the ECMWF Integrated Forecasting System. Soil temperature level 2 K Temperature of the soil in layer 2 (7 -28cm) of the ECMWF Integrated Forecasting System. Soil temperature level 3 K Temperature of the soil in layer 3 (28-100cm) of the ECMWF Integrated Forecasting System. Soil temperature level 3 K Temperature of the soil in layer 3 (28-100cm) of the ECMWF Integrated Forecasting System. Soil temperature level 4 K Temperature of the soil in layer 4 (100-289 cm) of the ECMWF Integrated Forecasting System. Soil temperature level 4 K Temperature of the soil in layer 4 (100-289 cm) of the ECMWF Integrated Forecasting System. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Surface latent heat flux J m-2 Exchange of latent heat with the surface through turbulent diffusion. This variables is accumulated from the beginning of the forecast time to the end of the forecast step. By model convention, downward fluxes are positive. Surface latent heat flux J m-2 Exchange of latent heat with the surface through turbulent diffusion. This variables is accumulated from the beginning of the forecast time to the end of the forecast step. By model convention, downward fluxes are positive. Surface net solar radiation J m-2 Amount of solar radiation (also known as shortwave radiation) reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation J m-2 Amount of solar radiation (also known as shortwave radiation) reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Net thermal radiation at the surface. Accumulated field from the beginning of the forecast time to the end of the forecast step. By model convention downward fluxes are positive. Surface net thermal radiation J m-2 Net thermal radiation at the surface. Accumulated field from the beginning of the forecast time to the end of the forecast step. By model convention downward fluxes are positive. Surface pressure Pa Pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water. It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this variable are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Surface pressure Pa Pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water. It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this variable are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation. Surface sensible heat flux J m-2 Transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This is a single level variable and it is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface sensible heat flux J m-2 Transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This is a single level variable and it is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 Amount of solar radiation (also known as shortwave radiation) reaching the surface of the Earth. This variable comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this variable). To a reasonably good approximation, this variable is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 Amount of solar radiation (also known as shortwave radiation) reaching the surface of the Earth. This variable comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this variable). To a reasonably good approximation, this variable is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 Amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches the Earth's surface. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this variable). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 Amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches the Earth's surface. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this variable). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Temperature of snow layer K This variable gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Temperature of snow layer K This variable gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Total evaporation m of water equivalent Accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This variable is accumulated from the beginning of the forecast to the end of the forecast step. The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Total evaporation m of water equivalent Accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This variable is accumulated from the beginning of the forecast to the end of the forecast step. The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Total precipitation m Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Total precipitation m Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. Volumetric soil water layer 1 m3 m-3 Volume of water in soil layer 1 (0 - 7 cm) of the ECMWF Integrated Forecasting System. The surface is at 0 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 m3 m-3 Volume of water in soil layer 1 (0 - 7 cm) of the ECMWF Integrated Forecasting System. The surface is at 0 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 Volume of water in soil layer 2 (7 -28 cm) of the ECMWF Integrated Forecasting System. Volumetric soil water layer 2 m3 m-3 Volume of water in soil layer 2 (7 -28 cm) of the ECMWF Integrated Forecasting System. Volumetric soil water layer 3 m3 m-3 Volume of water in soil layer 3 (28-100 cm) of the ECMWF Integrated Forecasting System. Volumetric soil water layer 3 m3 m-3 Volume of water in soil layer 3 (28-100 cm) of the ECMWF Integrated Forecasting System. Volumetric soil water layer 4 m3 m-3 Volume of water in soil layer 4 (100-289 cm) of the ECMWF Integrated Forecasting System. Volumetric soil water layer 4 m3 m-3 Volume of water in soil layer 4 (100-289 cm) of the ECMWF Integrated Forecasting System. 287 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/high-resolution-vegetation-phenology-and-productivity-4 https://www.wekeo.eu/data?view=viewer&t=1566840390697&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1JTkRJQ0VTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVklfUFBJXzEwTSIsInpJbmRleCI6NjB9LHsiaWQiOiJjMSIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1JTkRJQ0VTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVklfUUZMQUcyXzEwTSIsInpJbmRleCI6ODAsImlzSGlkZGVuIjp0cnVlfV0%3D&initial=1 High Resolution Vegetation Phenology and Productivity: Plant Phenology Index (raster 10m) version 1 revision 1, Sep. 2021 This metadata refers to the Plant Phenology Index (PPI) dataset, one of the near real-time (NRT) Vegetation Index products of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP), component of the Copernicus Land Monitoring Service (CLMS). The Plant Phenology Index (PPI) is a physically based vegetation index for improved monitoring of plant phenology, that is developed from a simplified solution to the radiative transfer equation by Jin and Eklundh (2014). PPI has a linear relationship with green leaf area index, a strong correlation with gross primary productivity, and is capable of disentangling remotely sensed plant phenology from snow seasonality. It is reported to be superior to other indices for spring phenology retrieval over the northern latitudes and for GPP estimation in African semi-arid ecosystems. Comparison of satellite-derived PPI to ground observations of plant phenology and gross primary productivity (GPP) shows strong similarity of temporal patterns over several Nordic boreal forest sites. The PPI dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for the period from October 2016 until today, with daily updates. Each file has an associated quality indicator (QFLAG2) to assist users with the screening of clouds, shadows from clouds and topography, snow and water surfaces. 288 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-real-time-situ-observations-objective http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_GLO_PHY_TS_OA_NRT_013_002 Global Ocean- Real time in-situ observations objective analysis Short description: For the Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the in-situ near real time database are produced monthly. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a support for localized experience (cruises), providing a validation source for operational models, observing seasonal cycle and inter-annual variability. DOI (product) :https://doi.org/10.48670/moi-00037 https://doi.org/10.48670/moi-00037 289 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-multiyear-ocean-colour-plankton-monthly http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_BAL_BGC_L4_MY_009_134 Baltic Sea Multiyear Ocean Colour Plankton monthly observations Short description: For the Baltic Sea Ocean Satellite Observations, the Italian National Research Council (CNR – Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets: * ''plankton'' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) Upstreams: SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv5) for the ""multi"" products, and OLCI-S3A & S3B for the ""olci"" products Temporal resolutions: monthly Spatial resolution: 1 km for ""multi"" and 300 meters for ""olci"" To find this product in the catalogue, use the search keyword ""OCEANCOLOUR_BAL_BGC_L4_MY"". DOI (product) :https://doi.org/10.48670/moi-00308 https://doi.org/10.48670/moi-00308 290 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-albedo https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-albedo satellite-albedo This dataset provides global Earth surface albedo products. The albedo has an impact on the amount of energy that Earth absorbs from the Sun and, therefore, is an important variable in climate and weather studies. The surface albedo quantifies the fraction of irradiance reflected by the surface of the Earth. It provides information on the radiative basis, thus on the temperature and water balance. The directional albedo or directional-hemispherical reflectance (also called black-sky albedo) is defined as the integration of the bi-directional reflectance over the viewing hemisphere. It assumes all energy is coming from a direct radiation from the sun and is computed for the local solar noon. The current algorithm is applied at a 10-day frequency on daily surface reflectances acquired during a moving temporal window of 20 days. It first normalizes the reflectances through inversion of a semi-empiric, kernel-driven reflectance model and favouring the most recent observations. Directional albedo is then computed, per spectral band, by integration over the viewing hemisphere and for local solar noon. Broadband albedo is then calculated from the spectral albedo by a linear relationship. Multiple platform/sensors were used offering various spatial resolution and temporal coverage (1 km from SPOT-VGT and PROBA-V, 4 km from AVHRR). Products are available from 1981 to present. DATA DESCRIPTION Data type Gridded Projection Plate Carrée projection Horizontal coverage Global land surface Horizontal resolution AVHRR: 1/30° (~4 km) VGT: 1/112° (~1 km) Sentinel-3: 1/336° (~300 m) Vertical coverage Top of the canopy Temporal coverage AVHRR: September 1981 to December 2005 SPOT-VGT: April 1998 to May 2014 PROBA-VGT: October 2013 to June 2020 Sentinel-3: July 2018 to April 2019 Temporal resolution 10 days File format NetCDF Versions V0 is a brokered dataset from the Copernicus Global Land Service (CGLS). V1 is produced by the Copernicus Climate Change Service (C3S). In this version uncertainty propagation was performed and the CDR dataset was extended in the past back to 1982 using the AVHRR2/3 NOAA series. V2 is produced by the C3S. This version builds on version 1 and adds a multi-sensor aspect to the albedo products delivered so far. The inversion of the BRDF coefficients was improved. A climatological BRDF based on SPOT-VGT is introduced as a priori information used for the inversion of the albedos of each sensor. This will increase the quality of the retrieval of the reflectance directional properties from the different sensors and improve the homogeneity among sensors. V3 is produced by the C3S. This version is built using the Sentinel3 A/B SLSTR and OLCI dataset and includes more spectral albedo bands compared to v2 and v1. V3 is a pre-operational version that may be replaced by an operational version when available. Update frequency Monthly updates DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Plate Carrée projection Projection Plate Carrée projection Horizontal coverage Global land surface Horizontal coverage Global land surface Horizontal resolution AVHRR: 1/30° (~4 km) VGT: 1/112° (~1 km) Sentinel-3: 1/336° (~300 m) Horizontal resolution AVHRR: 1/30° (~4 km) VGT: 1/112° (~1 km) Sentinel-3: 1/336° (~300 m) AVHRR: 1/30° (~4 km) VGT: 1/112° (~1 km) Sentinel-3: 1/336° (~300 m) Vertical coverage Top of the canopy Vertical coverage Top of the canopy Temporal coverage AVHRR: September 1981 to December 2005 SPOT-VGT: April 1998 to May 2014 PROBA-VGT: October 2013 to June 2020 Sentinel-3: July 2018 to April 2019 Temporal coverage AVHRR: September 1981 to December 2005 SPOT-VGT: April 1998 to May 2014 PROBA-VGT: October 2013 to June 2020 Sentinel-3: July 2018 to April 2019 AVHRR: September 1981 to December 2005 SPOT-VGT: April 1998 to May 2014 PROBA-VGT: October 2013 to June 2020 Sentinel-3: July 2018 to April 2019 Temporal resolution 10 days Temporal resolution 10 days File format NetCDF File format NetCDF Versions V0 is a brokered dataset from the Copernicus Global Land Service (CGLS). V1 is produced by the Copernicus Climate Change Service (C3S). In this version uncertainty propagation was performed and the CDR dataset was extended in the past back to 1982 using the AVHRR2/3 NOAA series. V2 is produced by the C3S. This version builds on version 1 and adds a multi-sensor aspect to the albedo products delivered so far. The inversion of the BRDF coefficients was improved. A climatological BRDF based on SPOT-VGT is introduced as a priori information used for the inversion of the albedos of each sensor. This will increase the quality of the retrieval of the reflectance directional properties from the different sensors and improve the homogeneity among sensors. V3 is produced by the C3S. This version is built using the Sentinel3 A/B SLSTR and OLCI dataset and includes more spectral albedo bands compared to v2 and v1. V3 is a pre-operational version that may be replaced by an operational version when available. Versions V0 is a brokered dataset from the Copernicus Global Land Service (CGLS). V1 is produced by the Copernicus Climate Change Service (C3S). In this version uncertainty propagation was performed and the CDR dataset was extended in the past back to 1982 using the AVHRR2/3 NOAA series. V2 is produced by the C3S. This version builds on version 1 and adds a multi-sensor aspect to the albedo products delivered so far. The inversion of the BRDF coefficients was improved. A climatological BRDF based on SPOT-VGT is introduced as a priori information used for the inversion of the albedos of each sensor. This will increase the quality of the retrieval of the reflectance directional properties from the different sensors and improve the homogeneity among sensors. V3 is produced by the C3S. This version is built using the Sentinel3 A/B SLSTR and OLCI dataset and includes more spectral albedo bands compared to v2 and v1. V3 is a pre-operational version that may be replaced by an operational version when available. V0 is a brokered dataset from the Copernicus Global Land Service (CGLS). V1 is produced by the Copernicus Climate Change Service (C3S). In this version uncertainty propagation was performed and the CDR dataset was extended in the past back to 1982 using the AVHRR2/3 NOAA series. V2 is produced by the C3S. This version builds on version 1 and adds a multi-sensor aspect to the albedo products delivered so far. The inversion of the BRDF coefficients was improved. A climatological BRDF based on SPOT-VGT is introduced as a priori information used for the inversion of the albedos of each sensor. This will increase the quality of the retrieval of the reflectance directional properties from the different sensors and improve the homogeneity among sensors. V3 is produced by the C3S. This version is built using the Sentinel3 A/B SLSTR and OLCI dataset and includes more spectral albedo bands compared to v2 and v1. V3 is a pre-operational version that may be replaced by an operational version when available. Update frequency Monthly updates Update frequency Monthly updates MAIN VARIABLES Name Units Description Broadband directional surface albedo (ALBB-DH) Dimensionless Integration of the Bidirectional Reflectance Distribution Function (BRDF) over the viewing hemisphere. It assumes that all the solar energy is coming in the form of a direct radiation from the sun. Also called black-sky albedo. The integration is computed over visible band [0.4-0.7µm], near infrared band [0.7-4µm] and over total spectrum [0.4-4µm]. Broadband hemispherical surface albedo (ALBB-BH) Dimensionless Integration of the directional albedo over the illumination hemisphere. It assumes a complete diffuse illumination. Also called white-sky albedo. The integration is computed over visible band [0.4-0.7µm], near infrared band [0.7-4µm] and over total spectrum [0.4-4µm]. Spectral directional surface albedo (ALSP-DH) Dimensionless Identical to ALBB-DH but values are given in function of the wavelength. Spectral hemispherical surface albedo (ALSP-BH) Dimensionless Identical to ALBB-BH but values are given in function of the wavelength. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Broadband directional surface albedo (ALBB-DH) Dimensionless Integration of the Bidirectional Reflectance Distribution Function (BRDF) over the viewing hemisphere. It assumes that all the solar energy is coming in the form of a direct radiation from the sun. Also called black-sky albedo. The integration is computed over visible band [0.4-0.7µm], near infrared band [0.7-4µm] and over total spectrum [0.4-4µm]. Broadband directional surface albedo (ALBB-DH) Dimensionless Integration of the Bidirectional Reflectance Distribution Function (BRDF) over the viewing hemisphere. It assumes that all the solar energy is coming in the form of a direct radiation from the sun. Also called black-sky albedo. The integration is computed over visible band [0.4-0.7µm], near infrared band [0.7-4µm] and over total spectrum [0.4-4µm]. Broadband hemispherical surface albedo (ALBB-BH) Dimensionless Integration of the directional albedo over the illumination hemisphere. It assumes a complete diffuse illumination. Also called white-sky albedo. The integration is computed over visible band [0.4-0.7µm], near infrared band [0.7-4µm] and over total spectrum [0.4-4µm]. Broadband hemispherical surface albedo (ALBB-BH) Dimensionless Integration of the directional albedo over the illumination hemisphere. It assumes a complete diffuse illumination. Also called white-sky albedo. The integration is computed over visible band [0.4-0.7µm], near infrared band [0.7-4µm] and over total spectrum [0.4-4µm]. Spectral directional surface albedo (ALSP-DH) Dimensionless Identical to ALBB-DH but values are given in function of the wavelength. Spectral directional surface albedo (ALSP-DH) Dimensionless Identical to ALBB-DH but values are given in function of the wavelength. Spectral hemispherical surface albedo (ALSP-BH) Dimensionless Identical to ALBB-BH but values are given in function of the wavelength. Spectral hemispherical surface albedo (ALSP-BH) Dimensionless Identical to ALBB-BH but values are given in function of the wavelength. RELATED VARIABLES Along with the main variables the following variables are included: error (uncertainty on every band in the variable); QFLAG (the quality flag of the product); NMOD (the number of valid observations during the 20-day synthesis period that are used to calculate the surface albedo); AGE (the age of the observation in days). RELATED VARIABLES RELATED VARIABLES Along with the main variables the following variables are included: error (uncertainty on every band in the variable); QFLAG (the quality flag of the product); NMOD (the number of valid observations during the 20-day synthesis period that are used to calculate the surface albedo); AGE (the age of the observation in days). Along with the main variables the following variables are included: error (uncertainty on every band in the variable); QFLAG (the quality flag of the product); NMOD (the number of valid observations during the 20-day synthesis period that are used to calculate the surface albedo); AGE (the age of the observation in days). 291 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-cerra-height-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-cerra-height-levels reanalysis-cerra-height-levels The Copernicus European Regional ReAnalysis (CERRA) datasets provide spatially and temporally consistent historical reconstructions of meteorological variables in the atmosphere and at the surface. There are four subsets: single levels (atmospheric and surface quantities), height levels (upper-air fields up to 500m), pressure levels (upper-air fields up to 1hPa) and model levels (native levels of the model). This entry provides reanalysis and forecast data on height levels for Europe from 1984 to present. One reason to provide atmospheric variables on height levels is for applications in the wind energy sector. Several atmospheric parameters are common to both reanalysis and forecast (e.g. temperature, wind), whilst others are produced only by the forecast model (e.g. specific rain water content). Reanalysis combines model data with observations into a complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved, reprocessed versions of the original observations, which all benefit the quality of the reanalysis product. The CERRA dataset was produced using the HARMONIE-ALADIN limited-area numerical weather prediction and data assimilation system, hereafter referred to as the CERRA system. The CERRA system employs a 3-dimensional variational data assimilation scheme of the atmospheric state at every assimilation time. The reanalysis dataset is convenient owing to its provision of atmospheric estimates at each model domain grid point over Europe for each regular output time, over a long period, and always using the same data format. The inputs to CERRA reanalysis are the observational data, lateral boundary conditions from ERA5 global reanalysis as prior estimates of the atmospheric state and physiographic datasets describing the surface characteristics of the model. The observing system has evolved over time, and although the data assimilation system can resolve data holes, the much sparser observational networks in the past periods (for example, a reduced amount of satellite data in the 1980s) can impact the quality of analyses leading to less accurate estimates. The uncertainty estimates for reanalysis variables are provided by the CERRA-EDA, a 10-member ensemble of data assimilation system. The added value of the CERRA data with respect to the global reanalysis products is expected to come, for example, with the higher horizontal resolution that permits the usage of a better description of the model topography and physiographic data, and the assimilation of more surface observations. More information about the CERRA dataset can be found in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Lambert conformal conic Horizontal coverage Europe. The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Horizontal resolution 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members Vertical coverage 11 height levels (from 15m up to 500m) Vertical resolution 15, 30, 50, 75, 100, 150, 200, 250, 300, 400 and 500m Temporal coverage September 1984 to June 2021 Temporal resolution Analysis data: 3-hourly for high-resolution, 6-hourly for ensemble members Forecast data: hourly for forecast range 1 - 6 (high-resolution and ensemble members), 3-hourly for forecast range 6 - 30 (high-resolution only) File format GRIB2 Update frequency New data will be added towards the end of 2023 DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert conformal conic Projection Lambert conformal conic Horizontal coverage Europe. The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Horizontal coverage Europe. The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Horizontal resolution 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members Horizontal resolution 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members Vertical coverage 11 height levels (from 15m up to 500m) Vertical coverage 11 height levels (from 15m up to 500m) Vertical resolution 15, 30, 50, 75, 100, 150, 200, 250, 300, 400 and 500m Vertical resolution 15, 30, 50, 75, 100, 150, 200, 250, 300, 400 and 500m Temporal coverage September 1984 to June 2021 Temporal coverage September 1984 to June 2021 Temporal resolution Analysis data: 3-hourly for high-resolution, 6-hourly for ensemble members Forecast data: hourly for forecast range 1 - 6 (high-resolution and ensemble members), 3-hourly for forecast range 6 - 30 (high-resolution only) Temporal resolution Analysis data: 3-hourly for high-resolution, 6-hourly for ensemble members Forecast data: hourly for forecast range 1 - 6 (high-resolution and ensemble members), 3-hourly for forecast range 6 - 30 (high-resolution only) Analysis data: 3-hourly for high-resolution, 6-hourly for ensemble members Forecast data: hourly for forecast range 1 - 6 (high-resolution and ensemble members), 3-hourly for forecast range 6 - 30 (high-resolution only) File format GRIB2 File format GRIB2 Update frequency New data will be added towards the end of 2023 Update frequency New data will be added towards the end of 2023 MAIN VARIABLES Name Units Description Pressure Pa The pressure is the air pressure at a certain height (15m-500m) above the surface valid for the grid area. The pressure is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Relative humidity % The relative humidity is the modelled humidity valid for the grid area determined at a certain height (15m-500m) above the surface. The parameter is given % ranging from 0-100. 0% means that the air is totally dry whereas 100% indicates that the air is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. Surface air relative humidity is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Specific cloud ice water content kg kg-1 Specific cloud ice water content is the grid-box mean ice water content (mass of condensate / mass of moist air) on a height level. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Specific cloud liquid water content kg kg-1 Specific cloud liquid water content is the grid-box mean liquid water content (mass of condensate / mass of moist air) on a height level. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Specific rain water content kg kg-1 The mass of water that is of raindrop size and so can fall to the surface as precipitation. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) that can fall to the surface as precipitation. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Temperature K The temperature is the air temperature valid for the grid area determined at a certain height (15m-500m) above the surface. The temperature is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Turbulent kinetic energy J kg-1 The turbulent kinetic energy is the mean kinetic energy per unit mass associated with eddies in turbulent flow. This parameter describes the turbulent kinetic energy at a certain height (15m-500m) above the surface and is valid for the grid area. The turbulent kinetic energy is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Wind direction degree The wind direction is the wind direction valid for the grid area determined for a certain height (15m-500m) above the surface. The parameter is given in degrees ranging from 0-360. Here, 0° means a northerly wind and 90° indicates an easterly wind. The wind direction is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Wind speed m s-1 Wind speed is the wind speed valid for the grid area determined for a certain height (15m-500m) above the surface. It is computed from both the zonal (u) and the meridional (v) wind components by wind speed = u2 + v2 . The wind speed is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Pressure Pa The pressure is the air pressure at a certain height (15m-500m) above the surface valid for the grid area. The pressure is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Pressure Pa The pressure is the air pressure at a certain height (15m-500m) above the surface valid for the grid area. The pressure is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Relative humidity % The relative humidity is the modelled humidity valid for the grid area determined at a certain height (15m-500m) above the surface. The parameter is given % ranging from 0-100. 0% means that the air is totally dry whereas 100% indicates that the air is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. Surface air relative humidity is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Relative humidity % The relative humidity is the modelled humidity valid for the grid area determined at a certain height (15m-500m) above the surface. The parameter is given % ranging from 0-100. 0% means that the air is totally dry whereas 100% indicates that the air is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. Surface air relative humidity is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Specific cloud ice water content kg kg-1 Specific cloud ice water content is the grid-box mean ice water content (mass of condensate / mass of moist air) on a height level. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Specific cloud ice water content kg kg-1 Specific cloud ice water content is the grid-box mean ice water content (mass of condensate / mass of moist air) on a height level. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Specific cloud liquid water content kg kg-1 Specific cloud liquid water content is the grid-box mean liquid water content (mass of condensate / mass of moist air) on a height level. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Specific cloud liquid water content kg kg-1 Specific cloud liquid water content is the grid-box mean liquid water content (mass of condensate / mass of moist air) on a height level. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Specific rain water content kg kg-1 The mass of water that is of raindrop size and so can fall to the surface as precipitation. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Specific rain water content kg kg-1 The mass of water that is of raindrop size and so can fall to the surface as precipitation. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) that can fall to the surface as precipitation. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) that can fall to the surface as precipitation. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Temperature K The temperature is the air temperature valid for the grid area determined at a certain height (15m-500m) above the surface. The temperature is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Temperature K The temperature is the air temperature valid for the grid area determined at a certain height (15m-500m) above the surface. The temperature is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Turbulent kinetic energy J kg-1 The turbulent kinetic energy is the mean kinetic energy per unit mass associated with eddies in turbulent flow. This parameter describes the turbulent kinetic energy at a certain height (15m-500m) above the surface and is valid for the grid area. The turbulent kinetic energy is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Turbulent kinetic energy J kg-1 The turbulent kinetic energy is the mean kinetic energy per unit mass associated with eddies in turbulent flow. This parameter describes the turbulent kinetic energy at a certain height (15m-500m) above the surface and is valid for the grid area. The turbulent kinetic energy is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Wind direction degree The wind direction is the wind direction valid for the grid area determined for a certain height (15m-500m) above the surface. The parameter is given in degrees ranging from 0-360. Here, 0° means a northerly wind and 90° indicates an easterly wind. The wind direction is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Wind direction degree The wind direction is the wind direction valid for the grid area determined for a certain height (15m-500m) above the surface. The parameter is given in degrees ranging from 0-360. Here, 0° means a northerly wind and 90° indicates an easterly wind. The wind direction is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Wind speed m s-1 Wind speed is the wind speed valid for the grid area determined for a certain height (15m-500m) above the surface. It is computed from both the zonal (u) and the meridional (v) wind components by wind speed = u2 + v2 . The wind speed is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Wind speed m s-1 Wind speed is the wind speed valid for the grid area determined for a certain height (15m-500m) above the surface. It is computed from both the zonal (u) and the meridional (v) wind components by wind speed = u2 + v2 . The wind speed is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. 292 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/vegetation-productivity-index-2013-2020-raster-1-km-0 http://land.copernicus.eu/global/products/vpi Vegetation Productivity Index 2013-2020 (raster 1 km), global tiles, 10-daily - version 1 The Vegetation Productivity Indicator (VPI) is a categorical type of difference vegetation index, whereby the actual NDVI is referenced against the NDVI percentiles of the historical year. The VPI is typically used to assess overall vegetation condition and to identify areas with below normal vegetation development, possibly linked to low agricultural productivity, as compared to what can be expected based on the historical range. Furthermore, VPI can also be used to identify drought affected areas. 293 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/vegetation-productivity-index-2013-2020-raster-1-km http://land.copernicus.eu/global/products/vpi Vegetation Productivity Index 2013-2020 (raster 1 km), global continents, 10-daily - version 1 The Vegetation Productivity Indicator (VPI) is a categorical type of difference vegetation index, whereby the actual NDVI is referenced against the NDVI percentiles of the historical year. The VPI is typically used to assess overall vegetation condition and to identify areas with below normal vegetation development, possibly linked to low agricultural productivity, as compared to what can be expected based on the historical range. Furthermore, VPI can also be used to identify drought affected areas. 294 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-west-shelf-mean-sea-level-extreme-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_VAR_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs North West Shelf Mean Sea Level extreme from Observations Reprocessing DEFINITION The OMI_VAR_EXTREME_SL_NORTHWESTSHELF_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset northwestshelf_omi_sl_extreme_var_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). CONTEXT Sea level is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990’s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one metre by the end of the century (Vousdoukas et al., 2020). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves. CMEMS KEY FINDINGS The completeness index criteria is fulfilled in this region by 23 stations, a significant increase with respect to those used in 2019 (only 6). Most of these new stations belong to UK and Denmark, and their reprocessed timeseries are now provided in product INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053. The mean 99th percentiles present a large spatial variability related to the tidal pattern, ranging from the 3.08 m and 3.38 m above mean sea level in Immingan (East England) and Calais (France, English Channel) respectively, to 0.59 m above mean sea level in Aarhus (Denmark). The standard deviation ranges between 3 and 8 cm. There is a clear positive anomaly of 99th percentiles in 2020 for most of the stations, reaching 11 cm in Kungsvik (Sweden) and Ullapool (Scotland). Null or very small negative anomalies are only observed at two stations in the southeastern coast of England. DOI (product):https://doi.org/10.48670/moi-00272 https://doi.org/10.48670/moi-00272 295 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-high-resolution-l4-sea-surface http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_MED_SST_L4_REP_OBSERVATIONS_010_021 Mediterranean Sea - High Resolution L4 Sea Surface Temperature Reprocessed Short description: The CMEMS Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05° resolution grid covering the period from January 1st 1982 to present (currently, up to six months before real time). The MED-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record provided by the ESA Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) initiatives, but also includes in input an adjusted version of the AVHRR Pathfinder dataset version 5.3 to increase the input observation coverage. DOI (product) :https://doi.org/10.48670/moi-00173 https://doi.org/10.48670/moi-00173 296 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-solar-radiation-timeseries https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-solar-radiation-timeseries cams-solar-radiation-timeseries The CAMS solar radiation services provide historical values (2004 to present) of global (GHI), direct (BHI) and diffuse (DHI) solar irradiation, as well as direct normal irradiation (BNI). The aim is to fulfil the needs of European and national policy development and the requirements of both commercial and public downstream services, e.g. for planning, monitoring, efficiency improvements and the integration of solar energy systems into energy supply grids. For clear-sky conditions, an irradiation time series is provided for any location in the world using information on aerosol, ozone and water vapour from the CAMS global forecasting system. Other properties, such as ground albedo and ground elevation, are also taken into account. Similar time series are available for cloudy (or "all sky") conditions but, since the high-resolution cloud information is directly inferred from satellite observations, these are currently only available inside the field-of-view of the Meteosat Second Generation (MSG) satellite, which is roughly Europe, Africa, the Atlantic Ocean and the Middle East. Data is offered in both ASCII and netCDF format. Additionally, an ASCII "expert mode" format can be selected which contains in addition to the irradiation, all the input data used in their calculation (aerosol optical properties, water vapour concentration, etc). This additional information is only meaningful in the time frame at which the calculation is performed and so is only available at 1-minute time steps in universal time (UT). More details about the products are given in the Documentation section. DATA DESCRIPTION Data type time series at specified point Horizontal coverage Global for cloud-free parameters, Meteosat satellite field-of-view for all-sky parameters Horizontal resolution interpolated to the point of interest from various input data spatial resolutions Temporal coverage February 2004 to present Temporal resolution 1-minute, 15-minute, 1-hourly, daily, monthly File format CSV, netCDF DATA DESCRIPTION DATA DESCRIPTION Data type time series at specified point Data type time series at specified point Horizontal coverage Global for cloud-free parameters, Meteosat satellite field-of-view for all-sky parameters Horizontal coverage Global for cloud-free parameters, Meteosat satellite field-of-view for all-sky parameters Horizontal resolution interpolated to the point of interest from various input data spatial resolutions Horizontal resolution interpolated to the point of interest from various input data spatial resolutions Temporal coverage February 2004 to present Temporal coverage February 2004 to present Temporal resolution 1-minute, 15-minute, 1-hourly, daily, monthly Temporal resolution 1-minute, 15-minute, 1-hourly, daily, monthly File format CSV, netCDF File format CSV, netCDF MAIN VARIABLES Name Units Description BHI Wh m-2 Direct horizontal all sky irradiation BHIc Wh m-2 Direct horizontal clear sky irradiation BNI Wh m-2 Direct normal all sky irradiation BNIc Wh m-2 Direct normal clear sky irradiation DHI Wh m-2 Diffuse horizontal all sky irradiation DHIc Wh m-2 Diffuse horizontal clear sky irradiation GHI Wh m-2 Global horizontal all sky irradiation GHIc Wh m-2 Global horizontal clear sky irradiation MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description BHI Wh m-2 Direct horizontal all sky irradiation BHI Wh m-2 Direct horizontal all sky irradiation BHIc Wh m-2 Direct horizontal clear sky irradiation BHIc Wh m-2 Direct horizontal clear sky irradiation BNI Wh m-2 Direct normal all sky irradiation BNI Wh m-2 Direct normal all sky irradiation BNIc Wh m-2 Direct normal clear sky irradiation BNIc Wh m-2 Direct normal clear sky irradiation DHI Wh m-2 Diffuse horizontal all sky irradiation DHI Wh m-2 Diffuse horizontal all sky irradiation DHIc Wh m-2 Diffuse horizontal clear sky irradiation DHIc Wh m-2 Diffuse horizontal clear sky irradiation GHI Wh m-2 Global horizontal all sky irradiation GHI Wh m-2 Global horizontal all sky irradiation GHIc Wh m-2 Global horizontal clear sky irradiation GHIc Wh m-2 Global horizontal clear sky irradiation 297 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-sea-and-ice-surface-temperature http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_008 Arctic Ocean - Sea and Ice Surface Temperature Short description: Arctic Sea and Ice surface temperature product based upon observations from the Metop_A AVHRR instrument. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone. DOI (product) :https://doi.org/10.48670/moi-00130 https://doi.org/10.48670/moi-00130 298 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/antarctic-monthly-sea-ice-extent-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=ANTARCTIC_OMI_SI_extent_obs Antarctic Monthly Sea Ice Extent from Observations Reprocessing DEFINITION Sea Ice Extent (SIE) is defined as the area covered by sufficient sea ice, that is the area of ocean having more than 15% Sea Ice Concentration (SIC). SIC is the fractional area of ocean surface that is covered with sea ice. SIC is computed from Passive Microwave satellite observations since 1979. SIE is often reported with units of 106 km2 (millions square kilometers). The change in sea ice extent (trend) is expressed in millions of km squared per decade (106 km2/decade). In addition, trends are expressed relative to the 1979-2021 period in % per decade. These trends are calculated (i) from the annual mean values; (ii) from the September values (winter ice loss); (iii) from February values (summer ice loss). The annual mean trend is reported on the key figure, the September (maximum extent) and February (minimum extent) values are reported in the text below. SIE includes all sea ice, except for lake and river ice. See also section 1.7 in Samuelsen et al. (2016) for an introduction to this Ocean Monitoring Indicator (OMI). CONTEXT Sea ice is frozen seawater that floats at the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and by how much the sea-ice cover is changing is essential for monitoring the health of the Earth (Meredith et al. 2019). CMEMS KEY FINDINGS Since 1979, there has been a slight increase of sea ice extent in the Southern Hemisphere. While the period 2016-2019 was characterized by much lower values, year 2020 was mostly back on the long-term trend. Over the period 1979-2021 , the annual rate amounts to +0.07 +/- 0.05 106 km2 per decade (+0.62% per decade). Winter (September) sea ice extent trend amounts to +0.10 +/- 0.06 106 km2 per decade (+0.55% per decade). Summer (February) sea ice extent trend amounts to +0.03 +/- 0.05 106 km2 per decade (+0.76% per decade). These trend estimates are hardly significant, which is in agreement with the IPCC SROCC, which has assessed that ‘Antarctic sea ice extent overall has had no statistically significant trend (1979–2018) due to contrasting regional signals and large interannual variability (high confidence).’ (IPCC, 2019). Sea ice extent in 2021 was slightly above the 1979-2021 average between March and August DOI (product):https://doi.org/10.48670/moi-00187 https://doi.org/10.48670/moi-00187 299 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/riparian-zones-land-coverland-use-2018-vector-europe-6 https://land.copernicus.eu/local/riparian-zones/riparian-zones-2018 Riparian Zones Land Cover/Land Use 2018 (vector), Europe, 6-yearly, Dec. 2021 Riparian zones represent transitional areas occurring between land and freshwater ecosystems, characterised by distinctive hydrology, soil and biotic conditions and strongly influenced by the stream water. They provide a wide range of riparian functions (e.g. chemical filtration, flood control, bank stabilization, aquatic life and riparian wildlife support, etc.) and ecosystem services. The Riparian Zones products support the objectives of several European legal acts and policy initiatives, such as the EU Biodiversity Strategy to 2020, the Habitats and Birds Directives and the Water Framework Directive. This metadata refers to the Riparian Zones 2018 Land Cover/Land Use (LC/LU), which LC/LU classification is tailored to the needs of biodiversity monitoring in a variable buffer zone of selected rivers (Strahler levels 2-9 derived from EU-Hydro) for the reference year 2018. LC/LU is extracted from Very High Resolution (VHR) satellite data and other available data in a buffer zone of selected rivers for supporting biodiversity monitoring and mapping and assessment of ecosystems and their services. The class definitions follow the pre-defined nomenclature on the basis of Mapping and Assessment of Ecosystems and their Services (MAES) typology of ecosystems (Level 1 to Level 4) and CORINE Land Cover. The classification provides 55 distinct thematic classes with a Minimum Mapping Unit (MMU) of 0.5 ha and a Minimum Mapping Width (MMW) of 10 m. The production of the Riparian Zones products was coordinated by the European Environment Agency in the frame of the EU Copernicus programme. 300 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-extreme-precipitation-catalogue-past-events https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-extreme-precipitation-catalogue-past-events app-extreme-precipitation-catalogue-past-events This application allows users to explore a catalogue of historical extreme precipitation events across Europe. The magnitude and likely impacted area of the extreme events are presented, along with information on the reported financial damages and losses. The damage and loss information have been compiled from multiple external repositories and collated in a uniform manner to be presented in conjunction with the climatic data. The extreme precipitation events are detected and ranked using a standardised daily precipitation amount over the 95th or 99th percentile of wet days (daily precipitation >= 1mm). This indicator, available in the Extreme precipitation risk indicators for Europe and European cities from 1950 to 2019 dataset, provides a gridded product capable of identifying past extreme precipitation events for each grid point across Europe in a standardised manner. This allows users to explore various spatial and temporal scales and extract specific information for the given time period and region. Extreme precipitation risk indicators for Europe and European cities from 1950 to 2019 The interactive map displays the 95th percentile of precipitation for either ERA5 or E-Obs (depending on user selection) for the period 1989-2018. Users can pan around Europe or zoom in to select one of the Nomenclature of Territorial Units for Statistics (NUTS) regions (national, level 1 or level 2) to open a focused view for that region. The focused view presents the standardized precipitation amount over the 95th percentile for each extreme event in the user-selected time period. Also available is a table reporting information about the extreme events for each day. Note: the user is informed for the instances that no data is available. User-selectable parameters User-selectable parameters Extreme precipitation indicators computed from ERA5 or E-OBS data sources; The requested time period (start date/end date) for which the events should be extracted; NUTS 0/NUTS 1/NUTS 2 territorial units across Europe. Extreme precipitation indicators computed from ERA5 or E-OBS data sources; The requested time period (start date/end date) for which the events should be extracted; NUTS 0/NUTS 1/NUTS 2 territorial units across Europe. INPUT VARIABLES Name Units Description Source Precipitation at fixed percentiles kg m-2 Total precipitation when daily precipitation amounts exceed the 95th percentiles in wet days (daily precipitation ≥ 1 mm) computed over the 30-year period (1989-2018). Extreme precipitation indicators for Europe Standardised precipitation exceeding fixed percentiles Dimensionless Standardised daily precipitation amount over the grid point's 95th or 99th percentile of wet days (daily precipitation ≥ 1 mm). Values are decimal, ranging between 0-17 (95th percentile) or 0-10 (99th percentile). These values may be used to detect and rank extreme precipitation events. Extreme precipitation indicators for Europe INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Precipitation at fixed percentiles kg m-2 Total precipitation when daily precipitation amounts exceed the 95th percentiles in wet days (daily precipitation ≥ 1 mm) computed over the 30-year period (1989-2018). Extreme precipitation indicators for Europe Precipitation at fixed percentiles kg m-2 Total precipitation when daily precipitation amounts exceed the 95th percentiles in wet days (daily precipitation ≥ 1 mm) computed over the 30-year period (1989-2018). Extreme precipitation indicators for Europe Extreme precipitation indicators for Europe Standardised precipitation exceeding fixed percentiles Dimensionless Standardised daily precipitation amount over the grid point's 95th or 99th percentile of wet days (daily precipitation ≥ 1 mm). Values are decimal, ranging between 0-17 (95th percentile) or 0-10 (99th percentile). These values may be used to detect and rank extreme precipitation events. Extreme precipitation indicators for Europe Standardised precipitation exceeding fixed percentiles Dimensionless Standardised daily precipitation amount over the grid point's 95th or 99th percentile of wet days (daily precipitation ≥ 1 mm). Values are decimal, ranging between 0-17 (95th percentile) or 0-10 (99th percentile). These values may be used to detect and rank extreme precipitation events. Extreme precipitation indicators for Europe Extreme precipitation indicators for Europe OUTPUT VARIABLES Name Units Description Area Dimensionless Fraction of area potentially affected by the extreme event over selected NUTS region. It is a dimensionless value obtained as the ratio between the number (n) of grid points with nrr95p≥1 and the number of grid points (N) falling within the NUTS. The area is computed when nr99p≥1 at least for one grid point. End date dd/mm/yyyy Last day of the recorded event Fatalities Number of people Number of casualties/deaths attributed to the event Flood source - If recorded, water bodies that gave rise to the flood Losses million EUR, 2019 Deflated reported economic damage Magnitude Dimensionless Magnitude of the recorded event over selected NUTS region. It is a dimensionless value obtained by averaging the nrr95p for grid points with nrr95p≥1 falling within the NUTS. The magnitude is computed when nr99p≥1 at least for one grid point Persons affected Number of people Number of people affected attributed to the event Rank Dimensionless R provides the rank of the event over the selected NUTS. It is obtained by multiplying the area potentially affected by an event by its magnitude Regions affected Number of regions NUTS 31 regions reported as affected Start date dd/mm/yyyy First day of the recorded event Type - "Flash" for pluvial or "River" for fluvial flood OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Area Dimensionless Fraction of area potentially affected by the extreme event over selected NUTS region. It is a dimensionless value obtained as the ratio between the number (n) of grid points with nrr95p≥1 and the number of grid points (N) falling within the NUTS. The area is computed when nr99p≥1 at least for one grid point. Area Dimensionless Fraction of area potentially affected by the extreme event over selected NUTS region. It is a dimensionless value obtained as the ratio between the number (n) of grid points with nrr95p≥1 and the number of grid points (N) falling within the NUTS. The area is computed when nr99p≥1 at least for one grid point. End date dd/mm/yyyy Last day of the recorded event End date dd/mm/yyyy Last day of the recorded event Fatalities Number of people Number of casualties/deaths attributed to the event Fatalities Number of people Number of casualties/deaths attributed to the event Flood source - If recorded, water bodies that gave rise to the flood Flood source - If recorded, water bodies that gave rise to the flood Losses million EUR, 2019 Deflated reported economic damage Losses million EUR, 2019 Deflated reported economic damage Magnitude Dimensionless Magnitude of the recorded event over selected NUTS region. It is a dimensionless value obtained by averaging the nrr95p for grid points with nrr95p≥1 falling within the NUTS. The magnitude is computed when nr99p≥1 at least for one grid point Magnitude Dimensionless Magnitude of the recorded event over selected NUTS region. It is a dimensionless value obtained by averaging the nrr95p for grid points with nrr95p≥1 falling within the NUTS. The magnitude is computed when nr99p≥1 at least for one grid point Persons affected Number of people Number of people affected attributed to the event Persons affected Number of people Number of people affected attributed to the event Rank Dimensionless R provides the rank of the event over the selected NUTS. It is obtained by multiplying the area potentially affected by an event by its magnitude Rank Dimensionless R provides the rank of the event over the selected NUTS. It is obtained by multiplying the area potentially affected by an event by its magnitude Regions affected Number of regions NUTS 31 regions reported as affected Regions affected Number of regions NUTS 31 regions reported as affected Start date dd/mm/yyyy First day of the recorded event Start date dd/mm/yyyy First day of the recorded event Type - "Flash" for pluvial or "River" for fluvial flood Type - "Flash" for pluvial or "River" for fluvial flood 301 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/south-atlantic-gyre-area-chlorophyll-time-series-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_oligo_sag_area_mean South Atlantic Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing DEFINITION Oligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data inside the gyre. Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021). CONTEXT Oligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth’s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. CMEMS KEY FINDINGS The trend in the South Altantic gyre area for the 1997 Sept – 2021 December period was positive, with a 0.01% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 0.09% rate for the 1997-2020 trend (though within the uncertainties associated with the two estimates) and is not statistically significant (p>0.05). During the 1997 Sept – 2021 December period, the trend in chlorophyll concentration was positive (0.73% year-1) relative to 2000-01-01 values. This is a significant increase from the trend of 0.35% year-1 for the 1997-2020 period and is statistically significant (p<0.05). The last two years of the timeseries show an increased deviation from the mean. DOI (product):https://doi.org/10.48670/moi-00228 https://doi.org/10.48670/moi-00228 302 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-ocean-colour-plankton-my-l4-daily http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_MED_BGC_L4_MY_009_144 Mediterranean Sea Ocean Colour Plankton MY L4 daily gapfree observations and climatology and monthly observations Short description: For the Mediterranean Sea Ocean Satellite Observations, the Italian National Research Council (CNR – Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets: * ''plankton'' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004), and the interpolated gap-free Chl concentration (to provide a ""cloud free"" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided. Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A for the ""multi"" products, and OLCI-S3A & S3B for the ""olci"" products Temporal resolutions: monthly and daily (for ""gap-free"" and climatology data) Spatial resolution: 1 km for ""multi"" and 300 meters for ""olci"" To find this product in the catalogue, use the search keyword ""OCEANCOLOUR_MED_BGC_L4_MY"". DOI (product) :https://doi.org/10.48670/moi-00300 https://doi.org/10.48670/moi-00300 303 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-shipping-arctic https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-shipping-arctic sis-shipping-arctic As part of the C3S Global Shipping service, sea ice condition climate projections were used to derive indicators which are useful to the global shipping industry. The derived indicators are gathered in the present catalogue and distributed as monthly decadal projections. The skill of 25 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projection models was assessed against the historical sea ice concentration from ERA-Interim. The 5 most skillful CMIP5 models were used (relative root mean square error < 10%) to derive the present indicators. Both RCP4.5 and RCP8.5 CMIP5 experiments were considered. Two different types of indicators were produced. The first type provides information about the ice and accessibility conditions over the whole arctic ocean and includes: Sea ice extent maps, accessibility maps for different ice classes and average sea ice concentration and thickness conditions maps. The second type of indicators (e.g. trip duration, fuel consumption) provides information about the navigation conditions along a specific arctic route going through the so-called North-East Passage for an ARC4 bulk carrier. Due to the global warming and the continuous decline of Arctic sea ice, maritime transport in the Arctic region has been increased dramatically. While operating in the Arctic area, ships face regular environment loads and ice loads synchronously, the consideration of ice resistance is essential for the fuel consumption estimation in the ice-covered water voyage. For this specific bulk carrier, the Arctic sailing cost is composed of the following operational costs: the insurance cost is assumed to be 500usd/day, the ship renting cost is assumed to be 40kusd/day and the crew cost (including wages, social benefits, entertainments, etc.) is assumed to be 6kusd/day. The other big category of the cost is associated with the fuel cost. The fuel cost is computed by first estimating the ship’s resistance in the still water and in various ice conditions along the arctic ship route. It should be noted that since in the climate projection data does not contain the wave and wind information, the resistance caused by wind and wave will be neglected in the calculation. The calm/still water resistance is estimated by the well-known formula proposed by Holtrop and Mennen (1982). The ice resistance is estimated by the method proposed by Lindqvist (1989), which divides resistance components into ice crushing, bending-induced breaking and submergence, based on the physical phenomena. This method gives ice resistance as functions of ship main dimensions, hull form, ice thickness, ice friction and ice strength. After getting the ship resistance for various ice conditions and operational conditions (ship speed), it will be divided by the propulsion efficiency of the ship to get the require marine engine power, that can push the ship forward to overtake the still water and ice resistance. The fuel consumption rate for the calculated engine power will be easily analysed by given the engine oil properties. For the given arctic ship route and sailing time (speeds), the total fuel cost can be estimated. The ship’s (maximum) sailing speed at various ice conditions is determined according to the Russian Polar Class rules. Finally, another cost is related to if the ship needs support from Ice breaker for resorted ice navigation. Whether or not the ice breaker is needed is determined according to the ship’s ice class and the most severe ice conditions along the ship’s arctic route during that voyage based on Russian Polar Class rules. If the ice breaker is needed, then a big fixed ice breaker renting cost of 1200kusd will be added to the total cost. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid. Horizontal coverage Global trajectories Horizontal resolution 1.0° x 1.0° Temporal coverage From January 2010 to December 2100. Temporal resolution Month File format NetCDF4 Conventions Climate and Forecast Metadata Convention v1.6. Attribute Convention for Dataset Discovery (ACDD) v1.3. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid. Projection Regular latitude-longitude grid. Horizontal coverage Global trajectories Horizontal coverage Global trajectories Horizontal resolution 1.0° x 1.0° Horizontal resolution 1.0° x 1.0° Temporal coverage From January 2010 to December 2100. Temporal coverage From January 2010 to December 2100. Temporal resolution Month Temporal resolution Month File format NetCDF4 File format NetCDF4 Conventions Climate and Forecast Metadata Convention v1.6. Attribute Convention for Dataset Discovery (ACDD) v1.3. Conventions Climate and Forecast Metadata Convention v1.6. Attribute Convention for Dataset Discovery (ACDD) v1.3. MAIN VARIABLES Name Units Description Arctic accessibility index ARC4 Dimensionless Mask (0 - 1) indicating whether the ice class ARC4 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice thickness. Arctic accessibility index ARC5 Dimensionless Mask (0 - 1) indicating whether the ice class ARC5 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice thickness. Arctic accessibility index ARC6 Dimensionless Mask (0 - 1) indicating whether the ice class ARC6 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice. Arctic accessibility index ARC7 Dimensionless Mask (0 - 1) indicating whether the ice class ARC7 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice thickness. Arctic accessibility index ARC8 Dimensionless Mask (0 - 1) indicating whether the ice class ARC8 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice thickness. Arctic accessibility index ARC9 Dimensionless Mask (0 - 1) indicating whether the ice class ARC9 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice thickness. Arctic route cost $ The predicted total cost for a specific ice class ship and defined Arctic Passage route, based on monthly decadal projection value of sea ice concentration and sea ice thickness. Costs cover rental, crew, fuel, insurance and eventual icebreaker costs. Fuel consumption kg The predicted fuel consumption at every waypoint along the arctic route for a specific ice class ship and defined Arctic Passage route, based on the sea ice concentration and sea ice thickness projection.' Ice resistance N The predicted ice resistance for a specific ice class ship and defined Arctic Passage route, based on the sea ice concentration and sea ice thickness projection. Values are provided as monthly decadal projection. Icebreaker need Dimensionless Mask (0 - 1) indicating whether an icebreaker is needed for a specific ice class ship and defined Arctic Passage route, based on monthly decadal projection value of sea ice concentration and sea ice thickness Maximum sea ice extent mask Dimensionless Mask (0-1) providing the monthly decadal maximum of the arctic sea ice extent projection. Mean sea ice extent mask Dimensionless Mask (0-1) providing the monthly decadal mean of the arctic sea ice extent projection. Minimum sea ice extent mask Dimensionless Mask (0-1) providing the monthly decadal minimum of the arctic sea ice extent projection. Sea ice concentration Dimensionless The monthly decadal averaged value of the area fraction of the model grid cell covered by sea ice predicted by CMIP5 models. Sea ice thickness m The monthly decadal averaged value of the sea ice thickness projection. Shaft power W The predicted shaft power for a specific ice class ship and defined Arctic Passage route, based on the sea ice concentration and sea ice thickness projection. Values are provided as monthly decadal projection. Ship speed m s-1 The predicted allowed ship speed for a specific ice class ship and defined Arctic Passage route, based on the sea ice concentration and sea ice thickness projection. Values are provided as monthly decadal projection. Trip duration s The predicted sailing time profile for a specific ice class ship and defined Arctic Passage route, based on the monthly decadal sea ice concentration and sea ice thickness projection. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Arctic accessibility index ARC4 Dimensionless Mask (0 - 1) indicating whether the ice class ARC4 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice thickness. Arctic accessibility index ARC4 Dimensionless Mask (0 - 1) indicating whether the ice class ARC4 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice thickness. Arctic accessibility index ARC5 Dimensionless Mask (0 - 1) indicating whether the ice class ARC5 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice thickness. Arctic accessibility index ARC5 Dimensionless Mask (0 - 1) indicating whether the ice class ARC5 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice thickness. Arctic accessibility index ARC6 Dimensionless Mask (0 - 1) indicating whether the ice class ARC6 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice. Arctic accessibility index ARC6 Dimensionless Mask (0 - 1) indicating whether the ice class ARC6 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice. Arctic accessibility index ARC7 Dimensionless Mask (0 - 1) indicating whether the ice class ARC7 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice thickness. Arctic accessibility index ARC7 Dimensionless Mask (0 - 1) indicating whether the ice class ARC7 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice thickness. Arctic accessibility index ARC8 Dimensionless Mask (0 - 1) indicating whether the ice class ARC8 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice thickness. Arctic accessibility index ARC8 Dimensionless Mask (0 - 1) indicating whether the ice class ARC8 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice thickness. Arctic accessibility index ARC9 Dimensionless Mask (0 - 1) indicating whether the ice class ARC9 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice thickness. Arctic accessibility index ARC9 Dimensionless Mask (0 - 1) indicating whether the ice class ARC9 ship can access a specific Arctic area, determined by the monthly decadal projection value of sea ice concentration and sea ice thickness. Arctic route cost $ The predicted total cost for a specific ice class ship and defined Arctic Passage route, based on monthly decadal projection value of sea ice concentration and sea ice thickness. Costs cover rental, crew, fuel, insurance and eventual icebreaker costs. Arctic route cost $ The predicted total cost for a specific ice class ship and defined Arctic Passage route, based on monthly decadal projection value of sea ice concentration and sea ice thickness. Costs cover rental, crew, fuel, insurance and eventual icebreaker costs. Fuel consumption kg The predicted fuel consumption at every waypoint along the arctic route for a specific ice class ship and defined Arctic Passage route, based on the sea ice concentration and sea ice thickness projection.' Fuel consumption kg The predicted fuel consumption at every waypoint along the arctic route for a specific ice class ship and defined Arctic Passage route, based on the sea ice concentration and sea ice thickness projection.' Ice resistance N The predicted ice resistance for a specific ice class ship and defined Arctic Passage route, based on the sea ice concentration and sea ice thickness projection. Values are provided as monthly decadal projection. Ice resistance N The predicted ice resistance for a specific ice class ship and defined Arctic Passage route, based on the sea ice concentration and sea ice thickness projection. Values are provided as monthly decadal projection. Icebreaker need Dimensionless Mask (0 - 1) indicating whether an icebreaker is needed for a specific ice class ship and defined Arctic Passage route, based on monthly decadal projection value of sea ice concentration and sea ice thickness Icebreaker need Dimensionless Mask (0 - 1) indicating whether an icebreaker is needed for a specific ice class ship and defined Arctic Passage route, based on monthly decadal projection value of sea ice concentration and sea ice thickness Maximum sea ice extent mask Dimensionless Mask (0-1) providing the monthly decadal maximum of the arctic sea ice extent projection. Maximum sea ice extent mask Dimensionless Mask (0-1) providing the monthly decadal maximum of the arctic sea ice extent projection. Mean sea ice extent mask Dimensionless Mask (0-1) providing the monthly decadal mean of the arctic sea ice extent projection. Mean sea ice extent mask Dimensionless Mask (0-1) providing the monthly decadal mean of the arctic sea ice extent projection. Minimum sea ice extent mask Dimensionless Mask (0-1) providing the monthly decadal minimum of the arctic sea ice extent projection. Minimum sea ice extent mask Dimensionless Mask (0-1) providing the monthly decadal minimum of the arctic sea ice extent projection. Sea ice concentration Dimensionless The monthly decadal averaged value of the area fraction of the model grid cell covered by sea ice predicted by CMIP5 models. Sea ice concentration Dimensionless The monthly decadal averaged value of the area fraction of the model grid cell covered by sea ice predicted by CMIP5 models. Sea ice thickness m The monthly decadal averaged value of the sea ice thickness projection. Sea ice thickness m The monthly decadal averaged value of the sea ice thickness projection. Shaft power W The predicted shaft power for a specific ice class ship and defined Arctic Passage route, based on the sea ice concentration and sea ice thickness projection. Values are provided as monthly decadal projection. Shaft power W The predicted shaft power for a specific ice class ship and defined Arctic Passage route, based on the sea ice concentration and sea ice thickness projection. Values are provided as monthly decadal projection. Ship speed m s-1 The predicted allowed ship speed for a specific ice class ship and defined Arctic Passage route, based on the sea ice concentration and sea ice thickness projection. Values are provided as monthly decadal projection. Ship speed m s-1 The predicted allowed ship speed for a specific ice class ship and defined Arctic Passage route, based on the sea ice concentration and sea ice thickness projection. Values are provided as monthly decadal projection. Trip duration s The predicted sailing time profile for a specific ice class ship and defined Arctic Passage route, based on the monthly decadal sea ice concentration and sea ice thickness projection. Trip duration s The predicted sailing time profile for a specific ice class ship and defined Arctic Passage route, based on the monthly decadal sea ice concentration and sea ice thickness projection. 304 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/seasonal-monthly-ocean https://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal-monthly-ocean seasonal-monthly-ocean This entry covers global ocean data aggregated to a monthly time resolution. The catalogue entry includes temperature and salinity characteristics of the upper oceans and complements the other seasonal forecast catalogue entries for the land and atmospheric variables. Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect distributions of outcomes. Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The seasonal forecast data is grouped in several catalogue entries (CDS datasets), currently defined by the model component and type of variable: outputs from the ocean component or the atmospheric one (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). The data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system. DATA DESCRIPTION Data type GRIB Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 1° x 1° Vertical coverage Upper part of the oceans Temporal coverage Hindcasts: at least 1993-2016 Forecasts: from March 2023 Temporal resolution Monthly File format NetCDF Versions Latest version is provided. Files provide a version tag. Update frequency Real time forecasts are released once per month on the 13th at 12UTC DATA DESCRIPTION DATA DESCRIPTION Data type GRIB Data type GRIB Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 1° x 1° Horizontal resolution 1° x 1° Vertical coverage Upper part of the oceans Vertical coverage Upper part of the oceans Temporal coverage Hindcasts: at least 1993-2016 Forecasts: from March 2023 Temporal coverage Hindcasts: at least 1993-2016 Forecasts: from March 2023 Hindcasts: at least 1993-2016 Forecasts: from March 2023 Temporal resolution Monthly Temporal resolution Monthly File format NetCDF File format NetCDF Versions Latest version is provided. Files provide a version tag. Versions Latest version is provided. Files provide a version tag. Update frequency Real time forecasts are released once per month on the 13th at 12UTC Update frequency Real time forecasts are released once per month on the 13th at 12UTC MAIN VARIABLES Name Units Description Depth average potential temperature of upper 300m K Mean sea water potential temperature in the upper 300m of the sea. The potential temperature is the temperature of a parcel of sea water would have if moved adiabatically to sea level pressure. Depth average salinity of upper 300m psu Mean sea water salt concentration in the upper 300m of the sea. Depth of 14°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 14°C. Depth of 17°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 17°C. Depth of 20°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 20°C. Depth of 26°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 26°C. Depth of 28°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 28°C. Mixed layer depth 0.01 m The depth of the ocean where the average sea water density exceeds the near surface density (sigma theta) plus 0.01 kg/m3. Mixed layer depth 0.03 m The depth of the ocean where the average sea water density exceeds the near surface density (sigma theta) plus 0.03 kg/m3. Sea ice thickness m Mean thickness of the sea ice layer in the area of the grid cell covered by ice. Sea surface height above geoid m Vertical distance between the actual sea surface and a reference surface of constant geopotential with which mean sea level would coincide if the ocean were at rest. Sea surface salinity psu Salt concentration at the sea surface. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Depth average potential temperature of upper 300m K Mean sea water potential temperature in the upper 300m of the sea. The potential temperature is the temperature of a parcel of sea water would have if moved adiabatically to sea level pressure. Depth average potential temperature of upper 300m K Mean sea water potential temperature in the upper 300m of the sea. The potential temperature is the temperature of a parcel of sea water would have if moved adiabatically to sea level pressure. Depth average salinity of upper 300m psu Mean sea water salt concentration in the upper 300m of the sea. Depth average salinity of upper 300m psu Mean sea water salt concentration in the upper 300m of the sea. Depth of 14°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 14°C. Depth of 14°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 14°C. Depth of 17°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 17°C. Depth of 17°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 17°C. Depth of 20°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 20°C. Depth of 20°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 20°C. Depth of 26°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 26°C. Depth of 26°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 26°C. Depth of 28°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 28°C. Depth of 28°C isotherm m The depth below sea level of those locations in the sea where the temperature values are 28°C. Mixed layer depth 0.01 m The depth of the ocean where the average sea water density exceeds the near surface density (sigma theta) plus 0.01 kg/m3. Mixed layer depth 0.01 m The depth of the ocean where the average sea water density exceeds the near surface density (sigma theta) plus 0.01 kg/m3. Mixed layer depth 0.03 m The depth of the ocean where the average sea water density exceeds the near surface density (sigma theta) plus 0.03 kg/m3. Mixed layer depth 0.03 m The depth of the ocean where the average sea water density exceeds the near surface density (sigma theta) plus 0.03 kg/m3. Sea ice thickness m Mean thickness of the sea ice layer in the area of the grid cell covered by ice. Sea ice thickness m Mean thickness of the sea ice layer in the area of the grid cell covered by ice. Sea surface height above geoid m Vertical distance between the actual sea surface and a reference surface of constant geopotential with which mean sea level would coincide if the ocean were at rest. Sea surface height above geoid m Vertical distance between the actual sea surface and a reference surface of constant geopotential with which mean sea level would coincide if the ocean were at rest. Sea surface salinity psu Salt concentration at the sea surface. Sea surface salinity psu Salt concentration at the sea surface. 305 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/leaf-area-index-1999-2020-raster-1-km-global-10-daily http://land.copernicus.eu/global/products/lai Leaf Area Index 1999-2020 (raster 1 km), global, 10-daily - version 1 LAI was defined by CEOS as half the developed area of the convex hull wrapping the green canopy elements per unit horizontal ground. This definition allows accounting for elements which are not flat such as needles or stems. LAI is strongly non linearly related to reflectance. Therefore, its estimation from remote sensing observations will be scale dependant over heterogeneous landscapes. When observing a canopy made of different layers of vegetation, it is mandatory to consider all the green layers. This is particularly important for forest canopies where the understory may represent a very significant contribution to the total canopy LAI. The derived LAI corresponds to the total green LAI, including the contribution of the green elements of the understory. 306 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-ocean-colour-plankton-my-l4-daily-gapfree http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_BLK_BGC_L4_MY_009_154 Black Sea Ocean Colour Plankton MY L4 daily gapfree observations and climatology and monthly observations Short description: For the Black Sea Ocean Satellite Observations, the Italian National Research Council (CNR – Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets: * ''plankton'' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated gap-free Chl concentration (to provide a ""cloud free"" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018); moreover, daily climatology for chlorophyll concentration is provided. Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A for the ""multi"" products, and OLCI-S3A & S3B for the ""olci"" products Temporal resolutions: monthly and daily (for ""gap-free"" and climatology data) Spatial resolution: 1 km for ""multi"" and 300 meters for ""olci"" To find this product in the catalogue, use the search keyword ""OCEANCOLOUR_BLK_BGC_L4_MY"". DOI (product) :https://doi.org/10.48670/moi-00304 https://doi.org/10.48670/moi-00304 307 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-and-sea-ice-sigma-nought http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_ARC_PHY_L3M_NRT_011_017 ARCTIC Ocean and Sea-Ice Sigma-Nought Short description: For the Arctic Ocean - multiple Sentinel-1 scenes, Sigma0 calibrated and noise-corrected, with individual geographical map projections over Svalbard and Greenland Sea regions. DOI (product) :https://doi.org/10.48670/moi-00124 https://doi.org/10.48670/moi-00124 308 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/european-north-west-shelfiberia-biscay-irish-seas-high-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_ATL_PHY_L3S_NRT_010_037 European North West Shelf/Iberia Biscay Irish Seas – High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations Short description: For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.02° resolution grid. It includes observations by polar orbiting and geostationary satellites . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. DOI (product) :https://doi.org/10.48670/moi-00310 https://doi.org/10.48670/moi-00310 309 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-sea-level-mediterranean https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-mediterranean satellite-sea-level-mediterranean Sea level anomaly is the height of water over the mean sea surface in a given time and region. In this dataset sea level anomalies are computed with respect to a twenty-year mean reference period (1993-2012). Up-to-date altimeter standards are used to estimate the sea level anomalies with a mapping algorithm specifically dedicated to the Mediterranean Sea. The steady number of reference satellite used in the production of this dataset contributes to the long-term stability of the sea level record. Improvements of the accuracy, sampling of meso-scale processes and of the high-latitude coverage were achieved by using a few additional satellite missions. This dataset includes uncertainties for each grid cell. More details about the sea level retrieval, additional filters, optimisation procedures, and the error estimation are given in the Documentation section. DATA DESCRIPTION Data type Gridded Horizontal coverage (29.47, 45.16)°N (-6.00, 36.60)°E: Mediterranean Sea. Horizontal resolution 0.125° x 0.125° Temporal coverage 1 January 1993 to 3 June 2020 Temporal resolution Daily File format NetCDF Versions vDT2018 Update frequency No longer updated DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage (29.47, 45.16)°N (-6.00, 36.60)°E: Mediterranean Sea. Horizontal coverage (29.47, 45.16)°N (-6.00, 36.60)°E: Mediterranean Sea. Horizontal resolution 0.125° x 0.125° Horizontal resolution 0.125° x 0.125° Temporal coverage 1 January 1993 to 3 June 2020 Temporal coverage 1 January 1993 to 3 June 2020 Temporal resolution Daily Temporal resolution Daily File format NetCDF File format NetCDF Versions vDT2018 Versions vDT2018 Update frequency No longer updated Update frequency No longer updated MAIN VARIABLES Name Units Description Absolute dynamic topography m Sea surface height above the geoid computed as the sum of the sea level anomaly with the mean dynamic topography Absolute geostrophic velocity meridian component m s-1 Northward component of the absolute geostrophic current Absolute geostrophic velocity zonal component m s-1 Eastward component of the absolute geostrophic current Geostrophic velocity anomalies meridian component m s-1 Northward component of the geostrophic current Geostrophic velocity anomalies zonal component m s-1 Eastward component of the geostrophic current Sea level anomaly m Sea surface height above mean sea surface computed with respect to a 20-year mean reference period (1993-2012) MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Absolute dynamic topography m Sea surface height above the geoid computed as the sum of the sea level anomaly with the mean dynamic topography Absolute dynamic topography m Sea surface height above the geoid computed as the sum of the sea level anomaly with the mean dynamic topography Absolute geostrophic velocity meridian component m s-1 Northward component of the absolute geostrophic current Absolute geostrophic velocity meridian component m s-1 Northward component of the absolute geostrophic current Absolute geostrophic velocity zonal component m s-1 Eastward component of the absolute geostrophic current Absolute geostrophic velocity zonal component m s-1 Eastward component of the absolute geostrophic current Geostrophic velocity anomalies meridian component m s-1 Northward component of the geostrophic current Geostrophic velocity anomalies meridian component m s-1 Northward component of the geostrophic current Geostrophic velocity anomalies zonal component m s-1 Eastward component of the geostrophic current Geostrophic velocity anomalies zonal component m s-1 Eastward component of the geostrophic current Sea level anomaly m Sea surface height above mean sea surface computed with respect to a 20-year mean reference period (1993-2012) Sea level anomaly m Sea surface height above mean sea surface computed with respect to a 20-year mean reference period (1993-2012) 310 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-ecv-cmip5-bias-corrected https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-ecv-cmip5-bias-corrected sis-ecv-cmip5-bias-corrected This dataset contains 4 Essential Climate Variables (ECV) for the 18 bias adjusted Global Climate Models (GCM) from CMIP5: daily precipitation rate, and daily mean, maximum and minimum temperatures. The data are bias adjusted using the Distribution Based Scaling (DBS) method versus the global reference dataset HydroGFD2.0, both bias adjustment method and global reference dataset developed by the Swedish Meteorological and Hydrological Institute (SMHI). precipitation rate mean, maximum and minimum temperatures The DBS method is a parametric quantile-mapping variant. This type of methods fit a statistical distribution to the cumulative distribution function and use those fitted distributions to conduct the quantile-mapping. Here, we used a double-gamma distribution (i.e. separate gamma distributions for the bulk and the high tail) for precipitation and the normal distribution for all temperature variables. Temperature corrections were done conditional on the wet/dry state of the corresponding precipitation time series. The seasonal variations in the biases were represented by monthly parameter windows for precipitation and a smoothed seasonal cycle for the temperature distribution parameters. The smoothing was done using twelve harmonic components. There is some post-processing in place for the data set to be suitable for hydrological impact modeling. Bias-adjustment of daily mean, maximum and minimum temperature using quantile mapping can in some cases lead to inconsistencies. For instance, maximum (minimum) temperature could be lower (higher) than mean temperature. If such inconsistencies occur, daily minimum and maximum temperatures are adjusted in such a way that the anomalies with respect to the daily mean temperature are in line with the climatological anomalies for the particular day in the seasonal cycle. This means, for example, that an inconsistency occurring on June 25 will be adjusted using the climatological anomalies for June 25, estimated by a moving window. Also, the adjustment is done conditional on the wet/dry state of the corresponding precipitation series. The climatology of the anomalies was derived from the HydroGFD2.0 dataset. In addition, DBS’s limitations lead to some data not being bias-adjusted or values beyond physically plausible ranges. In those cases, DBS gives missing values as output. Grid cells where the DBS method resulted in such missing values have been interpolated in time or space. If interpolation was not possible, full time series from nearest grid cell was copied to relating grid point. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 0.5° x 0.5° Temporal coverage From 1978 to 2100 Temporal resolution Day File format NetCDF DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.5° x 0.5° Horizontal resolution 0.5° x 0.5° Temporal coverage From 1978 to 2100 Temporal coverage From 1978 to 2100 Temporal resolution Day Temporal resolution Day File format NetCDF File format NetCDF MAIN VARIABLES Name Units Description Maximum 2m temperature K Ambient air temperature. The data represents the maximum during the aggregation period at 2m above the surface. Mean 2m temperature K Ambient air temperature. The data represents the mean over the aggregation period at 2m above the surface. Minimum 2m temperature K Ambient air temperature. The data represents the minimum during the aggregation period at 2m above the surface. Precipitation flux kg m-2 s-1 The deposition of water to the Earth s surface in the form of rain, snow, ice or hail. The precipitation flux is the mass of water per unit area and time. The data represents the mean over the aggregation period. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Maximum 2m temperature K Ambient air temperature. The data represents the maximum during the aggregation period at 2m above the surface. Maximum 2m temperature K Ambient air temperature. The data represents the maximum during the aggregation period at 2m above the surface. Mean 2m temperature K Ambient air temperature. The data represents the mean over the aggregation period at 2m above the surface. Mean 2m temperature K Ambient air temperature. The data represents the mean over the aggregation period at 2m above the surface. Minimum 2m temperature K Ambient air temperature. The data represents the minimum during the aggregation period at 2m above the surface. Minimum 2m temperature K Ambient air temperature. The data represents the minimum during the aggregation period at 2m above the surface. Precipitation flux kg m-2 s-1 The deposition of water to the Earth s surface in the form of rain, snow, ice or hail. The precipitation flux is the mass of water per unit area and time. The data represents the mean over the aggregation period. Precipitation flux kg m-2 s-1 The deposition of water to the Earth s surface in the form of rain, snow, ice or hail. The precipitation flux is the mass of water per unit area and time. The data represents the mean over the aggregation period. 311 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-sea-ice-thickness https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-ice-thickness satellite-sea-ice-thickness This dataset provides monthly gridded data of sea ice thickness for the Arctic region based on satellite radar altimetry observations. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its presence or its retreat has a strong impact on air-sea interactions, the Earth’s energy budget as well as marine ecosystems. It is recognized by the Global Climate Observing System as an Essential Climate Variable. Sea ice thickness is one of the parameters commonly used to characterise sea ice, alongside sea ice concentration, sea ice edge, and sea ice type, also available in the Climate Data Store. Satellite radar altimeters provide measurements of the sea ice freeboard, which is the difference between the height of the surface of sea ice and the surface of water in open leads (areas of open water within the sea ice). Because of the buoyancy of ice in water, typically about 90% of the ice thickness remains under water and thus the total ice thickness is about 10 times the freeboard. However, snow on top of sea ice changes this ratio and complicates the estimation of the ice thickness, requiring the use of auxiliary information about snow depth and density. The retrieval of ice thickness uses the narrow radar swath at the nadir of the satellite at full resolution of approximately 1-10 km and a point spacing of 300 meters. This Level-2 sea-ice thickness products (not provided here) is then gridded for a period of a month to obtain full coverage of a north polar grid at a resolution of 25 km. The algorithm used was developed as part of the European Space Agency Climate Change Initiative (ESA CCI) on Sea Ice. The data provided here are Level-3 Collated (L3C) products: they contain monthly gridded values from orbit data from a single platform (Envisat or CryoSat-2) without interpolation or any other form of gap filling. The files also contain estimates of the algorithm uncertainty as well as a quality status flag indicating potential issues with the retrieval not captured in the algorithm uncertainty. Sources of uncertainty in the algorithm are related to the auxiliary data and to the use of different radar altimeter concepts in Envisat (pulse-limited) and CryoSat-2 (synthetic aperture radar). This dataset combines a Climate Data Record (CDR), which has sufficient length, consistency, and continuity to be used to assess climate variability and change, and an Interim Climate Data Record (ICDR), which provides regular temporal extensions to the CDR and where consistency with the CDR is expected but not extensively checked. Here, the CDR is based on measurements from the RA-2 altimeter on Envisat (October 2002 to October 2010) and the SIRAL altimeter on CryoSat-2 (November 2010 to April 2020). The ICDR is based on observations from CryoSat-2 only (from April 2015 onward) and is updated monthly with a one-month delay behind real time. Users should note that the quality and accuracy of the data record are higher during the CryoSat-2 period than during the Envisat period. As a result, care should be taken when combining the two missions to assess long-term changes and trends. More information can be found in the Product User Guide and Product Quality Assessment Report. This dataset is currently limited spatially to the Arctic region and temporally to the winter months of October through April due to unresolved bias originating from melting snow or open melt ponds in the remaining five months. For a similar reason, no sea-ice thickness data with sufficient quality exist for the Southern Hemisphere. The extension of the CDR/ICDR to other periods, regions, and radar altimeter missions is under development in the extension of the ESA CCI Sea Ice project (ESA CCI+). This dataset is produced on behalf of the Copernicus Climate Change Service (C3S). DATA DESCRIPTION Data type Gridded Projection Lambert Azimuthal Equal Area (EASE-Grid version 2.0) centred over the North Pole Horizontal coverage Northern Hemisphere Horizontal resolution 25 km grid resolution (true spatial resolution: 1-10 km) Vertical coverage Surface Vertical resolution Single level Temporal coverage October 2002 to present, only for Northern Hemisphere winter months (October through April) Temporal resolution Monthly Temporal gaps From May through September File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.7, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions v2.0: Based on ESA CCI CDR v3.0; improved computation of snow load and uncertainty estimation. v1.0: Initial version based on ESA CCI CDR v2.0; not updated beyond April 2021. Update frequency Monthly (with a 1-month latency) DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert Azimuthal Equal Area (EASE-Grid version 2.0) centred over the North Pole Projection Lambert Azimuthal Equal Area (EASE-Grid version 2.0) centred over the North Pole Horizontal coverage Northern Hemisphere Horizontal coverage Northern Hemisphere Horizontal resolution 25 km grid resolution (true spatial resolution: 1-10 km) Horizontal resolution 25 km grid resolution (true spatial resolution: 1-10 km) Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage October 2002 to present, only for Northern Hemisphere winter months (October through April) Temporal coverage October 2002 to present, only for Northern Hemisphere winter months (October through April) Temporal resolution Monthly Temporal resolution Monthly Temporal gaps From May through September Temporal gaps From May through September File format NetCDF 4 File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.7, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.7, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions v2.0: Based on ESA CCI CDR v3.0; improved computation of snow load and uncertainty estimation. v1.0: Initial version based on ESA CCI CDR v2.0; not updated beyond April 2021. Versions v2.0: Based on ESA CCI CDR v3.0; improved computation of snow load and uncertainty estimation. v1.0: Initial version based on ESA CCI CDR v2.0; not updated beyond April 2021. Update frequency Monthly (with a 1-month latency) Update frequency Monthly (with a 1-month latency) MAIN VARIABLES Name Units Description Sea ice thickness m Mean thickness of the sea ice layer in the area of the grid cell covered by ice. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Sea ice thickness m Mean thickness of the sea ice layer in the area of the grid cell covered by ice. Sea ice thickness m Mean thickness of the sea ice layer in the area of the grid cell covered by ice. RELATED VARIABLES Name Units Description Quality flag Dimensionless Quality flag for the sea ice thickness retrievals. It provides an expert guess on data quality (nominal, intermediate, low) based on a list of criterions. See Product User Guide for details. Status flag Dimensionless Status flag detailing what filters, masks, or processing steps were applied to each pixel in the sea ice thickness retrievals. See Product User Guide for details. Uncertainty m Statistical uncertainty of the sea ice thickness retrieval algorithm. It corresponds to the mean uncertainty of along-track values at full sensor resolution. RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Quality flag Dimensionless Quality flag for the sea ice thickness retrievals. It provides an expert guess on data quality (nominal, intermediate, low) based on a list of criterions. See Product User Guide for details. Quality flag Dimensionless Quality flag for the sea ice thickness retrievals. It provides an expert guess on data quality (nominal, intermediate, low) based on a list of criterions. See Product User Guide for details. Status flag Dimensionless Status flag detailing what filters, masks, or processing steps were applied to each pixel in the sea ice thickness retrievals. See Product User Guide for details. Status flag Dimensionless Status flag detailing what filters, masks, or processing steps were applied to each pixel in the sea ice thickness retrievals. See Product User Guide for details. Uncertainty m Statistical uncertainty of the sea ice thickness retrieval algorithm. It corresponds to the mean uncertainty of along-track values at full sensor resolution. Uncertainty m Statistical uncertainty of the sea ice thickness retrieval algorithm. It corresponds to the mean uncertainty of along-track values at full sensor resolution. 312 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-water-hydrological-change https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-water-hydrological-change sis-water-hydrological-change The dataset provides a number of indicators of the potential change, relative to a reference period of the recent past, in hydrological conditions over the 21st Century based on an ensemble of climate and hydrological models. The indicators cover hydrological variables of river discharge, soil moisture, snow water equivalent and groundwater recharge. These indicators are produced by various hydrological models using input variables of historical and projected precipitation, temperature and potential evapotranspiration. The indicators transform data from climate projections into usable information for the European water sector. They were defined in discussion with stakeholder groups working in different areas of the water sector (hydropower, irrigation, water supply) to provide clear information on climate projections for water resources as annual, seasonal and monthly change factors for a range of variables. A range of global climate models and standard projection scenarios (based on latest Copernicus Climate Change Service and Coupled Model Inter-comparison Project Phase 5 climate modelling experiments) were used along a multi-hydrological model approach to produce these indicators. This ensemble approach to the climate and hydrological modelling captures the uncertainty and variability of the hydrological regime. Precipitation and temperature data from five global climate models was downscaled to 5km x 5km resolution with the daily values disaggregated to 3-hourly values. These data were used to force four hydrological models to produce the hydrological variables to derive the indicators. The indicators are given as relative changes for a given 30-year projection window with respect to the reference period estimates of 1971-2010 for Representative Concentration Pathways (RCP) 2.6 and 8.5, for each grid cell. This dataset is produced on behalf of Copernicus Climate Change Service, by UK Centre for Ecology & Hydrology (UKCEH), Helmholtz Centre for Environmental Research (UFZ), Leipzig, Centro Tecnológico del Agua (Cetaqua), Climate Partnership LLC (CPL), Environment Agency (EA), Mediterranean Network of Basin Organisations (MENBO), Norwegian Water Resources & Energy Directorate (NVE). DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Greater Europe Horizontal resolution 5 km x 5 km Temporal coverage from 2011 to 2095 Temporal resolution 30 year averages calculated with a 5-year time-step Temporal gaps No gaps File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v.1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Greater Europe Horizontal coverage Greater Europe Horizontal resolution 5 km x 5 km Horizontal resolution 5 km x 5 km Temporal coverage from 2011 to 2095 Temporal coverage from 2011 to 2095 Temporal resolution 30 year averages calculated with a 5-year time-step Temporal resolution 30 year averages calculated with a 5-year time-step Temporal gaps No gaps Temporal gaps No gaps File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v.1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v.1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Versions 1.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Air temperature °C The temperature of the air at approximately 2m above the surface. Values are given as the absolute change from the reference period (1971-2010). Groundwater recharge % The volume of percolating water through the unsaturated zone to the aquifer. Values are given as the relative change from the reference period (1971-2010). Potential evapotranspiration % The amount of evaporation that would occur if a sufficient water source were available. Values are given as the relative change from the reference period (1971-2010). Precipitation % The water falling as rain, snow, sleet, or hail per unit area during a given time period. Values are given as the relative change from the reference period (1971-2010). River discharge % River discharge (or streamflow) is the volumetric discharge through stream or river channel. The river routing model mRm has been utilized in these simulations. Values are given as the relative change from the reference period (1971-2010). Snow water equivalent % The equivalent volume of water in the snow pack if the snow were to be melted. Values are given as the relative change from the reference period (1971-2010). Volumetric soil moisture % The volume of water within the unsaturated zone of the soil profile. Values are given as the relative change from the reference period (1971-2010). MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Air temperature °C The temperature of the air at approximately 2m above the surface. Values are given as the absolute change from the reference period (1971-2010). Air temperature °C The temperature of the air at approximately 2m above the surface. Values are given as the absolute change from the reference period (1971-2010). Groundwater recharge % The volume of percolating water through the unsaturated zone to the aquifer. Values are given as the relative change from the reference period (1971-2010). Groundwater recharge % The volume of percolating water through the unsaturated zone to the aquifer. Values are given as the relative change from the reference period (1971-2010). Potential evapotranspiration % The amount of evaporation that would occur if a sufficient water source were available. Values are given as the relative change from the reference period (1971-2010). Potential evapotranspiration % The amount of evaporation that would occur if a sufficient water source were available. Values are given as the relative change from the reference period (1971-2010). Precipitation % The water falling as rain, snow, sleet, or hail per unit area during a given time period. Values are given as the relative change from the reference period (1971-2010). Precipitation % The water falling as rain, snow, sleet, or hail per unit area during a given time period. Values are given as the relative change from the reference period (1971-2010). River discharge % River discharge (or streamflow) is the volumetric discharge through stream or river channel. The river routing model mRm has been utilized in these simulations. Values are given as the relative change from the reference period (1971-2010). River discharge % River discharge (or streamflow) is the volumetric discharge through stream or river channel. The river routing model mRm has been utilized in these simulations. Values are given as the relative change from the reference period (1971-2010). Snow water equivalent % The equivalent volume of water in the snow pack if the snow were to be melted. Values are given as the relative change from the reference period (1971-2010). Snow water equivalent % The equivalent volume of water in the snow pack if the snow were to be melted. Values are given as the relative change from the reference period (1971-2010). Volumetric soil moisture % The volume of water within the unsaturated zone of the soil profile. Values are given as the relative change from the reference period (1971-2010). Volumetric soil moisture % The volume of water within the unsaturated zone of the soil profile. Values are given as the relative change from the reference period (1971-2010). 313 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-agriculture-crop-development-explorer https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-agriculture-crop-development-explorer app-agriculture-crop-development-explorer The Crop Development Explorer provides an interface to explore changes in crop development/phenology between historic and future periods as estimated using outout from the selected climate model. Estimates are available for four pre-defined crops (wheat, maize, rice, and soybean) and it is possible to define any other crop by specifying the relevant parameters. The Crop Development Explorer allows users to explore the future climatic suitability for different crops and varieties worldwide. Crop Development Explorer Crop Development Explorer Climate change and climate variability can affect crop production in multiple ways: Rain and sunshine determines the amount of biomass plants can produce (i.e. how large the plant is, how many leaves etc.); Temperature determines how fast a crop develops (i.e. when it will bloom, when seeds will be ripe). The statistics presented in this application were derived from the Agroclimatic indicators from 1951 to 2099 derived from climate projections, for further details on how these statistics were derived please refer to the documentation provided on the dataset entry. Additional crop specific spatial masks, crop calendars and other parameters are also used in the Crop Development Explorer but these are not available for download on the dataset entry. Agroclimatic indicators from 1951 to 2099 derived from climate projections Crop Development Explorer The main application provides an interactive map with three selectable layers: 10-day averages (dekads) of sowing, flowering and harvesting. Zooming in on the map will make national and administrative borders visible. Users can either select one of the predefined crops, or parameterise their own using the 4 defining variables. Similarly, the climate options can be selected from a range of climate/weather models and time periods. Clicking a point on the map opens a detailed view of the location which contains two time-series plots. One time-series displays the timing of the different crop stages and the second time-series displays the climate characteristics (incl extremes) during successive crop stages. A range of (bias corrected) climate projections and scenarios can be accessed. The data used in the time series can be download in a .csv format. User-selectable parameters User-selectable parameters Crop: Winter wheat Spring wheat Soybean Maize 1st rice 2nd rice User defined (with crop parameters set by user) Crop parameters (if Crop = user defined, please refer to user documentation for further details): Degree-days for sowing till flowering Degree-days for sowing till harvest Temperature threshold (T-threshold) for degree-days for degree-day accumulation Dekad of sowing/planting Scenario: Historical RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 Climate Period: 1981-2010 2011-2040 2041-2070 2071-2099 Climate Models: WFDEI GFDL-esm2 HadGEM2-es IPSL-CM5a MIROC-esm NorESM-m Agro-climatic indicator: Frost days Ice days Summer days Tropical nights Mean diurnal temperature range Mean daily mean temperature Mean daily max temperature Max daily max temperature Mean daily min temperature Min daily min temperature Precipitation total Wet days Heavy rain days Very heavy rain days Temperature threshold (T-threshold) for summer days counts (the temperature to reach for a day to be classified as a summer day) Crop: Winter wheat Spring wheat Soybean Maize 1st rice 2nd rice User defined (with crop parameters set by user) Winter wheat Spring wheat Soybean Maize 1st rice 2nd rice User defined (with crop parameters set by user) Winter wheat Spring wheat Soybean Maize 1st rice 2nd rice User defined (with crop parameters set by user) Crop parameters (if Crop = user defined, please refer to user documentation for further details): Degree-days for sowing till flowering Degree-days for sowing till harvest Temperature threshold (T-threshold) for degree-days for degree-day accumulation Dekad of sowing/planting Degree-days for sowing till flowering Degree-days for sowing till harvest Temperature threshold (T-threshold) for degree-days for degree-day accumulation Dekad of sowing/planting Degree-days for sowing till flowering Degree-days for sowing till harvest Temperature threshold (T-threshold) for degree-days for degree-day accumulation Dekad of sowing/planting Scenario: Historical RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 Historical RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 Historical RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 Climate Period: 1981-2010 2011-2040 2041-2070 2071-2099 1981-2010 2011-2040 2041-2070 2071-2099 1981-2010 2011-2040 2041-2070 2071-2099 Climate Models: WFDEI GFDL-esm2 HadGEM2-es IPSL-CM5a MIROC-esm NorESM-m WFDEI GFDL-esm2 HadGEM2-es IPSL-CM5a MIROC-esm NorESM-m WFDEI GFDL-esm2 HadGEM2-es IPSL-CM5a MIROC-esm NorESM-m Agro-climatic indicator: Frost days Ice days Summer days Tropical nights Mean diurnal temperature range Mean daily mean temperature Mean daily max temperature Max daily max temperature Mean daily min temperature Min daily min temperature Precipitation total Wet days Heavy rain days Very heavy rain days Frost days Ice days Summer days Tropical nights Mean diurnal temperature range Mean daily mean temperature Mean daily max temperature Max daily max temperature Mean daily min temperature Min daily min temperature Precipitation total Wet days Heavy rain days Very heavy rain days Frost days Ice days Summer days Tropical nights Mean diurnal temperature range Mean daily mean temperature Mean daily max temperature Max daily max temperature Mean daily min temperature Min daily min temperature Precipitation total Wet days Heavy rain days Very heavy rain days Temperature threshold (T-threshold) for summer days counts (the temperature to reach for a day to be classified as a summer day) INPUT VARIABLES Name Units Description Source Biologically effective degree days °C Sum of daily mean temperatures (TG) above 10°C and less than 30°C, over 10 days. Agroclimatic indicators Crop calendars and thermal time requirements Crop parameters derived from the FAO-GAEZ dataset, please refer to documentation for further details Brokered externally Frost days day Number of days per 10 days when TN < 0°C, where TN is the daily minimum temperature. This indicator provides information on frost damage. Agroclimatic indicators Heavy precipitation days day Number of days per 10 days when RR > 10mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Agroclimatic indicators Ice days day Number of days per 10 days when TX < 0°C, where TX is the daily maximum temperature. This indicator provides information on frost damage. Agroclimatic indicators Maximum of daily maximum temperature K Maximum value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Mean of daily maximum temperature K Mean value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Mean of daily mean temperature K Mean value of TG over 10 days, where TG is the daily mean temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Mean of daily minimum temperature K Mean value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Mean of diurnal temperature range °C Mean value of the daily difference between TX and TN (TX-TN) over 10 days, where TX and TN are daily maximum and minimum temperature respectively. This indicator provides information on climate variability and change. It also serves as a proxy for information on the clarity (transmittance) of the atmosphere. Agroclimatic indicators Minimum of daily minimum temperature K Minimum value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Precipitation sum mm Sum of RR over 10 days, where RR is the daily precipitation sum. This indicator provides information on possible water shortage or excess. Agroclimatic indicators Spatial crop mask Mask derived from the SPAM 2005 dataset, please refer to documentation for further details Brokered externally Summer days day Number of days per 10 days when TX > 25°C, where TX is the daily maximum temperature. This indicator provides an indication of the occurrence of heat stress. Agroclimatic indicators Tropical nights day Number of days per 10 days when TN > 20°C, where TN is the daily minimum temperature. This indicator provides an indication of occurrence of various pests. Agroclimatic indicators Very heavy precipitation days day Number of days per 10 days when RR > 20mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Agroclimatic indicators Wet days day Number of days per 10 days when RR > 1mm, where RR is the daily precipitation sum. This indicator provides information on intercepted reduction. Agroclimatic indicators INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Biologically effective degree days °C Sum of daily mean temperatures (TG) above 10°C and less than 30°C, over 10 days. Agroclimatic indicators Biologically effective degree days °C Sum of daily mean temperatures (TG) above 10°C and less than 30°C, over 10 days. Agroclimatic indicators Agroclimatic indicators Crop calendars and thermal time requirements Crop parameters derived from the FAO-GAEZ dataset, please refer to documentation for further details Brokered externally Crop calendars and thermal time requirements Crop parameters derived from the FAO-GAEZ dataset, please refer to documentation for further details Brokered externally Frost days day Number of days per 10 days when TN < 0°C, where TN is the daily minimum temperature. This indicator provides information on frost damage. Agroclimatic indicators Frost days day Number of days per 10 days when TN < 0°C, where TN is the daily minimum temperature. This indicator provides information on frost damage. Agroclimatic indicators Agroclimatic indicators Heavy precipitation days day Number of days per 10 days when RR > 10mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Agroclimatic indicators Heavy precipitation days day Number of days per 10 days when RR > 10mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Agroclimatic indicators Agroclimatic indicators Ice days day Number of days per 10 days when TX < 0°C, where TX is the daily maximum temperature. This indicator provides information on frost damage. Agroclimatic indicators Ice days day Number of days per 10 days when TX < 0°C, where TX is the daily maximum temperature. This indicator provides information on frost damage. Agroclimatic indicators Agroclimatic indicators Maximum of daily maximum temperature K Maximum value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Maximum of daily maximum temperature K Maximum value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Agroclimatic indicators Mean of daily maximum temperature K Mean value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Mean of daily maximum temperature K Mean value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Agroclimatic indicators Mean of daily mean temperature K Mean value of TG over 10 days, where TG is the daily mean temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Mean of daily mean temperature K Mean value of TG over 10 days, where TG is the daily mean temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Agroclimatic indicators Mean of daily minimum temperature K Mean value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Mean of daily minimum temperature K Mean value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Agroclimatic indicators Mean of diurnal temperature range °C Mean value of the daily difference between TX and TN (TX-TN) over 10 days, where TX and TN are daily maximum and minimum temperature respectively. This indicator provides information on climate variability and change. It also serves as a proxy for information on the clarity (transmittance) of the atmosphere. Agroclimatic indicators Mean of diurnal temperature range °C Mean value of the daily difference between TX and TN (TX-TN) over 10 days, where TX and TN are daily maximum and minimum temperature respectively. This indicator provides information on climate variability and change. It also serves as a proxy for information on the clarity (transmittance) of the atmosphere. Agroclimatic indicators Agroclimatic indicators Minimum of daily minimum temperature K Minimum value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Minimum of daily minimum temperature K Minimum value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Agroclimatic indicators Precipitation sum mm Sum of RR over 10 days, where RR is the daily precipitation sum. This indicator provides information on possible water shortage or excess. Agroclimatic indicators Precipitation sum mm Sum of RR over 10 days, where RR is the daily precipitation sum. This indicator provides information on possible water shortage or excess. Agroclimatic indicators Agroclimatic indicators Spatial crop mask Mask derived from the SPAM 2005 dataset, please refer to documentation for further details Brokered externally Spatial crop mask Mask derived from the SPAM 2005 dataset, please refer to documentation for further details Brokered externally Summer days day Number of days per 10 days when TX > 25°C, where TX is the daily maximum temperature. This indicator provides an indication of the occurrence of heat stress. Agroclimatic indicators Summer days day Number of days per 10 days when TX > 25°C, where TX is the daily maximum temperature. This indicator provides an indication of the occurrence of heat stress. Agroclimatic indicators Agroclimatic indicators Tropical nights day Number of days per 10 days when TN > 20°C, where TN is the daily minimum temperature. This indicator provides an indication of occurrence of various pests. Agroclimatic indicators Tropical nights day Number of days per 10 days when TN > 20°C, where TN is the daily minimum temperature. This indicator provides an indication of occurrence of various pests. Agroclimatic indicators Agroclimatic indicators Very heavy precipitation days day Number of days per 10 days when RR > 20mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Agroclimatic indicators Very heavy precipitation days day Number of days per 10 days when RR > 20mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Agroclimatic indicators Agroclimatic indicators Wet days day Number of days per 10 days when RR > 1mm, where RR is the daily precipitation sum. This indicator provides information on intercepted reduction. Agroclimatic indicators Wet days day Number of days per 10 days when RR > 1mm, where RR is the daily precipitation sum. This indicator provides information on intercepted reduction. Agroclimatic indicators Agroclimatic indicators OUTPUT VARIABLES Name Units Description Accumulated climate indicators As indicator input Calculated climate indicators for the four successive crop stages: vegetative, around flowering, reproductive, before maturity Flowering date dekad Calculated dekad to reach flowering (anthesis), starting from sowing date Harvest date dekad Calculated dekad to reach harvest (maturity), starting from sowing date Sow date dekad Prescribed average sowing dekad, read from respective hidden crop files OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Accumulated climate indicators As indicator input Calculated climate indicators for the four successive crop stages: vegetative, around flowering, reproductive, before maturity Accumulated climate indicators As indicator input Calculated climate indicators for the four successive crop stages: vegetative, around flowering, reproductive, before maturity Flowering date dekad Calculated dekad to reach flowering (anthesis), starting from sowing date Flowering date dekad Calculated dekad to reach flowering (anthesis), starting from sowing date Harvest date dekad Calculated dekad to reach harvest (maturity), starting from sowing date Harvest date dekad Calculated dekad to reach harvest (maturity), starting from sowing date Sow date dekad Prescribed average sowing dekad, read from respective hidden crop files Sow date dekad Prescribed average sowing dekad, read from respective hidden crop files 314 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-gridded-l4-sea-surface-heights-and-derived-1 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_L4_NRT_OBSERVATIONS_008_046 GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT Short description: Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications. This product is processed by the DUACS multimission altimeter data processing system. http://duacs.cls.fr http://duacs.cls.fr DOI (product) :https://doi.org/10.48670/moi-00149 https://doi.org/10.48670/moi-00149 315 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-ghg-reanalysis-egg4-monthly https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-ghg-reanalysis-egg4-monthly cams-global-ghg-reanalysis-egg4-monthly This dataset is part of the ECMWF Atmospheric Composition Reanalysis focusing on long-lived greenhouse gases: carbon dioxide (CO2) and methane (CH4). The emissions and natural fluxes at the surface are crucial for the evolution of the long-lived greenhouse gases in the atmosphere. In this dataset the CO2 fluxes from terrestrial vegetation are modelled in order to simulate the variability across a wide range of scales from diurnal to inter-annual. The CH4 chemical loss is represented by a climatological loss rate and the emissions at the surface are taken from a range of datasets. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. The assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available. The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with. The observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. For this reason, EAC4 is only available from 2003 onwards. The analysis procedure assimilates data in a window of 12 hours using the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window. This page provides monthly mean values, worldwide. Original 3-hourly outputs can be accessed at: https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-ghg-reanalysis-egg4 https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-ghg-reanalysis-egg4 More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 0.75°x0.75° Vertical coverage Surface, total column, model levels and pressure levels. Vertical resolution 60 model levels. Pressure levels: 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa Temporal coverage 2003 to 2020 Temporal resolution monthly File format GRIB, NetCDF Versions Only one version Update frequency Twice a year with 4-6 month delay DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.75°x0.75° Horizontal resolution 0.75°x0.75° Vertical coverage Surface, total column, model levels and pressure levels. Vertical coverage Surface, total column, model levels and pressure levels. Vertical resolution 60 model levels. Pressure levels: 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa Vertical resolution 60 model levels. Pressure levels: 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa Temporal coverage 2003 to 2020 Temporal coverage 2003 to 2020 Temporal resolution monthly Temporal resolution monthly File format GRIB, NetCDF File format GRIB, NetCDF Versions Only one version Versions Only one version Update frequency Twice a year with 4-6 month delay Update frequency Twice a year with 4-6 month delay MAIN VARIABLES Name Units 2m dewpoint temperature K 2m temperature K Accumulated carbon dioxide ecosystem respiration kg m-2 Accumulated carbon dioxide gross primary production kg m-2 Accumulated carbon dioxide net ecosystem exchange kg m-2 CH4 column-mean molar fraction ppb CO2 column-mean molar fraction ppm Carbon dioxide kg kg-1 Flux of carbon dioxide ecosystem respiration kg m-2 s-1 Flux of carbon dioxide gross primary production kg m-2 s-1 Flux of carbon dioxide net ecosystem exchange kg m-2 s-1 Geopotential m2 s-2 Mean sea level pressure Pa Methane kg kg-1 Relative humidity % Sea surface temperature K Sea-ice cover (0 - 1) Snow albedo (0 - 1) Snow depth m of water equivalent Temperature K Total column water kg m-2 Total column water vapour kg m-2 Vertical velocity Pa s-1 MAIN VARIABLES MAIN VARIABLES Name Units Name Units 2m dewpoint temperature K 2m dewpoint temperature K 2m temperature K 2m temperature K Accumulated carbon dioxide ecosystem respiration kg m-2 Accumulated carbon dioxide ecosystem respiration kg m-2 Accumulated carbon dioxide gross primary production kg m-2 Accumulated carbon dioxide gross primary production kg m-2 Accumulated carbon dioxide net ecosystem exchange kg m-2 Accumulated carbon dioxide net ecosystem exchange kg m-2 CH4 column-mean molar fraction ppb CH4 column-mean molar fraction ppb CO2 column-mean molar fraction ppm CO2 column-mean molar fraction ppm Carbon dioxide kg kg-1 Carbon dioxide kg kg-1 Flux of carbon dioxide ecosystem respiration kg m-2 s-1 Flux of carbon dioxide ecosystem respiration kg m-2 s-1 Flux of carbon dioxide gross primary production kg m-2 s-1 Flux of carbon dioxide gross primary production kg m-2 s-1 Flux of carbon dioxide net ecosystem exchange kg m-2 s-1 Flux of carbon dioxide net ecosystem exchange kg m-2 s-1 Geopotential m2 s-2 Geopotential m2 s-2 Mean sea level pressure Pa Mean sea level pressure Pa Methane kg kg-1 Methane kg kg-1 Relative humidity % Relative humidity % Sea surface temperature K Sea surface temperature K Sea-ice cover (0 - 1) Sea-ice cover (0 - 1) Snow albedo (0 - 1) Snow albedo (0 - 1) Snow depth m of water equivalent Snow depth m of water equivalent Temperature K Temperature K Total column water kg m-2 Total column water kg m-2 Total column water vapour kg m-2 Total column water vapour kg m-2 Vertical velocity Pa s-1 Vertical velocity Pa s-1 316 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/pan-european-high-resolution-image-mosaic-2012-true https://land.copernicus.eu/imagery-in-situ/european-image-mosaics/high-resolution/Image%202012/COV%202 Pan-European High Resolution Image Mosaic 2012 - True Colour, Coverage 2 (20 m), Febr. 2015 The pan-European High Resolution (HR) Image Mosaic 2006 provides HR2 (High Resolution: 20 meter) coverage over Europe for the continuation of Corine Land Cover like exercises and for the generation of HR layers by the EU and EEA. The surface covered by the image dataset is 5.8 million square kilometres and has a spatial resolution of 20 meters. The imagery is composed during specific acquisition windows in 2011, 2012 and 2013. Coverage 2 acquisitions are expected to be 6 weeks away from Coverage 1, down to a minimum of 2 weeks for northern countries, including United Kingdom. The ± 6 weeks criteria might not be strictly applied over Atlantic Islands and French DOMs (seasonal changes are limited in the equatorial DOMs). Images are derived from the following satellite sensors: RapidEye constellation The mosaic primarily is used as input data in the production of various Copernicus Land Monitoring Service (CLMS) datasets and services, such as land cover maps and high resolution layers on land cover characteristic and can be also useful for CLMS users for visualizations and classifications on land. The input imagery for the creation of the mosaic is provided by ESA. Due to license restrictions, HR Image Mosaic 2006 is only available as a web service (WMS), and not for data download. 317 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-ice-sheet-mass-balance https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-ice-sheet-mass-balance satellite-ice-sheet-mass-balance This dataset provides a complete archive of monthly mass change timeseries for the drainage basins of the Antarctic and Greenland Ice Sheets from NASA's Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) mission, which have operated from 2002 to current data. It is guided by the Global Climate Observing System targets for the Ice Sheets Mass Change Essential Climate Variable. The GRACE twin satellites measured changes in the Earth’s gravitational field over time and inferred from them mass changes over the ice sheets, using the regional integration approach. A timeseries of mass change is provided for each ice sheet, and also for each drainage basin separately. The dataset complements ice change measurements made by other methods, such as the surface elevation change rate detailed in the Climate Data Store (CDS) dataset: 'Ice sheet surface elevation change rate for Greenland and Antarctica from 1992 to present from derived satellite observations', which covers the same areas and includes the GRACE mission timespan. The data was brokered from ESA’s Greenland and ESA's Antarctic Ice Sheet Climate Change Initiative projects. DATA DESCRIPTION Data type Time series Horizontal coverage Average over the Antarctic and Greenland regions including the major drainage basins Horizontal resolution One time series per basin Vertical coverage Surface Vertical resolution Single level Temporal coverage From 2003 to 2020 Temporal resolution Monthly File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions 3.0 (for the period from 2003 to 2017 the data correspondes to the previous version 2.0) Update frequency Annually DATA DESCRIPTION DATA DESCRIPTION Data type Time series Data type Time series Horizontal coverage Average over the Antarctic and Greenland regions including the major drainage basins Horizontal coverage Average over the Antarctic and Greenland regions including the major drainage basins Horizontal resolution One time series per basin Horizontal resolution One time series per basin Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage From 2003 to 2020 Temporal coverage From 2003 to 2020 Temporal resolution Monthly Temporal resolution Monthly File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions 3.0 (for the period from 2003 to 2017 the data correspondes to the previous version 2.0) Versions 3.0 (for the period from 2003 to 2017 the data correspondes to the previous version 2.0) Update frequency Annually Update frequency Annually MAIN VARIABLES The names of the variables in the netCDF files differ from the names used in this Catalogue entry. For details on the variable names within the netCDF file please refer to the product user guide and specification in the documentation. Name Units Description Antarctic basin X ice mass balance Gt Gravimetric mass balance for basin X of the Antarctic ice sheet where X represents one of the 27 Antarctic basins. Antarctic basin definition Dimensionless Zwally definition of major drainage basins of the Antarctic ice sheet. Antarctic ice mass balance Gt Total gravimetric mass balance for the Antarctic ice sheet and ice shelves. Antarctic peninsula ice mass balance Gt Total gravimetric mass balance for the Antarctic Peninsula. East Antactic ice mass balance Gt Total gravimetric mass balance for East Antarctica. Greenland basin X ice mass balance Gt Gravimetric mass balance for basin X of the Greenland ice sheet where X represents one of the 8 Greenland basins. Greenland basin definition Dimensionless Zwally definition of major drainage basins of the Greenland ice sheet. Greenland ice mass balance Gt Total gravimetric mass balance for Greenland ice sheet. West Antactic ice mass balance Gt Total gravimetric mass balance for West Antarctica. MAIN VARIABLES MAIN VARIABLES The names of the variables in the netCDF files differ from the names used in this Catalogue entry. For details on the variable names within the netCDF file please refer to the product user guide and specification in the documentation. The names of the variables in the netCDF files differ from the names used in this Catalogue entry. For details on the variable names within the netCDF file please refer to the product user guide and specification in the documentation. Name Units Description Name Units Description Antarctic basin X ice mass balance Gt Gravimetric mass balance for basin X of the Antarctic ice sheet where X represents one of the 27 Antarctic basins. Antarctic basin X ice mass balance Gt Gravimetric mass balance for basin X of the Antarctic ice sheet where X represents one of the 27 Antarctic basins. Antarctic basin definition Dimensionless Zwally definition of major drainage basins of the Antarctic ice sheet. Antarctic basin definition Dimensionless Zwally definition of major drainage basins of the Antarctic ice sheet. Antarctic ice mass balance Gt Total gravimetric mass balance for the Antarctic ice sheet and ice shelves. Antarctic ice mass balance Gt Total gravimetric mass balance for the Antarctic ice sheet and ice shelves. Antarctic peninsula ice mass balance Gt Total gravimetric mass balance for the Antarctic Peninsula. Antarctic peninsula ice mass balance Gt Total gravimetric mass balance for the Antarctic Peninsula. East Antactic ice mass balance Gt Total gravimetric mass balance for East Antarctica. East Antactic ice mass balance Gt Total gravimetric mass balance for East Antarctica. Greenland basin X ice mass balance Gt Gravimetric mass balance for basin X of the Greenland ice sheet where X represents one of the 8 Greenland basins. Greenland basin X ice mass balance Gt Gravimetric mass balance for basin X of the Greenland ice sheet where X represents one of the 8 Greenland basins. Greenland basin definition Dimensionless Zwally definition of major drainage basins of the Greenland ice sheet. Greenland basin definition Dimensionless Zwally definition of major drainage basins of the Greenland ice sheet. Greenland ice mass balance Gt Total gravimetric mass balance for Greenland ice sheet. Greenland ice mass balance Gt Total gravimetric mass balance for Greenland ice sheet. West Antactic ice mass balance Gt Total gravimetric mass balance for West Antarctica. West Antactic ice mass balance Gt Total gravimetric mass balance for West Antarctica. RELATED VARIABLES Uncertainty estimates are provided for all the main variables. For more details please refer to the documentation. RELATED VARIABLES RELATED VARIABLES Uncertainty estimates are provided for all the main variables. For more details please refer to the documentation. Uncertainty estimates are provided for all the main variables. For more details please refer to the documentation. 318 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-situ-reprocessed-carbon-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_GLO_BGC_CARBON_DISCRETE_MY_013_050 Global Ocean - In Situ reprocessed carbon observations - SOCATv2022 / GLODAPv2.2022 Short description: Global Ocean- in-situ reprocessed Carbon observations. This product contains observations and gridded files from two up-to-date carbon and biogeochemistry community data products: Surface Ocean Carbon ATlas SOCATv2021 and GLobal Ocean Data Analysis Project GLODAPv2.2021. The SOCATv2022-OBS dataset contains >25 million observations of fugacity of CO2 of the surface global ocean from 1957 to early 2022. The quality control procedures are described in Bakker et al. (2016). These observations form the basis of the gridded products included in SOCATv2020-GRIDDED: monthly, yearly and decadal averages of fCO2 over a 1x1 degree grid over the global ocean, and a 0.25x0.25 degree, monthly average for the coastal ocean. GLODAPv2.2022-OBS contains >1 million observations from individual seawater samples of temperature, salinity, oxygen, nutrients, dissolved inorganic carbon, total alkalinity and pH from 1972 to 2020. These data were subjected to an extensive quality control and bias correction described in Olsen et al. (2020). GLODAPv2-GRIDDED contains global climatologies for temperature, salinity, oxygen, nitrate, phosphate, silicate, dissolved inorganic carbon, total alkalinity and pH over a 1x1 degree horizontal grid and 33 standard depths using the observations from the previous iteration of GLODAP, GLODAPv2. SOCAT and GLODAP are based on community, largely volunteer efforts, and the data providers will appreciate that those who use the data cite the corresponding articles (see References below) in order to support future sustainability of the data products. DOI (product) :https://doi.org/10.48670/moi-00035 https://doi.org/10.48670/moi-00035 319 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-radiative-forcing-auxilliary-variables https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-radiative-forcing-auxilliary-variables cams-global-radiative-forcing-auxilliary-variables This dataset provides aerosol optical depths and aerosol-radiation radiative effects for four different aerosol origins: anthropogenic, mineral dust, marine, and land-based fine-mode natural aerosol. The latter mostly consists of biogenic aerosols. The data are a necessary complement to the "CAMS global radiative forcings" dataset (see "Related Data"). The calculation of aerosol radiative forcing requires a discrimination between aerosol of anthropogenic and natural origin. However, the CAMS reanalysis, which is used to provide the aerosol concentrations, does not make this distinction. The anthropogenic fraction was therefore derived by a method which uses aerosol size as a proxy for aerosol origin. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 1° x 1° Vertical coverage Vertically integrated optical depths. Radiative effects at two levels: surface and top-of-atmosphere Vertical resolution Single level Temporal coverage 2003-2017 Temporal resolution Daily, in monthly files File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions 1.5 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 1° x 1° Horizontal resolution 1° x 1° Vertical coverage Vertically integrated optical depths. Radiative effects at two levels: surface and top-of-atmosphere Vertical coverage Vertically integrated optical depths. Radiative effects at two levels: surface and top-of-atmosphere Vertical resolution Single level Vertical resolution Single level Temporal coverage 2003-2017 Temporal coverage 2003-2017 Temporal resolution Daily, in monthly files Temporal resolution Daily, in monthly files File format NetCDF 4 File format NetCDF 4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions 1.5 Versions 1.5 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Aerosol absorption optical depth at 550 nm Dimensionless Aerosol optical depth at 550 nm Dimensionless Radiative effect of aerosol-radiation interations W m-2 MAIN VARIABLES MAIN VARIABLES Name Units Name Units Aerosol absorption optical depth at 550 nm Dimensionless Aerosol absorption optical depth at 550 nm Dimensionless Aerosol optical depth at 550 nm Dimensionless Aerosol optical depth at 550 nm Dimensionless Radiative effect of aerosol-radiation interations W m-2 Radiative effect of aerosol-radiation interations W m-2 320 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterrranean-outflow-water-index-reanalysis-multi http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=IBI_OMI_WMHE_mow Mediterrranean Outflow Water Index from Reanalysis & Multi-Observations Reprocessing DEFINITION Variations of the Mediterranean Outflow Water at 1000 m depth are monitored through area-averaged salinity anomalies in specifically defined boxes. The salinity data are extracted from several CMEMS products and averaged in the corresponding monitoring domain: * IBI-MYP: IBI_MULTIYEAR_PHY_005_002 * IBI-NRT: IBI_ANALYSISFORECAST_PHYS_005_001 * GLO-MYP: GLOBAL_REANALYSIS_PHY_001_030 * CORA: INSITU_GLO_TS_REP_OBSERVATIONS_013_002_b * ARMOR: MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 The anomalies of salinity have been computed relative to the monthly climatology obtained from IBI-MYP. Outcomes from diverse products are combined to deliver a unique multi-product result. Multi-year products (IBI-MYP, GLO,MYP, CORA, and ARMOR) are used to show an ensemble mean and the standard deviation of members in the covered period. The IBI-NRT short-range product is not included in the ensemble, but used to provide the deterministic analysis of salinity anomalies in the most recent year. CONTEXT The Mediterranean Outflow Water is a saline and warm water mass generated from the mixing processes of the North Atlantic Central Water and the Mediterranean waters overflowing the Gibraltar sill (Daniault et al., 1994). The resulting water mass is accumulated in an area west of the Iberian Peninsula (Daniault et al., 1994) and spreads into the North Atlantic following advective pathways (Holliday et al. 2003; Lozier and Stewart 2008, de Pascual-Collar et al., 2019). The importance of the heat and salt transport promoted by the Mediterranean Outflow Water flow has implications beyond the boundaries of the Iberia-Biscay-Ireland domain (Reid 1979, Paillet et al. 1998, van Aken 2000). For example, (i) it contributes substantially to the salinity of the Norwegian Current (Reid 1979), (ii) the mixing processes with the Labrador Sea Water promotes a salt transport into the inner North Atlantic (Talley and MacCartney, 1982; van Aken, 2000), and (iii) the deep anti-cyclonic Meddies developed in the African slope is a cause of the large-scale westward penetration of Mediterranean salt (Iorga and Lozier, 1999). Several studies have demonstrated that the core of Mediterranean Outflow Water is affected by inter-annual variability. This variability is mainly caused by a shift of the MOW dominant northward-westward pathways (Bozec et al. 2011), it is correlated with the North Atlantic Oscillation (Bozec et al. 2011) and leads to the displacement of the boundaries of the water core (de Pascual-Collar et al., 2019). The variability of the advective pathways of MOW is an oceanographic process that conditions the destination of the Mediterranean salt transport in the North Atlantic. Therefore, monitoring the Mediterranean Outflow Water variability becomes decisive to have a proper understanding of the climate system and its evolution (e.g. Bozec et al. 2011, Pascual-Collar et al. 2019). The CMEMS IBI-OMI_WMHE_mow product is aimed to monitor the inter-annual variability of the Mediterranean Outflow Water in the North Atlantic. The objective is the establishment of a long-term monitoring program to observe the variability and trends of the Mediterranean water mass in the IBI regional seas. To do that, the salinity anomaly is monitored in key areas selected to represent the main reservoir and the three main advective spreading pathways. More details and a full scientific evaluation can be found in the CMEMS Ocean State report Pascual et al., 2018 and de Pascual-Collar et al. 2019. CMEMS KEY FINDINGS The absence of long-term trends in the monitoring domain Reservoir (b) suggests the steadiness of water mass properties involved on the formation of Mediterranean Outflow Water. Results obtained in monitoring box North (c) present an alternance of periods with positive and negative anomalies. The last negative period started in 2016 reaching up to the present. Such negative events are linked to the decrease of the northward pathway of Mediterranean Outflow Water (Bozec et al., 2011), which appears to return to steady conditions in 2020 and 2021. Results for box West (d) reveal a cycle of negative (2015-2017) and positive (2017 up to the present) anomalies. The positive anomalies of salinity in this region are correlated with an increase of the westward transport of salinity into the inner North Atlantic (de Pascual-Collar et al., 2019), which appear to be maintained for years 2020-2021. Results in monitoring boxes North and West are consistent with independent studies (Bozec et al., 2011; and de Pascual-Collar et al., 2019), suggesting a westward displacement of Mediterranean Outflow Water and the consequent contraction of the northern boundary. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00258 https://doi.org/10.48670/moi-00258 321 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/european-seas-gridded-l4-sea-surface-heights-and-derived http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_EUR_PHY_L4_NRT_OBSERVATIONS_008_060 EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES NRT Short description: Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in near-real time applications. This product is processed by the DUACS multimission altimeter data processing system. http://duacs.cls.fr http://duacs.cls.fr DOI (product) :https://doi.org/10.48670/moi-00142 https://doi.org/10.48670/moi-00142 322 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-major-baltic-inflow-timedepth-evolution-sto2 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTIC_OMI_WMHE_mbi_sto2tz_gotland Baltic Sea Major Baltic Inflow: time/depth evolution S,T,O2 from Observations Reprocessing "''DEFINITION Major Baltic Inflows bring large volumes of saline and oxygen-rich water into the bottom layers of the deep basins of the central Baltic Sea, i.e. the Gotland Basin. These Major Baltic Inflows occur seldom, sometimes many years apart (Mohrholz, 2018). The Major Baltic Inflow OMI consists of the time series of the bottom layer salinity in the Arkona Basin and in the Bornholm Basin (CMEMS OMI Baltic Sea Major Baltic Inflow: bottom salinity) and the time-depth plot of temperature, salinity and dissolved oxygen concentration in the Gotland Basin. Temperature, salinity and dissolved oxygen profiles in the Gotland Basin enable to estimate the amount of the Major Baltic Inflow water that has reached central Baltic, the depth interval of which has been the most affected, and how much the oxygen conditions have been improved. CONTEXT The Baltic Sea is a huge brackish water basin in Northern Europe whose salinity is controlled by its freshwater budget and by the water exchange with the North Sea (e.g. Neumann et al., 2017). This implies that fresher water lies on top of water with higher salinity. The saline water inflows to the Baltic Sea through the Danish Straits, especially the Major Baltic Inflows, shape hydrophysical conditions in the Gotland Basin of the central Baltic Sea, which in turn have a substantial influence on marine ecology on different trophic levels (Bergen et al., 2018; Raudsepp et al.,2019). In the absence of the Major Baltic Inflows, oxygen in the deeper layers of the Gotland Basin is depleted and replaced by hydrogen sulphide (e.g., Savchuk, 2018). As the Baltic Sea is connected to the North Sea only through very narrow and shallow channels in the Danish Straits, inflows of high salinity and oxygenated water into the Baltic occur only intermittently (e.g., Mohrholz, 2018). Long-lasting periods of oxygen depletion in the deep layers of the central Baltic Sea accompanied by a salinity decline and overall weakening of the vertical stratification are referred to as stagnation periods. Extensive stagnation periods occurred in the 1920s/1930s, in the 1950s/1960s and in the 1980s/beginning of 1990s (BACCII Author Team, 2015). CMEMS KEY FINDINGS Major Baltic Inflows in 1993, 2002 and 2014 show a very clear signal in the Gotland Basin, where Major Baltic Inflow events affect the water salinity, temperature and dissolved oxygen conditions up to 100-m depth. Each of the Major Baltic Inflows results in the increase of deep layer salinity in the Gotland Basin right after the event occurs, but maximum bottom salinities are detected about 1.5 years later. The periods with elevated salinity are rather long-lasting after the Major Baltic Inflows (about three years). Since 2017, the salinity below 150 metre depth has decreased, but the halocline has pushed upwards, which indicates saline water transport to the intermediate layers of the Gotland Basin. Usually, temperature drops right after the Major Baltic Inflow occurs, which indicates that cold water from adjacent upstream areas submerges to the bottom in the Gotland Deep. During the period of 1993-1997, deep water temperature stayed relatively low (less than 6 °C). Starting from 1998, the deep water has become warmer than during a previous period. Even moderate inflows, like in 1997/98, 2006/07 and 2018/19 bring warmer water to the bottom layer of the Gotland Basin. Since 2019, warm water (more than 7 °C) has occupied the layer below 100-m depth. Compared to the year 1993, the water temperature below the halocline has increased about 2 °C. Also, the temperature of the cold intermediate layer has increased over the period 1993-2021. Oxygen concentrations start to decline quite rapidly after the temporary oxygenation of the bottom waters. In 2014, the reasons were the lack of smaller inflows after the Major Baltic Inflow that could supply more oxygenated water to the Gotland Basin (Neumann et al., 2017) and intensification of biological oxygen consumption (Savchuk, 2018; Meier et al., 2018). In addition, warm water may increase oxygen consumption in the deep layer and an enhancement of anoxia. In 2021, oxygen was completely consumed below the depth of 75 metres. DOI (product):https://doi.org/10.48670/moi-00210 https://doi.org/10.48670/moi-00210 323 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/medium-resolution-vegetation-phenology-and-productivity-5 https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum_d1-0.pdf Medium Resolution Vegetation Phenology and Productivity: Large integral (raster 500m), Oct. 2022 The raster file is the temporal trend in above ground vegetation biomass productivity. The vegetation productivity dataset is based on the time series of the Plant Phenology Index (PPI) derived from the MODIS BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR). The PPI index is optimized for efficient monitoring of vegetation phenology and is derived from the source MODIS data using radiative transfer solutions applied to the reflectance in visible-red and near infrared spectral domains. The productivity indicator is based on calculating the area under the PPI temporal curve above the baseline (large integral - LINT) using the TIMESAT software for each year between and including 2000 and 2021. The Total Productivity (TPROD), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Total Productivity (TPROD), or large integral, is the growing season integral computed as the sum of all daily Plant Phenology Index values between the dates of the season start (SOSD) and end (EOSD). The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Total Productivity (TPROD) is one of the 13 parameters. The full list is available in the Product User Manual: https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… The Total Productivity (TPROD) time series dataset is made available as raster files with 500x 500m resolution, in ETRS89-LAEA projection corresponding to the MCD43 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2000 onwards. It is updated in the first quarter of each year. The full on-line access to open and free data for this resource will be made available by the end of 2022. Until then the data will be made available 'on-demand' by filling in the form at: https://land.copernicus.eu/contact-form https://land.copernicus.eu/contact-form 324 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/medium-resolution-vegetation-phenology-and-productivity-6 https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum_d1-0.pdf Medium Resolution Vegetation Phenology and Productivity: Start-of-season value (raster 500m), Oct. 2022 The Start-of-Season Value (SOSV), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European Medium Resolution Vegetation Phenology and Productivity (MR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Start-of-Season Value (SOSV) provides the value of the Plant Phenology Index (PPI) at the start of the vegetation growing season. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Start-of-Season Value (SOSV) is one of the 13 parameters. The full list is available in the Product User Manual: https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… The Start-of-Season Value (SOSV) time series dataset is made available as raster files with 500x 500m resolution, in ETRS89-LAEA projection corresponding to the MCD43 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2000 onwards. It is updated in the first quarter of each year. The full on-line access to open and free data for this resource will be made available by the end of 2022. Until then the data will be made available 'on-demand' by filling in the form at: https://land.copernicus.eu/contact-form https://land.copernicus.eu/contact-form 325 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-carra-model-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-carra-model-levels reanalysis-carra-model-levels The C3S Arctic Regional Reanalysis (CARRA) dataset contains 3-hourly analyses and hourly short term forecasts of atmospheric and surface meteorological variables (temperature, humidity, wind, cloud, precipitation and turbulent kinetic energy) at 2.5 km resolution. Additionally, forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC are available. The dataset includes two domains. The West domain covers Greenland, the Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, the Greenland Sea, and parts of Svalbard. The East domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia. The dataset has been produced with the use of the HARMONIE-AROME state-of-the-art non-hydrostatic regional numerical weather prediction model. High resolution reanalysis for the Arctic region is particularly important because the climate change is more pronounced in the Arctic region than elsewhere in the Earth. This fact calls for a better description of this region providing additional details with respect to the global reanalyses (ERA5 for instance). The additional information is provided by the higher horizontal resolution, more local observations (from the Nordic countries and Greenland), better description of surface characteristics (high resolution satellite and physiographic data), high resolution non-hydrostatic dynamics and improved physical parameterisation of clouds and precipitation in particular. The inputs to CARRA reanalysis are the observations, the ERA5 global reanalysis as lateral boundary conditions and the physiographic datasets describing the surface characteristics of the model. The observation values and information about their quality are used together to constrain the reanalysis where observations are available and provide information for the data assimilation system in areas in where less observations are available. More details about the reanalysis dataset and the extensive input data are given in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Horizontal coverage West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia Horizontal resolution 2.5km x 2.5km Vertical coverage From approximately 12m above the surface to a height of 10 hPa Vertical resolution 65 hybrid atmospheric model leves are included. These are defined in the system documenation under the Documentation tab above Temporal coverage From 1991 to present Temporal resolution 3-hourly analysis data. Hourly forecast data File format GRIB2 Update frequency Monthly. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Projection Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Horizontal coverage West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia Horizontal coverage West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia Horizontal resolution 2.5km x 2.5km Horizontal resolution 2.5km x 2.5km Vertical coverage From approximately 12m above the surface to a height of 10 hPa Vertical coverage From approximately 12m above the surface to a height of 10 hPa Vertical resolution 65 hybrid atmospheric model leves are included. These are defined in the system documenation under the Documentation tab above Vertical resolution 65 hybrid atmospheric model leves are included. These are defined in the system documenation under the Documentation tab above Temporal coverage From 1991 to present Temporal coverage From 1991 to present Temporal resolution 3-hourly analysis data. Hourly forecast data Temporal resolution 3-hourly analysis data. Hourly forecast data File format GRIB2 File format GRIB2 Update frequency Monthly. Update frequency Monthly. MAIN VARIABLES Name Units Description Cloud cover % Fraction of the grid box covered by clouds (liquid and ice) at the atmospheric model levels. Graupel kg kg-1 Mass fraction of graupel at the atmospheric pressure levels. Graupel is snow particles that have been melted and partially or completely refrozen. Hail is included in this precipitation type. Specific cloud ice water content kg kg-1 Mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud liquid water content kg kg-1 Mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This variable provides the average value for a grid box. Specific cloud snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific humidity kg kg-1 Mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. Temperature K Air temperature at the atmospheric model levels. Turbulent kinetic energy m2 s-2 Root-mean-square of the velocity fluctuations of the atmospheric large scale flow. U-component of wind m s-1 The zonal (u-component) wind components are defined in terms of the local grid orientation, which differs from geographic east-west direction.If the grid axes were aligned with geographic east directions, wind speeds towards east would be positive and wind speeds towards west would be negative. V-component of wind m s-1 The meridional (v-component) wind components are defined in terms of the local grid orientation, which differs from geographic north-south direction. If the grid axes were aligned with north directions, wind speeds towards north would be positive and wind speeds towards south would be negative. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Cloud cover % Fraction of the grid box covered by clouds (liquid and ice) at the atmospheric model levels. Cloud cover % Fraction of the grid box covered by clouds (liquid and ice) at the atmospheric model levels. Graupel kg kg-1 Mass fraction of graupel at the atmospheric pressure levels. Graupel is snow particles that have been melted and partially or completely refrozen. Hail is included in this precipitation type. Graupel kg kg-1 Mass fraction of graupel at the atmospheric pressure levels. Graupel is snow particles that have been melted and partially or completely refrozen. Hail is included in this precipitation type. Specific cloud ice water content kg kg-1 Mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud ice water content kg kg-1 Mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud liquid water content kg kg-1 Mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud liquid water content kg kg-1 Mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This variable provides the average value for a grid box. Specific cloud rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This variable provides the average value for a grid box. Specific cloud snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific cloud snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific humidity kg kg-1 Mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. Specific humidity kg kg-1 Mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. Temperature K Air temperature at the atmospheric model levels. Temperature K Air temperature at the atmospheric model levels. Turbulent kinetic energy m2 s-2 Root-mean-square of the velocity fluctuations of the atmospheric large scale flow. Turbulent kinetic energy m2 s-2 Root-mean-square of the velocity fluctuations of the atmospheric large scale flow. U-component of wind m s-1 The zonal (u-component) wind components are defined in terms of the local grid orientation, which differs from geographic east-west direction.If the grid axes were aligned with geographic east directions, wind speeds towards east would be positive and wind speeds towards west would be negative. U-component of wind m s-1 The zonal (u-component) wind components are defined in terms of the local grid orientation, which differs from geographic east-west direction.If the grid axes were aligned with geographic east directions, wind speeds towards east would be positive and wind speeds towards west would be negative. V-component of wind m s-1 The meridional (v-component) wind components are defined in terms of the local grid orientation, which differs from geographic north-south direction. If the grid axes were aligned with north directions, wind speeds towards north would be positive and wind speeds towards south would be negative. V-component of wind m s-1 The meridional (v-component) wind components are defined in terms of the local grid orientation, which differs from geographic north-south direction. If the grid axes were aligned with north directions, wind speeds towards north would be positive and wind speeds towards south would be negative. 326 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/high-resolution-snow-and-ice-monitoring-persistent-snow https://www.wekeo.eu/ High Resolution Snow and Ice Monitoring: Persistent Snow Area - LAEA projection (raster 20m) The Copernicus Persistent Snow Area (PSA) product is generated annually for the entire EEA38 and the United Kingdom, based on the Fractional Snow Cover (FSC) information. It provides the extent of the persistent snow cover, i.e. the area where snow is present throughout the hydrological year with a spatial resolution of 20 m x 20 m. This metadata refers to the PSA product distributed in raster files as tiles aligned with the Pan-European High-Resolution Layers in the European 20 m x 20 m grid (ETRS89 LAEA - EPSG: 3035). It is also available in another projection as tiles aligned with Sentinel-2 (UTM/WGS84) at 20 m x 20 m GSD. It is typically released on an annual basis with a release date in October, after the end of the hydrological year. Each product is composed of two separate GeoTIFF files corresponding to the different layers of the product, and another metadata file of the product. The first layer provides the PSA (with "no data" pixels in case of cloud conditions for all observations over the period of computation) whereas the second layer provides the QC (Quality Control, with the confidence level on the PSA layer). PSA is one of the products of the pan-European High-Resolution Snow & Ice service (HR-S&I), which are provided at high spatial resolution (20 m x 20 m and 60 m x 60 m), from the Sentinel-2 and Sentinel-1 constellations data from September 1, 2016 onwards. Visit https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… to get more information on the different HR-S&I products (Snow products : FSC, WDS, SWS, GFSC, and PSA. Ice products : RLIE and ARLIE) https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… 327 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-sea-surface-temperature-multi-sensor-l3 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010 Global Ocean - Sea Surface Temperature Multi-sensor L3 Observations Short description: For the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.1° resolution global grid. It includes observations by polar orbiting (NOAA-18 & NOAAA-19/AVHRR, METOP-A/AVHRR, ENVISAT/AATSR, AQUA/AMSRE, TRMM/TMI) and geostationary (MSG/SEVIRI, GOES-11) satellites . The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases DOI (product) :https://doi.org/10.48670/moi-00164 https://doi.org/10.48670/moi-00164 328 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-climate-mediterranean-sea-level https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-climate-mediterranean-sea-level app-climate-mediterranean-sea-level Sea level rise is impacting the Mediterranean region’s cultural heritage. Many United Nations Educational, Scientific and Cultural Organisation (UNESCO) World Heritage Sites in the Mediterranean are situated along the coast and are therefore under increasing risk from coastal flooding due to sea level rise and extreme storm surge events. The application, developed in partnership with the Union for the Mediterranean, explores the impact of sea level rise on 50 UNESCO World Heritage Sites identified to be particularly vulnerable to coastal flooding. The application presents the 1 in 100-year extreme sea level event from ERA5 reanalysis plus projected sea level rise for 2050 under the RCP8.5 climate scenario to measure the impact of future coastal flooding on exposed world heritage sites. Future extreme sea level event projections are also presented using data from an ensemble of Coupled Model Intercomparison Project 6 (CMIP6) models. The risk of coastal flooding is quantified using a simple flood risk index that is easily visualised on an interactive map (please see documentation for details). The current sea level trend from satellite observations are also presented for each cultural heritage site's location. The application demonstrates how climate data can support the adaptation to climate change management of cultural heritage sites in the Mediterranean by providing an easy-to-use tool for heritage managers and policymakers to inform adaptation planning. The application is developed using robust high-resolution data and modelling approaches that covers the whole Mediterranean basin whilst facilitating adaptation planning at a local level. The application was developed by the Copernicus Climate Change Service in partnership with the Union for the Mediterranean. INPUT VARIABLES Name Units Description Source Mean sea level m The annual mean sea level rise relative to the 1986-2005 reference period. Contributions to sea level rise include thermal expansion of the ocean, changes in ocean circulation, ice sheet contributions, and glacio-isostatic adjustment (but not subsidence or tectonics). Sea level change time series Sea level anomaly m Sea surface height above mean sea surface computed with respect to a 20-year mean reference period (1993-2012). Sea level daily gridded data from satellite observations Total water level return period m Total water levels include (changes in) tidal levels, surge levels and interactions, but with sea level rise removed. Total water level and surge level simulations are forced by ERA5 reanalysis. The 100-year return period is selected. Sea level change indicators INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Mean sea level m The annual mean sea level rise relative to the 1986-2005 reference period. Contributions to sea level rise include thermal expansion of the ocean, changes in ocean circulation, ice sheet contributions, and glacio-isostatic adjustment (but not subsidence or tectonics). Sea level change time series Mean sea level m The annual mean sea level rise relative to the 1986-2005 reference period. Contributions to sea level rise include thermal expansion of the ocean, changes in ocean circulation, ice sheet contributions, and glacio-isostatic adjustment (but not subsidence or tectonics). Sea level change time series Sea level change time series Sea level anomaly m Sea surface height above mean sea surface computed with respect to a 20-year mean reference period (1993-2012). Sea level daily gridded data from satellite observations Sea level anomaly m Sea surface height above mean sea surface computed with respect to a 20-year mean reference period (1993-2012). Sea level daily gridded data from satellite observations Sea level daily gridded data from satellite observations Total water level return period m Total water levels include (changes in) tidal levels, surge levels and interactions, but with sea level rise removed. Total water level and surge level simulations are forced by ERA5 reanalysis. The 100-year return period is selected. Sea level change indicators Total water level return period m Total water levels include (changes in) tidal levels, surge levels and interactions, but with sea level rise removed. Total water level and surge level simulations are forced by ERA5 reanalysis. The 100-year return period is selected. Sea level change indicators Sea level change indicators OUTPUT VARIABLES Name Units Description Flood risk index Dimensionless The flood risk index caclulated using the sea level (1 in 100-year extreme event). This is based on a current 1 in 100 year extreme event combined with sea level projections for the year 2050. Observed sea level change from satellites cm Annual mean of sea surface height above the mean sea level relative to the reference period 1993-2012. Sea level (1 in 100-year extreme event) m Sea level height based on a current 1 in 100 year extreme event combined with sea level projections for the year 2050. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Flood risk index Dimensionless The flood risk index caclulated using the sea level (1 in 100-year extreme event). This is based on a current 1 in 100 year extreme event combined with sea level projections for the year 2050. Flood risk index Dimensionless The flood risk index caclulated using the sea level (1 in 100-year extreme event). This is based on a current 1 in 100 year extreme event combined with sea level projections for the year 2050. Observed sea level change from satellites cm Annual mean of sea surface height above the mean sea level relative to the reference period 1993-2012. Observed sea level change from satellites cm Annual mean of sea surface height above the mean sea level relative to the reference period 1993-2012. Sea level (1 in 100-year extreme event) m Sea level height based on a current 1 in 100 year extreme event combined with sea level projections for the year 2050. Sea level (1 in 100-year extreme event) m Sea level height based on a current 1 in 100 year extreme event combined with sea level projections for the year 2050. 329 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-earth-radiation-budget https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-earth-radiation-budget satellite-earth-radiation-budget The Earth’s Radiation Budget (ERB) represents the overall balance between incoming and outgoing radiant energy at the Earth’s Top-of-the-Atmosphere. This Essential Climate Variable (ECV) is the primary forcing of the climate system and is therefore a fundamental quantity to be monitored to understand the Earth’s climate and its variability. The ERB comprises the quantification of the incoming radiation from the Sun and the outgoing reflected shortwave and emitted longwave radiation. The ERB represents the balance between incoming, predominantly solar, radiation and outgoing radiation as either reflected solar radiation or thermal radiation emitted by the Earth system. The Earth is in a state of dynamic radiative balance, energy arriving from the Sun is balanced by outgoing radiation from the top-of-the-atmosphere. Changes in the Earth’s surface and atmosphere (including cloud amount and properties, and concentrations of greenhouse gases) will alter the radiative balance of the Earth system and change the balance and distribution of outgoing thermal radiation and reflected solar radiation. Aside from the elevation (height) of the sun above the horizon, the largest factor controlling the variability in the Earth radiation budget is cloud cover; the fraction of cloud cover, their height and opacity all impact on both the reflected solar and outgoing infrared radiation. Thus, knowledge of cloud properties, along with atmospheric composition and surface properties, allows the earth radiation budget to be computed. This catalogue entry comprises data from a number of sources which are summarised in the following sections. 330 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-carra-pressure-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-carra-pressure-levels reanalysis-carra-pressure-levels The C3S Arctic Regional Reanalysis (CARRA) dataset contains 3-hourly analyses and hourly short term forecasts of atmospheric and surface meteorological variables (temperature, humidity, wind, and other thermodynamic variables) at 2.5 km resolution. Additionally, forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC are available. The dataset includes two domains. The West domain covers Greenland, the Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, the Greenland Sea, and parts of Svalbard. The East domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia. The dataset has been produced with the use of the HARMONIE-AROME state-of-the-art non-hydrostatic regional numerical weather prediction model. High resolution reanalysis for the Arctic region is particularly important because the climate change is more pronounced in the Arctic region than elsewhere in the Earth. This fact calls for a better description of this region providing additional details with respect to the global reanalyses (ERA5 for instance). The additional information is provided by the higher horizontal resolution, more local observations (from the Nordic countries and Greenland), better description of surface characteristics (high resolution satellite and physiographic data), high resolution non-hydrostatic dynamics and improved physical parameterisation of clouds and precipitation in particular. The inputs to CARRA reanalysis are the observations, the ERA5 global reanalysis as lateral boundary conditions and the physiographic datasets describing the surface characteristics of the model. The observation values and information about their quality are used together to constrain the reanalysis where observations are available and provide information for the data assimilation system in areas in where less observations are available. More details about the reanalysis dataset and the extensive input data are given in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Horizontal coverage West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia Horizontal resolution 2.5km x 2.5km Vertical coverage From 1000 hPa to 10 hPa Vertical resolution 23 pressure levels: 1000, 950, 925, 900, 875, 850, 825, 800, 750, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20 and 10 hPa Temporal coverage From 1991 to present Temporal resolution 3-hourly analysis data. Hourly forecast data File format GRIB2 Update frequency Monthly. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Projection Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Lambert conformal conic grid with 1069 x 1269 grid points for the CARRA-West domain Lambert conformal conic grid with 789 x 989 grid points for the CARRA-East domain Horizontal coverage West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia Horizontal coverage West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia West domain: The domain covers Greenland, Labrador Sea, Davis Strait, Baffin Bay, Denmark Strait, Iceland, Jan Mayen, Greenland Sea, and parts of Svalbard East domain: This domain covers Svalbard, Franz Josef Land, Novaya Zemlya, Barents Sea, and the Northern parts of the Norwegian Sea and Scandinavia Horizontal resolution 2.5km x 2.5km Horizontal resolution 2.5km x 2.5km Vertical coverage From 1000 hPa to 10 hPa Vertical coverage From 1000 hPa to 10 hPa Vertical resolution 23 pressure levels: 1000, 950, 925, 900, 875, 850, 825, 800, 750, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20 and 10 hPa Vertical resolution 23 pressure levels: 1000, 950, 925, 900, 875, 850, 825, 800, 750, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20 and 10 hPa Temporal coverage From 1991 to present Temporal coverage From 1991 to present Temporal resolution 3-hourly analysis data. Hourly forecast data Temporal resolution 3-hourly analysis data. Hourly forecast data File format GRIB2 File format GRIB2 Update frequency Monthly. Update frequency Monthly. MAIN VARIABLES Name Units Description Cloud cover % Fraction of the grid box covered by clouds (liquid and ice) at the atmospheric pressure levels. Geometric vertical velocity m s-1 The vertical component of the wind at the atmospheric pressure levels converted into the height-based vertical coordinate. Upward wind speeds are positive and downward wind speeds are negative. Geopotential m2 s-2 The work required to lift an air parcel of unit mass from mean sea level to the given pressure level. Graupel kg kg-1 Mass fraction of graupel at the atmospheric pressure levels. Graupel is snow particles that have been melted and partially or completely refrozen. Hail is included in this precipitation type. Potential vorticity K m2 kg-1 s-1 Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere. Pseudo-adiabatic potential temperature K The temperature that an air parcel would have if it was first expanded through a pseudo-adiabatic process to 0 hPa pressure and thereafter compressed to a pressure of 1000 hPa through a dry-adiabatic process. Relative humidity % Relation between actual humidity and saturation humidity. Values are in the interval [0,100]. 0% means that the air at the pressure level is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. Specific cloud ice water content kg kg-1 Mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud liquid water content kg kg-1 Mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This variable provides the average value for a grid box. Specific cloud snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Temperature K Air temperature at the atmospheric pressure levels. U-component of wind m s-1 The zonal (u-component) wind components are defined in terms of the local grid orientation, which differs from geographic east-west direction.If the grid axes were aligned with geographic east directions, wind speeds towards east would be positive and wind speeds towards west would be negative. V-component of wind m s-1 The meridional (v-component) wind components are defined in terms of the local grid orientation, which differs from geographic north-south direction. If the grid axes were aligned with north directions, wind speeds towards north would be positive and wind speeds towards south would be negative. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Cloud cover % Fraction of the grid box covered by clouds (liquid and ice) at the atmospheric pressure levels. Cloud cover % Fraction of the grid box covered by clouds (liquid and ice) at the atmospheric pressure levels. Geometric vertical velocity m s-1 The vertical component of the wind at the atmospheric pressure levels converted into the height-based vertical coordinate. Upward wind speeds are positive and downward wind speeds are negative. Geometric vertical velocity m s-1 The vertical component of the wind at the atmospheric pressure levels converted into the height-based vertical coordinate. Upward wind speeds are positive and downward wind speeds are negative. Geopotential m2 s-2 The work required to lift an air parcel of unit mass from mean sea level to the given pressure level. Geopotential m2 s-2 The work required to lift an air parcel of unit mass from mean sea level to the given pressure level. Graupel kg kg-1 Mass fraction of graupel at the atmospheric pressure levels. Graupel is snow particles that have been melted and partially or completely refrozen. Hail is included in this precipitation type. Graupel kg kg-1 Mass fraction of graupel at the atmospheric pressure levels. Graupel is snow particles that have been melted and partially or completely refrozen. Hail is included in this precipitation type. Potential vorticity K m2 kg-1 s-1 Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere. Potential vorticity K m2 kg-1 s-1 Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere. Pseudo-adiabatic potential temperature K The temperature that an air parcel would have if it was first expanded through a pseudo-adiabatic process to 0 hPa pressure and thereafter compressed to a pressure of 1000 hPa through a dry-adiabatic process. Pseudo-adiabatic potential temperature K The temperature that an air parcel would have if it was first expanded through a pseudo-adiabatic process to 0 hPa pressure and thereafter compressed to a pressure of 1000 hPa through a dry-adiabatic process. Relative humidity % Relation between actual humidity and saturation humidity. Values are in the interval [0,100]. 0% means that the air at the pressure level is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. Relative humidity % Relation between actual humidity and saturation humidity. Values are in the interval [0,100]. 0% means that the air at the pressure level is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. Specific cloud ice water content kg kg-1 Mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud ice water content kg kg-1 Mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud liquid water content kg kg-1 Mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud liquid water content kg kg-1 Mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This variable provides the average value for a grid box. Specific cloud rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This variable provides the average value for a grid box. Specific cloud snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific cloud snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Temperature K Air temperature at the atmospheric pressure levels. Temperature K Air temperature at the atmospheric pressure levels. U-component of wind m s-1 The zonal (u-component) wind components are defined in terms of the local grid orientation, which differs from geographic east-west direction.If the grid axes were aligned with geographic east directions, wind speeds towards east would be positive and wind speeds towards west would be negative. U-component of wind m s-1 The zonal (u-component) wind components are defined in terms of the local grid orientation, which differs from geographic east-west direction.If the grid axes were aligned with geographic east directions, wind speeds towards east would be positive and wind speeds towards west would be negative. V-component of wind m s-1 The meridional (v-component) wind components are defined in terms of the local grid orientation, which differs from geographic north-south direction. If the grid axes were aligned with north directions, wind speeds towards north would be positive and wind speeds towards south would be negative. V-component of wind m s-1 The meridional (v-component) wind components are defined in terms of the local grid orientation, which differs from geographic north-south direction. If the grid axes were aligned with north directions, wind speeds towards north would be positive and wind speeds towards south would be negative. 331 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-tidal-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=ARCTIC_ANALYSISFORECAST_PHY_TIDE_002_015 Arctic Ocean Tidal Analysis and Forecast Short description: The Arctic Ocean Surface Currents Analysis and Forecast system uses the HYCOM model at 3 km resolution forced with tides at its lateral boundaries, surface winds sea level pressure from the ECMWF (European Centre for Medium-Range Weather Forecasts). HYCOM runs daily providing 10 days forecast. The output variables are the surface currents and sea surface heights, provided at 15 minutes frequency, which therefore include mesoscale signals (though without data assimilation so far), tides and storm surge signals. DOI (product) :https://doi.org/10.48670/moi-00005 https://doi.org/10.48670/moi-00005 332 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/seasonal-postprocessed-pressure-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal-postprocessed-pressure-levels seasonal-postprocessed-pressure-levels This entry covers pressure-level data post-processed for bias adjustment on a monthly time resolution. pressure-level data monthly time resolution Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Seasonal forecasts Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). multi-system seasonal forecast service The variables available in this data set are listed in the table below. The data includes forecasts created in real-time since 2017. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 1° x 1° Vertical coverage From 1000 hPa to 10 hPa Temporal coverage 2017 to present Temporal resolution Monthly File format GRIB Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 1° x 1° Horizontal resolution 1° x 1° Vertical coverage From 1000 hPa to 10 hPa Vertical coverage From 1000 hPa to 10 hPa Temporal coverage 2017 to present Temporal coverage 2017 to present Temporal resolution Monthly Temporal resolution Monthly File format GRIB File format GRIB Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. MAIN VARIABLES Name Units Geopotential anomaly m2 s-2 Specific humidity anomaly kg kg-1 Temperature anomaly K U-component of wind anomaly m s-1 V-component of wind anomaly m s-1 MAIN VARIABLES MAIN VARIABLES Name Units Name Units Geopotential anomaly m2 s-2 Geopotential anomaly m2 s-2 Specific humidity anomaly kg kg-1 Specific humidity anomaly kg kg-1 Temperature anomaly K Temperature anomaly K U-component of wind anomaly m s-1 U-component of wind anomaly m s-1 V-component of wind anomaly m s-1 V-component of wind anomaly m s-1 333 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/esa-sst-cci-and-c3s-reprocessed-sea-surface-temperature http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_024 ESA SST CCI and C3S reprocessed sea surface temperature analyses Short description: The ESA SST CCI and C3S global Sea Surface Temperature Reprocessed product provides gap-free maps of daily average SST at 20 cm depth at 0.05deg. x 0.05deg. horizontal grid resolution, using satellite data from the (A)ATSRs, SLSTR and the AVHRR series of sensors (Merchant et al., 2019). The ESA SST CCI and C3S level 4 analyses were produced by running the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system (Good et al., 2020) to provide a high resolution (1/20deg. - approx. 5km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth for the global ocean. Only (A)ATSR, SLSTR and AVHRR satellite data processed by the ESA SST CCI and C3S projects were used, giving a stable product. It also uses reprocessed sea-ice concentration data from the EUMETSAT OSI-SAF (OSI-450 and OSI-430; Lavergne et al., 2019). DOI (product) :https://doi.org/10.48670/moi-00169 https://doi.org/10.48670/moi-00169 334 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-iberian-biscay-mean-sea-level-time-series-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=IBI_OMI_SL_area_averaged_anomalies Atlantic Iberian Biscay Mean Sea Level time series and trend from Observations Reprocessing DEFINITION The ocean monitoring indicator on regional mean sea level is derived from the DUACS delayed-time (DT-2021 version) altimeter gridded maps of sea level anomalies based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The mean sea level evolution estimated in the Irish-Biscay-Iberian (IBI) region is derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed (least square fit of sinusoidal function) and the time series is low-pass filtered (175 days cut-off). The curve is corrected for the regional mean effect of the Glacial Isostatic Adjustment (GIA) using the ICE5G-VM2 GIA model (Peltier, 2004). During 1993-1998, the Global men sea level (hereafter GMSL) has been known to be affected by a TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018; Legeais et al., 2020). This drift led to overestimate the trend of the GMSL during the first 6 years of the altimetry record (about 0.04 mm/y at global scale over the whole altimeter period). A correction of the drift is proposed for the Global mean sea level (Legeais et al., 2020). Whereas this TOPEX-A instrumental drift should also affect the regional mean sea level (hereafter RMSL) trend estimation, currently this empirical correction is currently not applied to the altimeter sea level dataset and resulting estimated for RMSL. Indeed, the pertinence of the global correction applied at regional scale has not been demonstrated yet and there is no clear consensus achieved on the way to proceed at regional scale. Additionally, the estimation of such a correction at regional scale is not obvious, especially in areas where few accurate independent measurements (e.g. in situ)- necessary for this estimation - are available. The trend uncertainty is provided in a 90% confidence interval (Prandi et al., 2021). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not taken into account. CONTEXT The indicator on area averaged sea level is a crucial index of climate change, and individual components contribute to sea level rise, including expansion due to ocean warming and melting of glaciers and ice sheets (WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report, global mean sea level (GMSL) increased by 0.20 (0.15 to 0.25) m over the period 1901 to 2018 with a rate 25 of rise that has accelerated since the 1960s to 3.7 (3.2 to 4.2) mm yr-1 for the period 2006–2018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). At regional scale, sea level does not change homogenously, and RMSL rise can also be influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). Rising sea level can strongly affect population and infrastructures in coastal areas, increase their vulnerability and risks for food security, particularly in low lying areas and island states. Adverse impacts from floods, storms and tropical cyclones with related losses and damages have increased due to sea level rise, and increase their vulnerability and increase risks for food security, particularly in low lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). In IBI region, the RMSL trend is modulated by decadal variations. As observed over the global ocean, the main actors of the long-term RMSL trend are associated with anthropogenic global/regional warming. Decadal variability is mainly linked to the strengthening or weakening of the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). The latest is driven by the North Atlantic Oscillation (NAO) (e.g. Delworth and Zeng, 2016). Along the European coast, the NAO also influences the along-slope winds dynamic which in return significantly contributes to the local sea level variability observed (Chafik et al., 2019). CMEMS KEY FINDINGS Over the [1993/01/01, 2021/08/02] period, the basin-wide RMSL in the IBI area rises at a rate of 3.8  0.82 mm/year. DOI (product):https://doi.org/10.48670/moi-00252 https://doi.org/10.48670/moi-00252 335 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-chlorophyll-trend-map-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_trend Baltic Sea Chlorophyll-a trend map from Observations Reprocessing DEFINITION This product includes the Baltic Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC which, in turn, results from the application of the regional chlorophyll algorithms over remote sensing reflectances (Rrs) provided by the Plymouth Marine Laboratory (PML) using an ad-hoc configuration for CMEMS of the ESA OC-CCI processor version 6 (OC-CCIv6) to merge at 1km resolution (rather than at 4km as for OC-CCI) MERIS, MODIS-AQUA, SeaWiFS, NPP-VIIRS and OLCI-A data. The chlorophyll product is derived from a Multi Layer Perceptron neural-net (MLP) developed on field measurements collected within the BiOMaP program of JRC/EC (Zibordi et al., 2011). The algorithm is an ensemble of different MLPs that use Rrs at different wavelengths as input. The processing chain and the techniques used to develop the algorithm are detailed in Brando et al. (2021a; 2021b). The trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens’s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included. CONTEXT Phytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration - as a proxy for phytoplankton - respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response in the Baltic Sea depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Kahru and Elmgren 2014) and anthropogenic climate change. Eutrophication is one of the most important issues for the Baltic Sea (HELCOM, 2018), therefore the use of long-term time series of consistent, well-calibrated, climate-quality data record is crucial for detecting eutrophication. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series. CMEMS KEY FINDINGS The average Baltic Sea trend for the 1997-2021 period is 0.5% per year. The basin shows a general positive chlorophyll trend. This result is in accordance with those of Sathyendranath et al. (2018), that reveal an increasing trend in chlorophyll concentration in most of the European Seas. Weak negative trend is observable in the northern sector of the Bothnian Bay and partially in the Gulf of Finland. DOI (product):https://doi.org/10.48670/moi-00198 https://doi.org/10.48670/moi-00198 336 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/high-resolution-snow-and-ice-monitoring-persistent-snow-0 https://cryo.land.copernicus.eu/finder/ High Resolution Snow and Ice Monitoring: Persistent Snow Area (raster 20m) The Copernicus Persistent Snow Area (PSA) product is generated annually for the entire EEA38 and the United Kingdom, based on the Fractional Snow Cover (FSC) information. It provides the extent of the persistent snow cover, i.e. the area where snow is present throughout the hydrological year with a spatial resolution of 20 m x 20 m. This metadata refers to the PSA product distributed in raster files as tiles aligned with Sentinel-2 (UTM/WGS84) at 20 m x 20 m GSD. It is also available in another projection as tiles aligned with the Pan-European High-Resolution Layers in the European 20 m x 20 m grid (ETRS89 LAEA - EPSG: 3035). It is typically released on an annual basis with a release date in October, after the end of the hydrological year. Each product is composed of two separate files corresponding to the different layers of the product, and another metadata file. PSA is one of the products of the pan-European High-Resolution Snow & Ice service (HR-S&I), which are provided at high spatial resolution (20 m x 20 m and 60 m x 60 m), from the Sentinel-2 and Sentinel-1 constellations data from September 1, 2016 onwards. Visit https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… to get more information on the different HR-S&I products (Snow products: FSC, WDS, SWS, GFSC, and PSA. Ice products: RLIE and ARLIE) https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… 337 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-sea-ice-extent-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=ARCTIC_OMI_SI_extent Arctic Sea Ice Extent from Reanalysis DEFINITION Estimates of Arctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Northern Hemisphere (excluding ice lakes) and from 1993 up to the year 2019 aiming to: i) obtain the Arctic sea ice extent as expressed in millions of km square (106 km2) to monitor both the large-scale variability and mean state and change. ii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013). These trends are calculated in three ways, i.e. (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss). The Arctic sea ice extent used here is based on the “multi-product” approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread. CONTEXT Sea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification, in global and regional sea level rates and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019). The sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018). CMEMS KEY FINDINGS Since the year 1993 the Arctic sea ice extent has decreased significantly at an annual rate of -0.75*106 km2 per decade. This represents an amount of –5.8 % per decade of Arctic sea ice extent loss over the period 1993 to 2018. Summer (September) sea ice extent loss amounts to -1.18*106 km2/decade (September values), which corresponds to -14.85% per decade. Winter (March) sea ice extent loss amounts to -0.57*106 km2/decade, which corresponds to -3.42% per decade. These values slightly exceed the estimates given in the AR5 IPCC assessment report (estimate up to the year 2012) as a consequence of continuing Northern Hemisphere sea ice extent loss. Main change in the mean seasonal cycle is characterized by less and less presence of sea ice during summertime with time. The last twelve years have the twelve lowest summer minimums ever measured since 1993, the summer 2012 still being the lowest minimum. 2019 follows the recent trend of the 2010's with a summer and winter well below the 1990-2000's average. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00190 https://doi.org/10.48670/moi-00190 338 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-physics-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_ANALYSISFORECAST_PHY_001_024 Global Ocean Physics Analysis and Forecast Short description The Operational Mercator global ocean analysis and forecast system at 1/12 degree is providing 10 days of 3D global ocean forecasts updated daily. The time series is aggregated in time in order to reach a two full year’s time series sliding window. This product includes daily and monthly mean files of temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom over the global ocean. It also includes hourly mean surface fields for sea level height, temperature and currents. The global ocean output files are displayed with a 1/12 degree horizontal resolution with regular longitude/latitude equirectangular projection. 50 vertical levels are ranging from 0 to 5500 meters. This product also delivers a special dataset for surface current which also includes wave and tidal drift called SMOC (Surface merged Ocean Current). DOI (product) :https://doi.org/10.48670/moi-00016 https://doi.org/10.48670/moi-00016 339 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-total-column-water-vapour-ocean https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-total-column-water-vapour-ocean satellite-total-column-water-vapour-ocean Water vapour is has been recognised as an Essential Climate Variable as it provides the basis for all cloud formation and physics and furthermore influences the Earth's heat budget due to its high absorbance of long and short-wave radiation. Total column water vapour (TCWV) is a measure of the integrated water vapour content of the atmosphere. This catalogue entry provides the TCWV data product usually called Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS). The HOAPS product uses a 1D-Var retrieval concept for atmospheric water vapour content from the measurements of the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) sensors in the microwave spectra over ice-free ocean surfaces. The dataset provides a continuous data record composed of a Thematic Climate Data Record (TCDR) from 1988 to 2014 and of an Interim Climate Data Record (ICDR) from 2015 onward. It was produced following the World Meteorological Organisation's Observing Systems Capability Analysis and Review Tool (OSCAR) requirements for TCWV. The SSM/I and SSMIS are well-established instruments for TCWV retrieval over ice-free open ocean surfaces and provide a long time series (1987 to present) and global coverage at a reasonable resolution. The TCDR HOAPS dataset is produced on behalf of EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) and the ICDR is produced within the Copernicus Climate Change Serice (C3S) project. Both products are based on the HOAPS 4.0 algorithm at DWD (Deutscher Wetterdienst). A related dataset providing TCWV data over both land and ocean, but only at monthly time resolution, computed using a different set of sensors and algorithms is also available (the link to that dataset is under the Related data section). DATA DESCRIPTION Data type Gridded Horizontal coverage Global ice-free ocean Horizontal resolution 0.5° x 0.5°. A number of gaps are to be expected. Vertical coverage Total atmospheric column Vertical resolution Single level Temporal coverage TCDR: From 1988 to 2014 ICDR: From 2015 to 2020 Temporal resolution Monthly means stored in yearly files 6-hourly stored in monthly files File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Versions HOAPS algorithm version 4.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global ice-free ocean Horizontal coverage Global ice-free ocean Horizontal resolution 0.5° x 0.5°. A number of gaps are to be expected. Horizontal resolution 0.5° x 0.5°. A number of gaps are to be expected. Vertical coverage Total atmospheric column Vertical coverage Total atmospheric column Vertical resolution Single level Vertical resolution Single level Temporal coverage TCDR: From 1988 to 2014 ICDR: From 2015 to 2020 Temporal coverage TCDR: From 1988 to 2014 ICDR: From 2015 to 2020 TCDR: From 1988 to 2014 ICDR: From 2015 to 2020 Temporal resolution Monthly means stored in yearly files 6-hourly stored in monthly files Temporal resolution Monthly means stored in yearly files 6-hourly stored in monthly files Monthly means stored in yearly files 6-hourly stored in monthly files File format NetCDF4 File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Versions HOAPS algorithm version 4.0 Versions HOAPS algorithm version 4.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Total column water vapour kg m-2 Total Column Water Vapour (also called integrated Water Vapour (IWV) or Precipitable Water Vapour (PWV)) is the integrated mass of gaseous water in the total column of the atmosphere over an area of 1 m². MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Total column water vapour kg m-2 Total Column Water Vapour (also called integrated Water Vapour (IWV) or Precipitable Water Vapour (PWV)) is the integrated mass of gaseous water in the total column of the atmosphere over an area of 1 m². Total column water vapour kg m-2 Total Column Water Vapour (also called integrated Water Vapour (IWV) or Precipitable Water Vapour (PWV)) is the integrated mass of gaseous water in the total column of the atmosphere over an area of 1 m². RELATED VARIABLES The files contain a number of auxiliary variables describing the uncertainty and statistical distribution of the main variables. For more information on the contents of the downloaded files, please refer to the documentation. RELATED VARIABLES RELATED VARIABLES The files contain a number of auxiliary variables describing the uncertainty and statistical distribution of the main variables. For more information on the contents of the downloaded files, please refer to the documentation. The files contain a number of auxiliary variables describing the uncertainty and statistical distribution of the main variables. For more information on the contents of the downloaded files, please refer to the documentation. 340 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/high-resolution-snow-and-ice-monitoring-aggregated-river https://land.copernicus.eu/user-corner/technical-library/hrsi-ice-pum High Resolution Snow and Ice Monitoring: Aggregated River and Lake Ice Extent The Copernicus Aggregated River and Lake Ice Extent (ARLIE) product is a spatially aggregated information on surface water conditions of rivers and lakes. ARLIE information is stored in a geodatabase, enriched every day from the River and Lake Ice extent products (RLIE S1, RLIE S2 and RLIE S1+S2) for the entire EEA38 and the United Kingdom. It provides percent coverage of snow-covered or snow-free ice on lakes and on 10 km river sections described by the EU-HYDRO river and lake network database. The ARLIE products are stored in a PostGIS persistent geodatabase. They can be retrieved by using a specific REST API. Users can query ice sheet summary information of river segments and lakes (ARLIE statistics) together with the geometry and caracteristics of the features on which these statistics were estimated. All geometry features are delivered in the ETRS89 LAEA (EPSG:3035) coordinate reference system. ARLIE is one of the products of the pan-European High-Resolution Snow & Ice service (HR-S&I), which are provided at high spatial resolution (20 m x 20 m and 60 m x 60 m) from the Sentinel-2 and Sentinel-1 constellations data from September 1, 2016 onwards. Visit https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… to get more information on the different HR-S&I products (Snow products : FSC, WDS, SWS, GFSC, and PSA. Ice products : RLIE and ARLIE) https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… 341 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-agroproductivity-indicators https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agroproductivity-indicators sis-agroproductivity-indicators This dataset provides crop productivity indicators up to near real-time and evapotranspiration indicators ending in 2018 with high-resolution coverage of agricultural regions with dominant cropping patterns from around the globe. The crop productivity indicators provide insight into the phenological development (periodic events in biological life cycles), total biomass and yield (crop weight per unit area of land) of four major crops (wheat, maize, soybean, rice) at high grid resolution (0.1° x 0.1°) and with global coverage. The high resolution and global coverage allow these indicators to be used to analyse the effect of climate variability on crop yields at regional scales. The crop productivity indicators are not suitable for field-scale analysis. The evapotranspiration indicators provide the actual and potential vegetation and soil evapotranspiration. This is the sum of water vapor fluxes from soil evaporation, wet canopy evaporation and plant transpiration at the dry canopy surface. The product provides insight into regional and interannual variability in vegetation water use (actual and potential) and water stress. It can be used as an indicator to determine the impact of water stress on crops and vegetation in general. The evapotranspiration indicators are not suitable for field-scale analysis. This dataset was produced on behalf of the Copernicus Climate Change Service. 342 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/provider-cems-without https://cds.climate.copernicus.eu/cdsapp#!/dataset/provider-cems-without provider-cems-without This page provides a brief overview of the Copernicus Emergency Management Service (CEMS) datasets made available to users through the Climate Data Store (CDS). CEMS is one of the six services provided by Copernicus, the European Union's Earth Observation Programme. Created using satellite, in-situ, and model data, CEMS products support all actors involved in the management of natural and man-made disasters throughout the disaster lifecycle. This includes prevention, preparedness, response and recovery activities. Copernicus Emergency Management Service (CEMS) Copernicus CEMS has been operational since 2012 and the European Commission's Joint Research Centre is responsible for the management, technical implementation and evolution of the service. Joint Research Centre CEMS consists of three components: On-demand mapping that provides satellite-based maps on the extent and impact of disasters for specific worldwide events upon request of eligible users; Early Warning and Monitoring that provides overviews of near real-time and early warning information for ongoing and forecasted events in relation to floods, droughts and forest fires, with European and global coverage; Exposure mapping, providing highly accurate information on the presence of settlements, their related infrastructure and population. On-demand mapping that provides satellite-based maps on the extent and impact of disasters for specific worldwide events upon request of eligible users; Early Warning and Monitoring that provides overviews of near real-time and early warning information for ongoing and forecasted events in relation to floods, droughts and forest fires, with European and global coverage; Exposure mapping, providing highly accurate information on the presence of settlements, their related infrastructure and population. CEMS-Floods CEMS-Floods CEMS flood early warning and monitoring component comprises the Global and European Flood Awareness Systems (GloFAS and EFAS) as well as the Global Flood Monitoring product. GloFAS EFAS Global Flood Monitoring There are three types of hydrological datasets at the native EFAS and GloFAS temporal and spatial resolutions: Historical (or reanalysis) multi-decadal simulations, that aim to reproduce observations and serve as a reference dataset. They are used for initial conditions, to define climatology and thresholds for forecast evaluation. Real-time forecasts, that aim to represent possible scenarios of the future. Both seasonal and shorter range forecasts are produced. Reforecasts, that are a set of forecasts for the past produced with a model configuration that is as close as possible to the operational system. They are used to define thresholds and for forecast evaluation. Both seasonal and shorter range reforecasts are produced. Historical (or reanalysis) multi-decadal simulations, that aim to reproduce observations and serve as a reference dataset. They are used for initial conditions, to define climatology and thresholds for forecast evaluation. Historical (or reanalysis) multi-decadal simulations, that aim to reproduce observations and serve as a reference dataset. They are used for initial conditions, to define climatology and thresholds for forecast evaluation. Real-time forecasts, that aim to represent possible scenarios of the future. Both seasonal and shorter range forecasts are produced. Real-time forecasts, that aim to represent possible scenarios of the future. Both seasonal and shorter range forecasts are produced. Reforecasts, that are a set of forecasts for the past produced with a model configuration that is as close as possible to the operational system. They are used to define thresholds and for forecast evaluation. Both seasonal and shorter range reforecasts are produced. Reforecasts, that are a set of forecasts for the past produced with a model configuration that is as close as possible to the operational system. They are used to define thresholds and for forecast evaluation. Both seasonal and shorter range reforecasts are produced. All datasets are updated after a major operational system upgrade and are given a new version number. CEMS-Fire CEMS-Fire CEMS fire early warning and monitoring component is composed of the European Forest Fire Information Service (EFFIS) and the Global Wildfire Information Service (GWIS - under development). EFFIS GWIS There are two types of fire danger datasets that for ease of use are provided at the same spatial resolution of the leading datasets (e.g. ERA5, System-5): Historical (or reanalysis) multi-decadal simulations, that aim to reproduce observations and serve as a reference dataset. They are used for initial conditions, to define climatology and thresholds for forecast evaluation. Reforecasts, that are a set of forecasts for the past produced with a model configuration that is as close as possible to the operational system. They are used to define thresholds and for forecast evaluation. Both seasonal and shorter-range reforecasts are produced. Historical (or reanalysis) multi-decadal simulations, that aim to reproduce observations and serve as a reference dataset. They are used for initial conditions, to define climatology and thresholds for forecast evaluation. Historical (or reanalysis) multi-decadal simulations, that aim to reproduce observations and serve as a reference dataset. They are used for initial conditions, to define climatology and thresholds for forecast evaluation. Reforecasts, that are a set of forecasts for the past produced with a model configuration that is as close as possible to the operational system. They are used to define thresholds and for forecast evaluation. Both seasonal and shorter-range reforecasts are produced. Reforecasts, that are a set of forecasts for the past produced with a model configuration that is as close as possible to the operational system. They are used to define thresholds and for forecast evaluation. Both seasonal and shorter-range reforecasts are produced. All datasets are updated after a major operational system upgrade and are given a new version number. More information about CEMS Floods and CEMS Fires Early Warning Systems (EWS) data and services can be found on the dedicated wiki pages. dedicated wiki pages 343 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecv-climate-change https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecv-for-climate-change ecv-for-climate-change The Essential Climate Variables for assessment of climate variability from 1979 to present dataset contains a selection of climatologies, monthly anomalies and monthly mean fields of Essential Climate Variables (ECVs) suitable for monitoring and assessment of climate variability and change. Selection criteria are based on accuracy and temporal consistency on monthly to decadal time scales. The ECV data products in this set have been estimated from climate reanalyses ERA-Interim and ERA5, and, depending on the source, may have been adjusted to account for biases and other known deficiencies. Data sources and adjustment methods used are described in the Product User Guide, as are various particulars such as the baseline periods used to calculate monthly climatologies and the corresponding anomalies. Essential Climate Variables for assessment of climate variability from 1979 to present The statistics provided are monthly average fields, climatologies and anomalies, as well as 12-month running mean anomalies. Climatologies and anomalies are calculated with respect to two reference periods: 1981-2010 (ERA5 and ERA-Int) and 1991-2020 (ERA5 only). The C3S monthly climate bulletin (https://climate.copernicus.eu/climate-bulletins) provides an assessment of the monthly state of the climate with an emphasis on the European geographical domain. This data record is used as the basis for these monthly bulletins. https://climate.copernicus.eu/climate-bulletins DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 0.25° x 0.25° Temporal coverage 1979 to present. Temporal resolution Monthly File format GRIB Update frequency Monthly DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.25° x 0.25° Horizontal resolution 0.25° x 0.25° Temporal coverage 1979 to present. Temporal coverage 1979 to present. Temporal resolution Monthly Temporal resolution Monthly File format GRIB File format GRIB Update frequency Monthly Update frequency Monthly MAIN VARIABLES Name Units Description 0-7cm volumetric soil moisture m3 m-3 This variable is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Precipitation m day-1 This variable is the accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. Monthly mean precipitation data is in units of “m”, effectively accumulated over a day, thereby giving “m day-1”. For consistency with the ECMWF parameter database, the units in the GRIB files are “m”. Sea-ice cover Dimensionless Sea ice is frozen sea water which floats on the surface of the ocean. Sea ice does not include ice which forms on land such as glaciers, icebergs and ice-sheets. It also excludes ice shelves which are anchored on land, but protrude out over the surface of the ocean. This variable is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which is defined as ocean according to the land sea mask at the resolution being used. Sea-ice cover can also be known as sea-ice fraction or sea-ice concentration. Surface air relative humidity % The ratio of the partial pressure of water vapour to the equilibrium vapour pressure of water at the same temperature near the surface. Surface air temperature K This variable is the temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 0-7cm volumetric soil moisture m3 m-3 This variable is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. 0-7cm volumetric soil moisture m3 m-3 This variable is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Precipitation m day-1 This variable is the accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. Monthly mean precipitation data is in units of “m”, effectively accumulated over a day, thereby giving “m day-1”. For consistency with the ECMWF parameter database, the units in the GRIB files are “m”. Precipitation m day-1 This variable is the accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. Monthly mean precipitation data is in units of “m”, effectively accumulated over a day, thereby giving “m day-1”. For consistency with the ECMWF parameter database, the units in the GRIB files are “m”. Sea-ice cover Dimensionless Sea ice is frozen sea water which floats on the surface of the ocean. Sea ice does not include ice which forms on land such as glaciers, icebergs and ice-sheets. It also excludes ice shelves which are anchored on land, but protrude out over the surface of the ocean. This variable is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which is defined as ocean according to the land sea mask at the resolution being used. Sea-ice cover can also be known as sea-ice fraction or sea-ice concentration. Sea-ice cover Dimensionless Sea ice is frozen sea water which floats on the surface of the ocean. Sea ice does not include ice which forms on land such as glaciers, icebergs and ice-sheets. It also excludes ice shelves which are anchored on land, but protrude out over the surface of the ocean. This variable is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which is defined as ocean according to the land sea mask at the resolution being used. Sea-ice cover can also be known as sea-ice fraction or sea-ice concentration. Surface air relative humidity % The ratio of the partial pressure of water vapour to the equilibrium vapour pressure of water at the same temperature near the surface. Surface air relative humidity % The ratio of the partial pressure of water vapour to the equilibrium vapour pressure of water at the same temperature near the surface. Surface air temperature K This variable is the temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Surface air temperature K This variable is the temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. 344 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-diurnal-subskin-sea-surface-temperature http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_BAL_PHY_SUBSKIN_L4_NRT_010_034 Baltic Sea - Diurnal Subskin Sea Surface Temperature Analysis Short description: For the Baltic Sea - the DMI Sea Surface Temperature Diurnal Subskin L4 aims at providing hourly analysis of the diurnal subskin signal at 0.02deg. x 0.02deg. horizontal resolution, using the BAL L4 NRT product as foundation temperature and satellite data from infra-red radiometers. Uses SST satellite products from the sensors: Metop B AVHRR, Sentinel-3 A/B SLSTR, VIIRS SUOMI NPP & NOAA20 DOI (product) :https://doi.org/10.48670/moi-00309 https://doi.org/10.48670/moi-00309 345 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/high-resolution-vegetation-phenology-and-productivity https://www.wekeo.eu/data?view=viewer&t=1566840390697&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjNCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1JTkRJQ0VTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVklfUFBJXzEwTSIsInpJbmRleCI6MTEwLCJpc0hpZGRlbiI6dHJ1ZX0seyJpZCI6ImM1IiwibGF5ZXJJZCI6IkVPOkhSVlBQOkRBVDpWRUdFVEFUSU9OLUlORElDRVMvX19ERUZBVUxUX18vQ0xNU19IUlZQUF9WSV9RRkxBRzJfMTBNIiwiekluZGV4IjoxMjB9XQ%3D%3D High Resolution Vegetation Phenology and Productivity: Vegetation Indices Quality Flag (raster 10m) - version 1 revision 1, Sep. 2021 This metadata refers to the Quality Flag (QFLAG2) dataset, one of the near real-time (NRT) Vegetation Index products of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP), component of the Copernicus Land Monitoring Service (CLMS). The Quality Flag (QFLAG2) is a quality indicator that assists users with the screening of clouds, shadows from clouds and topography, other dark areas, snow and water surfaces in their analysis of the four related Vegetation Indices datasets: the Plant Phenology Index (PPI), the Normalized Difference Vegetation Index (NDVI), the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The QFLAG2 dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for the period from October 2016 until today, with daily updates. 346 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-l3-significant-wave-height-reprocessed http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=WAVE_GLO_PHY_SWH_L3_MY_014_005 GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS Short description: Multi-Year mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle…). All the missions are homogenized with respect to a reference mission and in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise. This product is based on the ESA Sea State Climate Change Initiative data Level 3 product (version 2) and is formatted by the WAVE-TAC to be homogeneous with the CMEMS Level 3 Near-real-time product. It is based on the reprocessing of GDR data from the following altimeter missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa and Jason-3. CFOSAT Multi-Year dataset is based on the reprocessing of CFOSAT Level-2P products (CNES/CLS), inter-calibrated on Jason-3 reference mission issued from the CCI Sea State dataset. One file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH) and the unfiltered SWH (VAVH_UNFILTERED). DOI (product) :https://doi.org/10.48670/moi-00176 https://doi.org/10.48670/moi-00176 347 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/antarctic-ocean-sea-ice-drift-reprocessed http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_ANT_PHY_L3_MY_011_018 Antarctic Ocean Sea Ice Drift REPROCESSED Short description: Antarctic sea ice displacement during winter from medium resolution sensors since 2002 DOI (product) :https://doi.org/10.48670/moi-00120 https://doi.org/10.48670/moi-00120 348 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/seasonal-original-pressure-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal-original-pressure-levels seasonal-original-pressure-levels This entry covers pressure-level data at the original time resolution (once every 12 hours). pressure-level data original time resolution Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Seasonal forecasts Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). multi-system seasonal forecast service The variables available in this data set are listed in the table below. The data includes forecasts created in real-time (since 2017) and retrospective forecasts (hindcasts) initialised at equivalent intervals during the period 1993-2016. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 1° x 1° Vertical coverage From 1000 hPa to 10 hPa Temporal coverage 1993 to 2016 (hindcasts); 2017 to present (forecasts) Temporal resolution 12-hourly File format GRIB Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 1° x 1° Horizontal resolution 1° x 1° Vertical coverage From 1000 hPa to 10 hPa Vertical coverage From 1000 hPa to 10 hPa Temporal coverage 1993 to 2016 (hindcasts); 2017 to present (forecasts) Temporal coverage 1993 to 2016 (hindcasts); 2017 to present (forecasts) Temporal resolution 12-hourly Temporal resolution 12-hourly File format GRIB File format GRIB Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. MAIN VARIABLES Name Units Geopotential m2 s-2 Specific humidity kg kg-1 Temperature K U-component of wind m s-1 V-component of wind m s-1 MAIN VARIABLES MAIN VARIABLES Name Units Name Units Geopotential m2 s-2 Geopotential m2 s-2 Specific humidity kg kg-1 Specific humidity kg kg-1 Temperature K Temperature K U-component of wind m s-1 U-component of wind m s-1 V-component of wind m s-1 V-component of wind m s-1 349 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-physics-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_MULTIYEAR_PHY_001_030 Global Ocean Physics Reanalysis Short description: The GLORYS12V1 product is the CMEMS global ocean eddy-resolving (1/12° horizontal resolution, 50 vertical levels) reanalysis covering the altimetry (1993 onward). It is based largely on the current real-time global forecasting CMEMS system. The model component is the NEMO platform driven at surface by ECMWF ERA-Interim then ERA5 reanalyses for recent years. Observations are assimilated by means of a reduced-order Kalman filter. Along track altimeter data (Sea Level Anomaly), Satellite Sea Surface Temperature, Sea Ice Concentration and In situ Temperature and Salinity vertical Profiles are jointly assimilated. Moreover, a 3D-VAR scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity. This product includes daily and monthly mean files for temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom. The global ocean output files are displayed on a standard regular grid at 1/12° (approximatively 8 km) and on 50 standard levels. DOI (product) :https://doi.org/10.48670/moi-00021 https://doi.org/10.48670/moi-00021 350 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-agrometeorological-indicators https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators sis-agrometeorological-indicators This dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. Acquisition and pre-processing of the original ERA5 data is a complex and specialized job. By providing the AgERA5 dataset, users are freed from this work and can directly start with meaningful input for their analyses and modelling. To this end, the variables provided in this dataset match the input needs of most agriculture and agro-ecological models. Data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model. The data was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 0.1° x 0.1° Vertical coverage Variables are provided on a single level which may differ among variables Temporal coverage From 1979 to present Temporal resolution Daily File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Versions 1.0, 1.1 Update frequency Monthly DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.1° x 0.1° Horizontal resolution 0.1° x 0.1° Vertical coverage Variables are provided on a single level which may differ among variables Vertical coverage Variables are provided on a single level which may differ among variables Temporal coverage From 1979 to present Temporal coverage From 1979 to present Temporal resolution Daily Temporal resolution Daily File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Versions 1.0, 1.1 Versions 1.0, 1.1 Update frequency Monthly Update frequency Monthly MAIN VARIABLES Name Units Description 10m wind speed m s-1 Mean wind speed at a height of 10 metres above the surface over the period 00h-24h local time. 2m dewpoint temperature K Mean dewpoint temperature at a height of 2 metres above the surface over the period 00h-24h local time. The dew point is the temperature to which air must be cooled to become saturated with water vapor. In combination with the air temperature it is used to assess relative humidity. 2m relative humidity % Relative humidity at 06h, 09h, 12h. 15h, 18h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. 2m temperature K Air temperature at a height of 2 metres above the surface. Cloud cover Dimensionless The number of hours with clouds over the period 00h-24h local time divided by 24 hours. Liquid precipitation duration fraction Dimensionless The number of hours with precipitation over the period 00h-24h local time divided by 24 hours and per unit of area. Liquid precipitation is equivalent to the height of the layer of water that would have formed from precipitation had the water not penetrated the soil, run off, or evaporated. Precipitation flux mm day-1 Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per day. Snow thickness cm Mean snow depth over the period 00h-24h local time measured as volume of snow (cm3) per unit area (cm2). Snow thickness LWE cm Mean snow depth liquid water equivalent (LWE) over the period 00h-24h local time measured as volume of snow (cm3) per unit area (cm2) if all the snow had melted and had not penetrated the soil, runoff, or evaporated. Solar radiation flux J m-2 day-1 Total amount of energy provided by solar radiation at the surface over the period 00-24h local time per unit area and time. Solid precipitation duration fraction Dimensionless The number of hours with solid precipitation (freezing rain, snow, wet snow, mixture of rain and snow, and ice pellets) over the period 00h-24h local time divided by 24 hours and per unit of area. Vapour pressure hPa Contribution to the total atmospheric pressure provided by the water vapour over the period 00-24h local time per unit of time. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 10m wind speed m s-1 Mean wind speed at a height of 10 metres above the surface over the period 00h-24h local time. 10m wind speed m s-1 Mean wind speed at a height of 10 metres above the surface over the period 00h-24h local time. 2m dewpoint temperature K Mean dewpoint temperature at a height of 2 metres above the surface over the period 00h-24h local time. The dew point is the temperature to which air must be cooled to become saturated with water vapor. In combination with the air temperature it is used to assess relative humidity. 2m dewpoint temperature K Mean dewpoint temperature at a height of 2 metres above the surface over the period 00h-24h local time. The dew point is the temperature to which air must be cooled to become saturated with water vapor. In combination with the air temperature it is used to assess relative humidity. 2m relative humidity % Relative humidity at 06h, 09h, 12h. 15h, 18h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. 2m relative humidity % Relative humidity at 06h, 09h, 12h. 15h, 18h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. 2m temperature K Air temperature at a height of 2 metres above the surface. 2m temperature K Air temperature at a height of 2 metres above the surface. Cloud cover Dimensionless The number of hours with clouds over the period 00h-24h local time divided by 24 hours. Cloud cover Dimensionless The number of hours with clouds over the period 00h-24h local time divided by 24 hours. Liquid precipitation duration fraction Dimensionless The number of hours with precipitation over the period 00h-24h local time divided by 24 hours and per unit of area. Liquid precipitation is equivalent to the height of the layer of water that would have formed from precipitation had the water not penetrated the soil, run off, or evaporated. Liquid precipitation duration fraction Dimensionless The number of hours with precipitation over the period 00h-24h local time divided by 24 hours and per unit of area. Liquid precipitation is equivalent to the height of the layer of water that would have formed from precipitation had the water not penetrated the soil, run off, or evaporated. Precipitation flux mm day-1 Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per day. Precipitation flux mm day-1 Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per day. Snow thickness cm Mean snow depth over the period 00h-24h local time measured as volume of snow (cm3) per unit area (cm2). Snow thickness cm Mean snow depth over the period 00h-24h local time measured as volume of snow (cm3) per unit area (cm2). Snow thickness LWE cm Mean snow depth liquid water equivalent (LWE) over the period 00h-24h local time measured as volume of snow (cm3) per unit area (cm2) if all the snow had melted and had not penetrated the soil, runoff, or evaporated. Snow thickness LWE cm Mean snow depth liquid water equivalent (LWE) over the period 00h-24h local time measured as volume of snow (cm3) per unit area (cm2) if all the snow had melted and had not penetrated the soil, runoff, or evaporated. Solar radiation flux J m-2 day-1 Total amount of energy provided by solar radiation at the surface over the period 00-24h local time per unit area and time. Solar radiation flux J m-2 day-1 Total amount of energy provided by solar radiation at the surface over the period 00-24h local time per unit area and time. Solid precipitation duration fraction Dimensionless The number of hours with solid precipitation (freezing rain, snow, wet snow, mixture of rain and snow, and ice pellets) over the period 00h-24h local time divided by 24 hours and per unit of area. Solid precipitation duration fraction Dimensionless The number of hours with solid precipitation (freezing rain, snow, wet snow, mixture of rain and snow, and ice pellets) over the period 00h-24h local time divided by 24 hours and per unit of area. Vapour pressure hPa Contribution to the total atmospheric pressure provided by the water vapour over the period 00-24h local time per unit of time. Vapour pressure hPa Contribution to the total atmospheric pressure provided by the water vapour over the period 00-24h local time per unit of time. 351 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/pan-european-high-resolution-image-mosaic-2012-true-0 https://land.copernicus.eu/imagery-in-situ/european-image-mosaics/high-resolution/Image%202012/core-1-2013-cov-2-5m Pan-European High Resolution Image Mosaic 2012 - True Colour, Coverage 2 (5 m), June 2015 The pan-European High Resolution (HR) Image Mosaic 2012 provides HR1 (High Resolution: 5 meter) coverage over Europe. The surface covered by the image dataset is 5.8 million square kilometres and has a spatial resolution of 5 meters. The imagery is composed during specific acquisition windows in 2011, 2012 and 2013. Coverage 2 acquisitions are expected to be 6 weeks away from Coverage 1, down to a minimum of 2 weeks for northern countries, including United Kingdom. The ± 6 weeks criteria might not be strictly applied over Atlantic Islands and French DOMs (seasonal changes are limited in the equatorial DOMs). Images are derived from the following satellite sensors: RapidEye constellation The mosaic primarily is used as input data in the production of various Copernicus Land Monitoring Service (CLMS) datasets and services, such as land cover maps and high resolution layers on land cover characteristic and can be also useful for CLMS users for visualizations and classifications on land. The input imagery for the creation of the mosaic is provided by ESA. Due to license restrictions, HR Image Mosaic 2012 is only available as a web service (WMS), and not for data download. 352 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-greenhouse-gas-inversion https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-greenhouse-gas-inversion cams-global-greenhouse-gas-inversion This data set contains net fluxes at the surface, atmospheric mixing ratios at model levels, and column-mean atmospheric mixing ratios for carbon dioxide (CO2), methane (CH4) and nitrous oxide (N20). Natural and anthropogenic surface fluxes of greenhouse gases are key drivers of the evolution of Earth’s climate, so their monitoring is essential. Such information has been used in particular as part of the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). Ground-based and satellite remote-sensing observations provide a means to quantifying the net fluxes between the land and ocean on the one hand and the atmosphere on the other hand. This is done through a process called atmospheric inversion, which uses transport models of the atmosphere to link the observed concentrations of CO2, CH4 and N2O to the net fluxes at the Earth's surface. By correctly modelling the winds, vertical diffusion, and convection in the global atmosphere, the observed concentrations of the greenhouse gases are used to infer the surface fluxes for the last few decades. For CH4 and N2O, the flux inversions account also for the chemical loss of these greenhouse gases. The net fluxes include contributions from the natural biosphere (e.g., vegetation, wetlands) as well anthropogenic contributions (e.g., fossil fuel emissions, rice fields). The data sets for the three species are updated once or twice per year adding the most recent year to the data record, while re-processing the original data record for consistency. This is reflected by the different version numbers. In addition, fluxes for methane are available based on surface air samples only or based on a combination of surface air samples and satellite observations (reflected by an 's' in the version number). More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 1.9° x 3.75° (CO2, N2O), 2° x 3° (CH4) Temporal coverage 1979 - 2023 (CO2), 1990 - 2022 (CH4), 1995 - 2021 (N2O) Temporal resolution 3-hourly (CO2, N2O), 6-hourly/daily (CH4), monthly File format NetCDF DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 1.9° x 3.75° (CO2, N2O), 2° x 3° (CH4) Horizontal resolution 1.9° x 3.75° (CO2, N2O), 2° x 3° (CH4) Temporal coverage 1979 - 2023 (CO2), 1990 - 2022 (CH4), 1995 - 2021 (N2O) Temporal coverage 1979 - 2023 (CO2), 1990 - 2022 (CH4), 1995 - 2021 (N2O) Temporal resolution 3-hourly (CO2, N2O), 6-hourly/daily (CH4), monthly Temporal resolution 3-hourly (CO2, N2O), 6-hourly/daily (CH4), monthly File format NetCDF File format NetCDF MAIN VARIABLES Name Units Carbon dioxide dry mole fraction 10-6 mol mol-1 Carbon dioxide total column dry mole fraction 10-6 mol mol-1 Fossile emissions of carbon for the whole grid box kgC m-2 s-1 Fossile emissions of carbon for the whole grid box and the whole month kgC m-2 month-1 Methane dry mole fraction 10-9 Methane total column dry mole fraction 10-9 Nitrous oxide dry mole fraction 10-9 mol mol-1 Posterior land surface upward mass flux of carbon for the whole grid box and the whole month without fossile kgC m-2 month-1 Posterior ocean surface upward mass flux of carbon for the whole grid box and the whole month without fossile kgC m-2 month-1 Posterior surface upward mass flux of carbon (including fossile) kgC m-2 s-1 Prior land surface upward mass flux of carbon for the whole grid box and the whole month without fossile kgC m-2 month-1 Prior ocean surface upward mass flux of carbon for the whole grid box and the whole month without fossile kgC m-2 month-1 Prior surface upward mass flux of carbon (including fossile) kgC m-2 s-1 Surface upward mass flux of methane kg m-2 s-1 Surface upward mass flux of methane from biomass-burning sources kg m-2 s-1 Surface upward mass flux of methane from other sources kg m-2 s-1 Surface upward mass flux of methane from rice sources kg m-2 s-1 Surface upward mass flux of methane from wetlands sources kg m-2 s-1 Surface upward mass flux of nitrogen for the whole grid box and the whole month kgN m-2 month-1 MAIN VARIABLES MAIN VARIABLES Name Units Name Units Carbon dioxide dry mole fraction 10-6 mol mol-1 Carbon dioxide dry mole fraction 10-6 mol mol-1 Carbon dioxide total column dry mole fraction 10-6 mol mol-1 Carbon dioxide total column dry mole fraction 10-6 mol mol-1 Fossile emissions of carbon for the whole grid box kgC m-2 s-1 Fossile emissions of carbon for the whole grid box kgC m-2 s-1 Fossile emissions of carbon for the whole grid box and the whole month kgC m-2 month-1 Fossile emissions of carbon for the whole grid box and the whole month kgC m-2 month-1 Methane dry mole fraction 10-9 Methane dry mole fraction 10-9 Methane total column dry mole fraction 10-9 Methane total column dry mole fraction 10-9 Nitrous oxide dry mole fraction 10-9 mol mol-1 Nitrous oxide dry mole fraction 10-9 mol mol-1 Posterior land surface upward mass flux of carbon for the whole grid box and the whole month without fossile kgC m-2 month-1 Posterior land surface upward mass flux of carbon for the whole grid box and the whole month without fossile kgC m-2 month-1 Posterior ocean surface upward mass flux of carbon for the whole grid box and the whole month without fossile kgC m-2 month-1 Posterior ocean surface upward mass flux of carbon for the whole grid box and the whole month without fossile kgC m-2 month-1 Posterior surface upward mass flux of carbon (including fossile) kgC m-2 s-1 Posterior surface upward mass flux of carbon (including fossile) kgC m-2 s-1 Prior land surface upward mass flux of carbon for the whole grid box and the whole month without fossile kgC m-2 month-1 Prior land surface upward mass flux of carbon for the whole grid box and the whole month without fossile kgC m-2 month-1 Prior ocean surface upward mass flux of carbon for the whole grid box and the whole month without fossile kgC m-2 month-1 Prior ocean surface upward mass flux of carbon for the whole grid box and the whole month without fossile kgC m-2 month-1 Prior surface upward mass flux of carbon (including fossile) kgC m-2 s-1 Prior surface upward mass flux of carbon (including fossile) kgC m-2 s-1 Surface upward mass flux of methane kg m-2 s-1 Surface upward mass flux of methane kg m-2 s-1 Surface upward mass flux of methane from biomass-burning sources kg m-2 s-1 Surface upward mass flux of methane from biomass-burning sources kg m-2 s-1 Surface upward mass flux of methane from other sources kg m-2 s-1 Surface upward mass flux of methane from other sources kg m-2 s-1 Surface upward mass flux of methane from rice sources kg m-2 s-1 Surface upward mass flux of methane from rice sources kg m-2 s-1 Surface upward mass flux of methane from wetlands sources kg m-2 s-1 Surface upward mass flux of methane from wetlands sources kg m-2 s-1 Surface upward mass flux of nitrogen for the whole grid box and the whole month kgN m-2 month-1 Surface upward mass flux of nitrogen for the whole grid box and the whole month kgN m-2 month-1 353 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-necd https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-necd app-necd This application helps to identify if in a specific year, an EU Member State experienced an exceptionally cold winter or an exceptionally dry summer, in the context of Directive (EU) 2016/2284 on the reduction of national emissions of certain atmospheric pollutants (“the NEC Directive” or NECD). Article 5(2) of Directive (EU) 2016/2284 states that: ‘If in a given year a Member State, due to an exceptionally cold winter or an exceptionally dry summer, cannot comply with its emission reduction commitments, it may comply with those commitments by averaging its national annual emissions for the year in question, the year preceding that year and the year following it, provided that this average does not exceed the national annual emission level determined by the Member State's reduction commitment.’ This application shows a map containing four visible layers, applicable to both temperature and precipitation: Proximity to threshold – shows for each year the difference between the variable and the threshold. This layer displays how far each Member State is from the threshold value for the selected year in the time slider. Below threshold – a binary display showing the Member States falling below the threshold for the selected year in the time slider (Figure 2). Threshold (10th percentile) – displays the 10th percentile value for each Member State for the selected year in the time slider. Mean temperature (Jan-Feb-Mar)/ Total precipitation (Jun-Jul-Aug) – the default map selection - displays the variable from the dropdown menu for the selected year in the time slider (Figure 1). Proximity to threshold – shows for each year the difference between the variable and the threshold. This layer displays how far each Member State is from the threshold value for the selected year in the time slider. Below threshold – a binary display showing the Member States falling below the threshold for the selected year in the time slider (Figure 2). Threshold (10th percentile) – displays the 10th percentile value for each Member State for the selected year in the time slider. Mean temperature (Jan-Feb-Mar)/ Total precipitation (Jun-Jul-Aug) – the default map selection - displays the variable from the dropdown menu for the selected year in the time slider (Figure 1). INPUT VARIABLES Name Units Description Source Air temperature K ERA5 monthly averaged temperature of air at 2m above the surface. ERA5 Mean total precipitation rate kg m-2 day-1 ERA5 monthly averaged total precipitation rate. ERA5 INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Air temperature K ERA5 monthly averaged temperature of air at 2m above the surface. ERA5 Air temperature K ERA5 monthly averaged temperature of air at 2m above the surface. ERA5 ERA5 Mean total precipitation rate kg m-2 day-1 ERA5 monthly averaged total precipitation rate. ERA5 Mean total precipitation rate kg m-2 day-1 ERA5 monthly averaged total precipitation rate. ERA5 ERA5 354 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-sea-level-global https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global satellite-sea-level-global This dataset provides gridded daily and monthly mean global estimates of sea level anomaly based on satellite altimetry measurements. The rise in global mean sea level in recent decades has been one of the most important and well-known consequences of climate warming, putting a large fraction of the world population and economic infrastructure at greater risk of flooding. However, changes in the global average sea level mask regional variations that can be one order of magnitude larger. Therefore, it is essential to measure changes in sea level over the world’s oceans as accurately as possible. Sea level anomaly is defined as the height of water over the mean sea surface in a given time and region. In this dataset sea level anomalies are computed with respect to a twenty-year mean reference period (1993-2012) using up-to-date altimeter standards. In the past, the altimeter sea level datasets were distributed on the CNES AVISO altimetry portal until their production was taken over by the Copernicus Marine Environment Monitoring Service (CMEMS) and the Copernicus Climate Change Service (C3S) in 2015 and 2016 respectively. The sea level dataset provided here by C3S is climate-oriented, that is, dedicated to the monitoring of the long-term evolution of sea level and the analysis of the ocean/climate indicators, both requiring a homogeneous and stable sea level record. To achieve this, a steady two-satellite merged constellation is used at all time steps in the production system: one satellite serves as reference and ensures the long-term stability of the data record; the other satellite (which varies across the record) is used to improve accuracy, sample mesoscale processes and provide coverage at high latitudes. The C3S sea level dataset is used to produce Ocean Monitoring Indicators (e.g. global and regional mean sea level evolution), available in the CMEMS catalogue. The CMEMS sea level dataset has a more operational focus as it is dedicated to the retrieval of mesoscale signals in the context of ocean modeling and analysis of the ocean circulation on a global or regional scale. Such applications require the most accurate sea level estimates at each time step with the best spatial sampling of the ocean with all satellites available, with less emphasis on long-term stability and homogeneity. This dataset is updated three times a year with a delay of about 5 months relative to present time. This delay is mainly due to the timeliness of the input data, the centred processing temporal window and the validation process. However, these processing and validation steps are essential to enhance the stability and accuracy of the sea level products and make them suitable for climate applications. This dataset includes estimates of sea level anomaly and absolute dynamic topography together with the corresponding geostrophic velocities, which provide an approximation of the ocean surface currents. More details about these variables, the sea level retrieval algorithms, additional filters, optimisation procedures, and the error estimation can be found in the documentation. DATA DESCRIPTION Data type Gridded Horizontal coverage Global ocean Horizontal resolution 0.25° x 0.25° Vertical coverage Single level Temporal coverage 1 January 1993 to present (with a latency of about 5 months) Temporal resolution Daily and monthly Temporal gaps No gaps File format NetCDF-4 Versions vDT2021: Regularly updated; based on new versions of Level-2 input data and updated production system. vDT2018: No longer updated; covering 1 January 1993 to 3 June 2020. Update frequency Approximately 3 times / year DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global ocean Horizontal coverage Global ocean Horizontal resolution 0.25° x 0.25° Horizontal resolution 0.25° x 0.25° Vertical coverage Single level Vertical coverage Single level Temporal coverage 1 January 1993 to present (with a latency of about 5 months) Temporal coverage 1 January 1993 to present (with a latency of about 5 months) Temporal resolution Daily and monthly Temporal resolution Daily and monthly Temporal gaps No gaps Temporal gaps No gaps File format NetCDF-4 File format NetCDF-4 Versions vDT2021: Regularly updated; based on new versions of Level-2 input data and updated production system. vDT2018: No longer updated; covering 1 January 1993 to 3 June 2020. Versions vDT2021: Regularly updated; based on new versions of Level-2 input data and updated production system. vDT2018: No longer updated; covering 1 January 1993 to 3 June 2020. Update frequency Approximately 3 times / year Update frequency Approximately 3 times / year MAIN VARIABLES Name Units Description Absolute dynamic topography m Sea surface height above the geoid computed as the sum of the sea level anomaly with the mean dynamic topography. Daily fields are provided. Absolute geostrophic velocity meridian component m s-1 Northward component of the absolute geostrophic current. Daily fields are provided. Absolute geostrophic velocity zonal component m s-1 Eastward component of the absolute geostrophic current. Daily fields are provided. Eddy kinetic energy cm-2 s-2 Surface gridded eddy kinetic energy (EKE) derived from the sea level anomaly field, based on the geostrophic relationship. Daily and monthly mean fields are provided. Geostrophic velocity anomalies meridian component m s-1 Northward component of the geostrophic current. Daily fields are provided. Geostrophic velocity anomalies zonal component m s-1 Eastward component of the geostrophic current. Daily fields are provided. Sea level anomaly m Sea surface height above mean sea surface computed with respect to a 20-year mean reference period (1993-2012). Daily and monthly mean fields are provided. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Absolute dynamic topography m Sea surface height above the geoid computed as the sum of the sea level anomaly with the mean dynamic topography. Daily fields are provided. Absolute dynamic topography m Sea surface height above the geoid computed as the sum of the sea level anomaly with the mean dynamic topography. Daily fields are provided. Absolute geostrophic velocity meridian component m s-1 Northward component of the absolute geostrophic current. Daily fields are provided. Absolute geostrophic velocity meridian component m s-1 Northward component of the absolute geostrophic current. Daily fields are provided. Absolute geostrophic velocity zonal component m s-1 Eastward component of the absolute geostrophic current. Daily fields are provided. Absolute geostrophic velocity zonal component m s-1 Eastward component of the absolute geostrophic current. Daily fields are provided. Eddy kinetic energy cm-2 s-2 Surface gridded eddy kinetic energy (EKE) derived from the sea level anomaly field, based on the geostrophic relationship. Daily and monthly mean fields are provided. Eddy kinetic energy cm-2 s-2 Surface gridded eddy kinetic energy (EKE) derived from the sea level anomaly field, based on the geostrophic relationship. Daily and monthly mean fields are provided. Geostrophic velocity anomalies meridian component m s-1 Northward component of the geostrophic current. Daily fields are provided. Geostrophic velocity anomalies meridian component m s-1 Northward component of the geostrophic current. Daily fields are provided. Geostrophic velocity anomalies zonal component m s-1 Eastward component of the geostrophic current. Daily fields are provided. Geostrophic velocity anomalies zonal component m s-1 Eastward component of the geostrophic current. Daily fields are provided. Sea level anomaly m Sea surface height above mean sea surface computed with respect to a 20-year mean reference period (1993-2012). Daily and monthly mean fields are provided. Sea level anomaly m Sea surface height above mean sea surface computed with respect to a 20-year mean reference period (1993-2012). Daily and monthly mean fields are provided. RELATED VARIABLES Name Units Description Ice flag Dimensionless Flag indicating the presence (flag = 1) or absence (flag = 0) of sea ice. See Product User Guide for more details. This variable is only available in version vDT2021. Instrumental drift correction m Correction applied to the TOPEX-A satellite input data from 1993-1998 to correct for instrumental drift. This correction is based on a global comparison of observations from satellite altimetry versus tide gauges. See Product User Guide and Product Quality Assessment Report for more details. This variable is only available in version vDT2021. RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Ice flag Dimensionless Flag indicating the presence (flag = 1) or absence (flag = 0) of sea ice. See Product User Guide for more details. This variable is only available in version vDT2021. Ice flag Dimensionless Flag indicating the presence (flag = 1) or absence (flag = 0) of sea ice. See Product User Guide for more details. This variable is only available in version vDT2021. Instrumental drift correction m Correction applied to the TOPEX-A satellite input data from 1993-1998 to correct for instrumental drift. This correction is based on a global comparison of observations from satellite altimetry versus tide gauges. See Product User Guide and Product Quality Assessment Report for more details. This variable is only available in version vDT2021. Instrumental drift correction m Correction applied to the TOPEX-A satellite input data from 1993-1998 to correct for instrumental drift. This correction is based on a global comparison of observations from satellite altimetry versus tide gauges. See Product User Guide and Product Quality Assessment Report for more details. This variable is only available in version vDT2021. 355 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/n2k-2006-vector-europe-6-yearly-jul-2021 https://land.copernicus.eu/local/natura/n2k-2006?tab=download N2K 2006 (vector), Europe, 6-yearly, Jul. 2021 This metadata refers to CLMS N2K 2006 product, the Copernicus Land Cover/Land Use (LC/LU) status map, with 2006 as reference year for the classification, tailored to the needs of biodiversity monitoring in selected Natura2000 sites: 4790 sites of natural and semi-natural grassland formations listed in Annex I of the Habitats Directive, including a 2 km buffer zone surrounding the sites and covering an area of 631.820 km² across Europe (EU27, the United Kingdom and Switzerland). The product includes three Emerald sites in Switzerland. LC/LU has been extracted from VHR satellite data and other available data. This metadata specifically refers to the revision of the 2006 N2K status map carried out during the production of the 2018 update. The production of N2K updates was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 356 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-uerra-europe-complete https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-uerra-europe-complete reanalysis-uerra-europe-complete The UERRA datasets contain reanalysis data of the atmosphere, the surface and near-surface as well as for the soil covering Europe. Essential climate variables are generated with the UERRA-HARMONIE and the MESCAN-SURFEX systems. UERRA-HARMONIE is a 3-dimensional variational data assimilation system, while MESCAN-SURFEX is a complementary surface analysis system. Using the Optimal Interpolation method, MESCAN provides the best estimate of daily accumulated precipitation and six-hourly air temperature and relative humidity at 2 meters above the model topography. The land surface platform SURFEX is forced with downscaled forecast fields from UERRA-HARMONIE as well as MESCAN analyses. It is run offline, i.e. without feedback to the atmospheric analysis performed in MESCAN or the UERRA-HARMONIE data assimilation cycles. Using SURFEX offline allows taking full benefit of precipitation analysis and to use the more advanced physics options to better represent surface variables such as surface temperature and surface fluxes, and soil processes related to water and heat transfer in the soil and snow. In general, reanalysis combines model data with observations into a complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (6 hours in the UERRA-HARMONIE system) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. The assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The laws of physics allow for estimates at locations where data coverage is low. The provision of estimates at each grid point in Europe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with. The observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. UERRA-HARMONIE data is available from 1961 and is updated once a month with a delay to real-time of about 4 months. The system provides four analyses per day – at 0 UTC, 6 UTC, 12 UTC, and 18 UTC. Between the analyses, forecasts of the system are available with hourly resolution. Hence, estimates of the status of the atmosphere are available for every hour since 1961. Moreover, forecasts up to 30 hours are available from the analyses initialised at 0 UTC and 12 UTC. In addition to observations in the model domain, a regional reanalysis needs information at its lateral boundaries. For the UERRA-HARMONIE system, this information is taken from the global reanalyses ERA40 (until the end of 1978) and ERA-interim (from 1979). The improvement over global products comes with the higher horizontal resolution that allows incorporating more regional details, e.g. topography. Moreover, it enables the system even to consider more observations at places with dense observation networks. The UERRA-HARMONIE regional reanalysis is produced at a horizontal resolution of 11km and MESCAN SURFEX provides data at a resolution of 5.5km. For the UERRA-HARMONIE system, variables are produced at the surface and on model levels (65 levels) but are also interpolated to two other level types: pressure levels (24 levels between 1000-10hPa), and height levels (11 levels between 15m-500m). The output of height levels were introduced with special focus on the wind energy sector and their needs. Soil data is available on 14 levels from the surface to a depth of 12m. The number of available parameters varies between the different level types. In order to make data access more manageable, the UERRA-HARMONIE and MESCAN-SURFEX dataset has been split into four records. Analysis time steps are available via the CDS whereas forecasts are available only through CDSAPI. DATA DESCRIPTION Data type Gridded Projection Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Horizontal coverage Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Horizontal resolution 11km x 11km for the UERRA-HARMONIE system. 5.5km x 5.5km for the MESCAN-SURFEX system. Vertical coverage Near surface for single level variables. From 15m to 500m for height levels variables. From 1000 hPa to 10 hPa for pressure level variables. From the surface to a depth of 12m for the MESCAN-SURFEX system. For the UERRA-HARMONIE system the vertical soil coordinates have no precise depth values. They are defined in terms of a time constant determining how quickly they adjust and restore. Please, see the documenation section for more information. Vertical resolution Single level for near surface variables. 11 height levels from 15m to 500m. 24 levels for upper-air pressure level variables. 3 soil levels for soil variables computed from the UERRA-HARMONIE system. 14 soil levels for soil variables computed from the MESCAN-SURFEX system: 0.01m, 0.04m, 0.1m, 0.2m, 0.4m, 0.6m, 0.8m, 1m, 1.5m, 2m, 3m, 5m, 8m, 12m. Temporal coverage January 1961 to July 2019. Temporal resolution Analysis are availabe each day at 00, 06, 12 and 18 UTC. Forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC. File format GRIB2 Update frequency No expected updates. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Projection Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Horizontal coverage Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Horizontal coverage Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Horizontal resolution 11km x 11km for the UERRA-HARMONIE system. 5.5km x 5.5km for the MESCAN-SURFEX system. Horizontal resolution 11km x 11km for the UERRA-HARMONIE system. 5.5km x 5.5km for the MESCAN-SURFEX system. 11km x 11km for the UERRA-HARMONIE system. 5.5km x 5.5km for the MESCAN-SURFEX system. Vertical coverage Near surface for single level variables. From 15m to 500m for height levels variables. From 1000 hPa to 10 hPa for pressure level variables. From the surface to a depth of 12m for the MESCAN-SURFEX system. For the UERRA-HARMONIE system the vertical soil coordinates have no precise depth values. They are defined in terms of a time constant determining how quickly they adjust and restore. Please, see the documenation section for more information. Vertical coverage Near surface for single level variables. From 15m to 500m for height levels variables. From 1000 hPa to 10 hPa for pressure level variables. From the surface to a depth of 12m for the MESCAN-SURFEX system. For the UERRA-HARMONIE system the vertical soil coordinates have no precise depth values. They are defined in terms of a time constant determining how quickly they adjust and restore. Please, see the documenation section for more information. Near surface for single level variables. From 15m to 500m for height levels variables. From 1000 hPa to 10 hPa for pressure level variables. From the surface to a depth of 12m for the MESCAN-SURFEX system. For the UERRA-HARMONIE system the vertical soil coordinates have no precise depth values. They are defined in terms of a time constant determining how quickly they adjust and restore. Please, see the documenation section for more information. Vertical resolution Single level for near surface variables. 11 height levels from 15m to 500m. 24 levels for upper-air pressure level variables. 3 soil levels for soil variables computed from the UERRA-HARMONIE system. 14 soil levels for soil variables computed from the MESCAN-SURFEX system: 0.01m, 0.04m, 0.1m, 0.2m, 0.4m, 0.6m, 0.8m, 1m, 1.5m, 2m, 3m, 5m, 8m, 12m. Vertical resolution Single level for near surface variables. 11 height levels from 15m to 500m. 24 levels for upper-air pressure level variables. 3 soil levels for soil variables computed from the UERRA-HARMONIE system. 14 soil levels for soil variables computed from the MESCAN-SURFEX system: 0.01m, 0.04m, 0.1m, 0.2m, 0.4m, 0.6m, 0.8m, 1m, 1.5m, 2m, 3m, 5m, 8m, 12m. Single level for near surface variables. 11 height levels from 15m to 500m. 24 levels for upper-air pressure level variables. 3 soil levels for soil variables computed from the UERRA-HARMONIE system. 14 soil levels for soil variables computed from the MESCAN-SURFEX system: 0.01m, 0.04m, 0.1m, 0.2m, 0.4m, 0.6m, 0.8m, 1m, 1.5m, 2m, 3m, 5m, 8m, 12m. Temporal coverage January 1961 to July 2019. Temporal coverage January 1961 to July 2019. Temporal resolution Analysis are availabe each day at 00, 06, 12 and 18 UTC. Forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC. Temporal resolution Analysis are availabe each day at 00, 06, 12 and 18 UTC. Forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC. Analysis are availabe each day at 00, 06, 12 and 18 UTC. Forecasts up to 30 hours initialised from the analyses at 00 and 12 UTC. File format GRIB2 File format GRIB2 Update frequency No expected updates. Update frequency No expected updates. 357 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/medium-resolution-vegetation-phenology-and-productivity-0 https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum_d1-0.pdf Medium Resolution Vegetation Phenology and Productivity: End-of-season date (raster 500m), Oct. 2022 The raster file is the time series of the end of the vegetation growing season. The end of the growing season time-series is based on the time series of the Plant Phenology Index (PPI) derived from the MODIS BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR). The PPI index is optimized for efficient monitoring of vegetation phenology and is derived from the source MODIS data using radiative transfer solutions applied to the reflectance in visible-red and near infrared spectral domains. The end of season indicator is based on calculating the end of the vegetation growing season from the annual PPI temporal curve using the TIMESAT software for each year between and including 2000 and 2021. The End-of-Season Date (EOSD), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The End-of-Season Date (EOSD) marks the date when the vegetation growing season ends in the time profile of the Plant Phenology Index (PPI). The end-of-season occurs, by definition, when the PPI value reaches 15% of the season amplitude during the green-down period. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The End-of-Season Date (EOSD) is one of the 13 parameters. The full list is available in the Product User Manual: https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… The End-of-Season Date (EOSD) time series dataset is made available as raster files with 500x 500m resolution, in ETRS89-LAEA projection corresponding to the MCD43 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2000 onwards. It is updated in the first quarter of each year. The full on-line access to open and free data for this resource will be made available by the end of 2022. Until then the data will be made available 'on-demand' by filling in the form at: https://land.copernicus.eu/contact-form https://land.copernicus.eu/contact-form 358 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-wave-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTICSEA_ANALYSISFORECAST_WAV_003_010 Baltic Sea Wave Analysis and Forecast Short description: This Baltic Sea wave model product provides forecasts for the wave conditions in the Baltic Sea. The Baltic forecast is updated twice a day providing a new six days forecast with hourly instantaneous data for significant wave height, wave period and wave direction for total sea, wind sea and swell, and also Stokes drift. The product is based on the wave model WAM cycle 4.6.2. The wave model is forced with surface currents, sea level anomaly and ice information from the CMEMS BAL MFC ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). DOI (product) :https://doi.org/10.48670/moi-00011 https://doi.org/10.48670/moi-00011 359 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-high-resolution-l3s-sea-surface http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_MED_PHY_L3S_MY_010_042 Mediterranean Sea - High Resolution L3S Sea Surface Temperature Reprocessed Short description: The CMEMS Reprocessed (REP) Mediterranean Sea (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05° resolution grid covering the period from January 1st 1982 to present (currently, up to six months before real time). The BS-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record provided by the ESA Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) initiatives, but also includes in input an adjusted version of the AVHRR Pathfinder dataset version 5.3 to increase the input observation coverage. DOI (product) :https://doi.org/10.48670/moi-00314 https://doi.org/10.48670/moi-00314 360 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/lake-ice-extent-2017-present-raster-250-m-baltic-daily https://land.copernicus.eu/global/products/ Lake Ice Extent 2017-present (raster 250 m), Baltic, daily - version 1 Lake ice is a good climate indicator and interesting parameter in hydrological modelling, weather prediction and, after integration over time, estimating dates for ice phenology events (ice break-up, ice-off). Lake Ice Extent products classify ice for freshwater bodies, per cloud-free pixel, into (i) fully snow covered ice, (ii) partially snow covered ice/clear ice and (iii) open water. 361 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/lake-ice-extent-2021-present-raster-500-m-northern https://land.copernicus.eu/global/products/lie Lake Ice Extent 2021-present (raster 500 m), northern hemisphere, daily - version 1 Lake ice is a good climate indicator and interesting parameter in hydrological modelling, weather prediction and, after integration over time, estimating dates for ice phenology events (ice break-up, ice-off). Lake Ice Extent products classify ice for freshwater bodies, per cloud-free pixel, into (i) fully snow covered ice, (ii) partially snow covered ice/clear ice and (iii) open water. 362 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sar-sea-ice-berg-concentration-and-individual-icebergs http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_007 SAR Sea Ice Berg Concentration and Individual Icebergs Observed with Sentinel-1 Short description: The iceberg product contains 4 datasets (IW and EW modes and mosaic for the two modes) describing iceberg concentration as number of icebergs counted within 10x10 km grid cells. The iceberg concentration is derived by applying a Constant False Alarm Rate (CFAR) algorithm on data from Synthetic Aperture Radar (SAR) satellite sensors. The iceberg product also contains two additional datasets of individual iceberg positions in Greenland-Newfoundland-Labrador Waters. These datasets are in shapefile format to allow the best representation of the icebergs (the 1st dataset contains the iceberg point observations, the 2nd dataset contains the polygonized satellite coverage). These are also derived by applying a Constant False Alarm Rate (CFAR) algorithm on Sentinel-1 SAR imagery. Despite its precision (individual icebergs are proposed), this product is a generic and automated product and needs expertise to be correctly used. For all applications concerning marine navigation, please refer to the national Ice Service of the country concerned. DOI (product) :https://doi.org/10.48670/moi-00129 https://doi.org/10.48670/moi-00129 363 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-era5-single-levels-preliminary-back-extension https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-preliminary-back-extension reanalysis-era5-single-levels-preliminary-back-extension This entry is a preliminary version of the ERA5 reanalysis back extension from 1950 to 1978. It has now been superseded by the ERA5 Climate Data Store entries from 1940 onwards and will be deprecated in due course. Therefore, users are advised to use the latter, final release, instead. Although in many other respects the quality of this dataset is quite satisfactory (Bell et al., 2021), this preliminary data does suffer from tropical cyclones that are sometimes unrealistically intense. This is in contrast with the ERA5 product from 1959 onwards. For more details see the articles, ERA5 back extension 1950-1978 (Preliminary version): tropical cyclones are too intense and Changes in the ERA5 back extension compared to its preliminary version. (Bell et al., 2021) ERA5 back extension 1950-1978 (Preliminary version): tropical cyclones are too intense Changes in the ERA5 back extension compared to its preliminary version ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Currently, data is available from 1940, with superseded Climate Data Store entries for 1950-1978 (preliminary back extension, this page) and from 1940 onwards (final release plus timely updates). ERA5 replaces the ERA-Interim reanalysis. ERA5 Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. this article these guidelines Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1950 to 1978 (preliminary version)". DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Temporal coverage 1950 to 1978 Temporal resolution Hourly File format GRIB Update frequency Daily DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Horizontal resolution Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Temporal coverage 1950 to 1978 Temporal coverage 1950 to 1978 Temporal resolution Hourly Temporal resolution Hourly File format GRIB File format GRIB Update frequency Daily Update frequency Daily MAIN VARIABLES Name Units Description 100m u-component of wind m s-1 This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind. 100m v-component of wind m s-1 This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind. 10m u-component of neutral wind m s-1 This parameter is the eastward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m u-component of wind m s-1 This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m v-component of neutral wind m s-1 This parameter is the northward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m v-component of wind m s-1 This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m wind gust since previous post-processing m s-1 Maximum 3 second wind at 10 m height as defined by WMO. Parametrization represents turbulence only before 01102008; thereafter effects of convection are included. The 3 s gust is computed every time step and and the maximum is kept since the last postprocessing. 2m dewpoint temperature K This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Air density over the oceans kg m-3 This parameter is the mass of air per cubic metre over the oceans, derived from the temperature, specific humidity and pressure at the lowest model level in the atmospheric model. This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Angle of sub-gridscale orography radians This parameter is one of four parameters (the others being standard deviation, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. The angle of the sub-grid scale orography characterises the geographical orientation of the terrain in the horizontal plane (from a bird's-eye view) relative to an eastwards axis. This parameter does not vary in time. Anisotropy of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, slope and angle of sub-gridscale orography) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter is a measure of how much the shape of the terrain in the horizontal plane (from a bird's-eye view) is distorted from a circle. A value of one is a circle, less than one an ellipse, and 0 is a ridge. In the case of a ridge, wind blowing parallel to it does not exert any drag on the flow, but wind blowing perpendicular to it exerts the maximum drag. This parameter does not vary in time. Benjamin-feir index Dimensionless This parameter is used to calculate the likelihood of freak ocean waves, which are waves that are higher than twice the mean height of the highest third of waves. Large values of this parameter (in practice of the order 1) indicate increased probability of the occurrence of freak waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived from the statistics of the two-dimensional wave spectrum. More precisely, it is the square of the ratio of the integral ocean wave steepness and the relative width of the frequency spectrum of the waves. Further information on the calculation of this parameter is given in Section 10.6 of the ECMWF Wave Model documentation. Boundary layer dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Boundary layer height m This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface. The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop. The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review. Charnock Dimensionless This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind. When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by the ECMWF Wave Model. Clear-sky direct solar radiation at surface J m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Cloud base height m The height above the Earth's surface of the base of the lowest cloud layer, at the specified time. This parameter is calculated by searching from the second lowest model level upwards, to the height of the level where cloud fraction becomes greater than 1% and condensate content greater than 1.E-6 kg kg-1. Fog (i.e., cloud in the lowest model layer) is not considered when defining cloud base height. Coefficient of drag with waves Dimensionless This parameter is the resistance that ocean waves exert on the atmosphere. It is sometimes also called a "friction coefficient". It is calculated by the wave model as the ratio of the square of the friction velocity, to the square of the neutral wind speed at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. Convective available potential energy J kg-1 This is an indication of the instability (or stability) of the atmosphere and can be used to assess the potential for the development of convection, which can lead to heavy rainfall, thunderstorms and other severe weather. In the ECMWF Integrated Forecasting System (IFS), CAPE is calculated by considering parcels of air departing at different model levels below the 350 hPa level. If a parcel of air is more buoyant (warmer and/or with more moisture) than its surrounding environment, it will continue to rise (cooling as it rises) until it reaches a point where it no longer has positive buoyancy. CAPE is the potential energy represented by the total excess buoyancy. The maximum CAPE produced by the different parcels is the value retained. Large positive values of CAPE indicate that an air parcel would be much warmer than its surrounding environment and therefore, very buoyant. CAPE is related to the maximum potential vertical velocity of air within an updraft; thus, higher values indicate greater potential for severe weather. Observed values in thunderstorm environments often may exceed 1000 joules per kilogram (J kg-1), and in extreme cases may exceed 5000 J kg-1. The calculation of this parameter assumes: (i) the parcel of air does not mix with surrounding air; (ii) ascent is pseudo-adiabatic (all condensed water falls out) and (iii) other simplifications related to the mixed-phase condensational heating. Convective inhibition J kg-1 This parameter is a measure of the amount of energy required for convection to commence. If the value of this parameter is too high, then deep, moist convection is unlikely to occur even if the convective available potential energy or convective available potential energy shear are large. CIN values greater than 200 J kg-1 would be considered high. An atmospheric layer where temperature increases with height (known as a temperature inversion) would inhibit convective uplift and is a situation in which convective inhibition would be large. Convective precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Rainfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Downward UV radiation at the surface J m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Duct base height m Duct base height as diagnosed from the vertical gradient of atmospheric refractivity. Eastward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Eastward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Forecast albedo Dimensionless This parameter is a measure of the reflectivity of the Earth's surface. It is the fraction of short-wave (solar) radiation reflected by the Earth's surface, for diffuse radiation, assuming a fixed spectrum of downward short-wave radiation at the surface. The values of this parameter vary between zero and one. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Short-wave radiation from the Sun is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Forecast logarithm of surface roughness for heat Dimensionless This parameter is the natural logarithm of the roughness length for heat. The surface roughness for heat is a measure of the surface resistance to heat transfer. This parameter is used to determine the air to surface transfer of heat. For given atmospheric conditions, a higher surface roughness for heat means that it is more difficult for the air to exchange heat with the surface. A lower surface roughness for heat means that it is easier for the air to exchange heat with the surface. Over the ocean, surface roughness for heat depends on the waves. Over sea-ice, it has a constant value of 0.001 m. Over land, it is derived from the vegetation type and snow cover. Forecast surface roughness m This parameter is the aerodynamic roughness length in metres. It is a measure of the surface resistance. This parameter is used to determine the air to surface transfer of momentum. For given atmospheric conditions, a higher surface roughness causes a slower near-surface wind speed. Over ocean, surface roughness depends on the waves. Over land, surface roughness is derived from the vegetation type and snow cover. Free convective velocity over the oceans m s-1 This parameter is an estimate of the vertical velocity of updraughts generated by free convection. Free convection is fluid motion induced by buoyancy forces, which are driven by density gradients. The free convective velocity is used to estimate the impact of wind gusts on ocean wave growth. It is calculated at the height of the lowest temperature inversion (the height above the surface of the Earth where the temperature increases with height). This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Friction velocity m s-1 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is a theoretical wind speed at the Earth's surface that expresses the magnitude of stress. It is calculated by dividing the surface stress by air density and taking its square root. For turbulent flow, the friction velocity is approximately constant in the lowest few metres of the atmosphere. This parameter increases with the roughness of the surface. It is used to calculate the way wind changes with height in the lowest levels of the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location at the surface of the Earth, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The (surface) geopotential height (orography) can be calculated by dividing the (surface) geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2 ). This parameter does not vary in time. Gravity wave dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. High cloud cover Dimensionless The proportion of a grid box covered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a "standard atmosphere")). The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. High vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "high". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Ice temperature layer 1 K This parameter is the sea-ice temperature in layer 1 (0 to 7cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 2 K This parameter is the sea-ice temperature in layer 2 (7 to 28cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 3 K This parameter is the sea-ice temperature in layer 3 (28 to 100cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 4 K This parameter is the sea-ice temperature in layer 4 (100 to 150cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Instantaneous 10m wind gust m s-1 This parameter is the maximum wind gust at the specified time, at a height of ten metres above the surface of the Earth. The WMO defines a wind gust as the maximum of the wind averaged over 3 second intervals. This duration is shorter than a model time step, and so the ECMWF Integrated Forecasting System (IFS) deduces the magnitude of a gust within each time step from the time-step-averaged surface stress, surface friction, wind shear and stability. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Instantaneous eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. Instantaneous large-scale surface precipitation fraction Dimensionless This parameter is the fraction of the grid box (0-1) covered by large-scale precipitation at the specified time. Large-scale precipitation is rain and snow that falls to the Earth's surface, and is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. Precipitation can also be due to convection generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box. Instantaneous moisture flux kg m-2 s-1 This parameter is the net rate of moisture exchange between the land/ocean surface and the atmosphere, due to the processes of evaporation (including evapotranspiration) and condensation, at the specified time. By convention, downward fluxes are positive, which means that evaporation is represented by negative values and condensation by positive values. Instantaneous northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. Instantaneous surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere, at the specified time, through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. The ECMWF convention for vertical fluxes is positive downwards. K index K This parameter is a measure of the potential for a thunderstorm to develop, calculated from the temperature and dew point temperature in the lower part of the atmosphere. The calculation uses the temperature at 850, 700 and 500 hPa and dewpoint temperature at 850 and 700 hPa. Higher values of K indicate a higher potential for the development of thunderstorms. This parameter is related to the probability of occurrence of a thunderstorm: <20 K No thunderstorm, 20-25 K Isolated thunderstorms, 26-30 K Widely scattered thunderstorms, 31-35 K Scattered thunderstorms, >35 K Numerous thunderstorms. Lake bottom temperature K This parameter is the temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake cover Dimensionless This parameter is the proportion of a grid box covered by inland water bodies (lakes, reservoirs, rivers and coastal waters). Values vary between 0: no inland water, and 1: grid box is fully covered with inland water. This parameter is specified from observations and does not vary in time. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth m This parameter is the mean depth of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is specified from in-situ measurements and indirect estimates and does not vary in time. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake ice depth m This parameter is the thickness of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter is the thickness of that ice layer. Lake ice temperature K This parameter is the temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). It is the temperature at the ice/atmosphere or ice/snow interface. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer depth m This parameter is the thickness of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake mix-layer temperature K This parameter is the temperature of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake shape factor Dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) i.e., it describes the shape of the vertical temperature profile. It is used to calculate the lake bottom temperature and other lake-related parameters. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake total layer temperature K This parameter is the mean temperature of the total water column in inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. This parameter is the mean temperature over the two layers. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Land-sea mask Dimensionless This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless. In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water. In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water. This parameter does not vary in time. Large scale rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Rainfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation fraction s This parameter is the accumulation of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Large-scale snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Leaf area index, high vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "high". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Leaf area index, low vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "low". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Low cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a "standard atmosphere")). Assumptions are made about the degree of overlap/randomness between clouds in different model levels. This parameter has values from 0 to 1. Low vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "low". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Maximum 2m temperature since previous post-processing K This parameter is the highest temperature of air at 2m above the surface of land, sea or inland water since the parameter was last archived in a particular forecast. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Maximum individual wave height m This parameter is an estimate of the height of the expected highest individual wave within a 20 minute time window. It can be used as a guide to the likelihood of extreme or freak waves. The interactions between waves are non-linear and occasionally concentrate wave energy giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered as a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Maximum total precipitation rate since previous post-processing kg m-2 s-1 The total precipitation is calculated from the combined large-scale and convective rainfall and snowfall rates every time step and the maximum is kept since the last postprocessing. Mean boundary layer dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean convective precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean direction of total swell degrees This parameter is the mean direction of waves associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of wind waves degrees The mean direction of waves generated by local winds. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea wave spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean eastward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean evaporation rate kg m-2 s-1 This parameter is the amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Mean gravity wave dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean large-scale precipitation fraction Dimensionless This parameter is the mean of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean large-scale precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean northward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean period of total swell s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea associated with swell, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. Mean period of wind waves s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea generated by local winds, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea spectrum. Mean potential evaporation rate kg m-2 s-1 This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean sea level pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of mean sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at mean sea level. It is calculated over all surfaces - land, sea and inland water. Maps of mean sea level pressure are used to identify the locations of low and high pressure weather systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds. The units of this parameter are pascals (Pa). Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Mean snow evaporation rate kg m-2 s-1 This parameter is the average rate of snow evaporation from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snow evaporation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Mean snowfall rate kg m-2 s-1 This parameter is the rate of snowfall at the Earth's surface. It is the sum of large-scale and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean snowmelt rate kg m-2 s-1 This parameter is the rate of snow melt in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the melting would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean square slope of waves Dimensionless This parameter can be related analytically to the average slope of combined wind-sea and swell waves. It can also be expressed as a function of wind speed under some statistical assumptions. The higher the slope, the steeper the waves. This parameter indicates the roughness of the sea/ocean surface which affects the interaction between ocean and atmosphere. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. Mean sub-surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface direct short-wave radiation flux W m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux, clear sky W m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward UV radiation flux W m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux, clear sky W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface latent heat flux W m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux, clear sky W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top downward short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux W m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux, clear sky W m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean top net short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux, clear sky W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean total precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface. It is the sum of the rates due to large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean vertical gradient of refractivity inside trapping layer m-1 Mean vertical gradient of atmospheric refractivity inside the trapping layer. Mean vertically integrated moisture divergence kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean wave direction degree true This parameter is the mean direction of ocean/sea surface waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use this type of wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of first swell partition degrees This parameter is the mean direction of waves in the first swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of second swell partition degrees This parameter is the mean direction of waves in the second swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of third swell partition degrees This parameter is the mean direction of waves in the third swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave period s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use such wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. Mean wave period based on first moment s This parameter is the reciprocal of the mean frequency of the wave components that represent the sea state. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for swell s This parameter is the reciprocal of the mean frequency of the wave components associated with swell. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for wind waves s This parameter is the reciprocal of the mean frequency of the wave components generated by local winds. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with wind waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for swell s This parameter is equivalent to the zero-crossing mean wave period for swell. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for wind waves s This parameter is equivalent to the zero-crossing mean wave period for waves generated by local winds. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period of first swell partition s This parameter is the mean period of waves in the first swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of second swell partition s This parameter is the mean period of waves in the second swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of third swell partition s This parameter is the mean period of waves in the third swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third swell partition might be from one system at one location and a different system at the neighbouring location). Mean zero-crossing wave period s This parameter represents the mean length of time between occasions where the sea/ocean surface crosses mean sea level. In combination with wave height information, it could be used to assess the length of time that a coastal structure might be under water, for example. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). In the ECMWF Integrated Forecasting System (IFS) this parameter is calculated from the characteristics of the two-dimensional wave spectrum. Medium cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a "standard atmosphere")). The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. Minimum 2m temperature since previous post-processing K This parameter is the lowest temperature of air at 2m above the surface of land, sea or inland waters since the parameter was last archived in a particular forecast. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. See further information. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Minimum total precipitation rate since previous post-processing kg m-2 s-1 The total precipitation is calculated from the combined large-scale and convective rainfall and snowfall rates every time step and the minimum is kept since the last postprocessing. Minimum vertical gradient of refractivity inside trapping layer m-1 Minimum vertical gradient of atmospheric refractivity inside the trapping layer. Model bathymetry m This parameter is the depth of water from the surface to the bottom of the ocean. It is used by the ocean wave model to specify the propagation properties of the different waves that could be present. Note that the ocean wave model grid is too coarse to resolve some small islands and mountains on the bottom of the ocean, but they can have an impact on surface ocean waves. The ocean wave model has been modified to reduce the wave energy flowing around or over features at spatial scales smaller than the grid box. Near IR albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Normalized energy flux into ocean Dimensionless This parameter is the normalised vertical flux of turbulent kinetic energy from ocean waves into the ocean. The energy flux is calculated from an estimation of the loss of wave energy due to white capping waves. A white capping wave is one that appears white at its crest as it breaks, due to air being mixed into the water. When waves break in this way, there is a transfer of energy from the waves to the ocean. Such a flux is defined to be negative. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into waves Dimensionless This parameter is the normalised vertical flux of energy from wind into the ocean waves. A positive flux implies a flux into the waves. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized stress into ocean Dimensionless This parameter is the normalised surface stress, or momentum flux, from the air into the ocean due to turbulence at the air-sea interface and breaking waves. It does not include the flux used to generate waves. The ECMWF convention for vertical fluxes is positive downwards. The stress has units of Newtons per metre squared, and this is normalised by being divided by the product of air density and the square of the friction velocity. Northward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Northward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Ocean surface stress equivalent 10m neutral wind direction degrees This parameter is the direction from which the "neutral wind" blows, in degrees clockwise from true north, at a height of ten metres above the surface of the Earth. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind direction used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind speed m s-1 This parameter is the horizontal speed of the "neutral wind", at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind speed used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Period corresponding to maximum individual wave height s This parameter is the period of the expected highest individual wave within a 20-minute time window. It can be used as a guide to the characteristics of extreme or freak waves. Wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. Occasionally waves of different periods reinforce and interact non-linearly giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered to be a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Potential evaporation m This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Precipitation type Dimensionless This parameter describes the type of precipitation at the surface, at the specified time. A precipitation type is assigned wherever there is a non-zero value of precipitation. In the ECMWF Integrated Forecasting System (IFS) there are only two predicted precipitation variables: rain and snow. Precipitation type is derived from these two predicted variables in combination with atmospheric conditions, such as temperature. Values of precipitation type defined in the IFS: 0: No precipitation, 1: Rain, 3: Freezing rain (i.e. supercooled raindrops which freeze on contact with the ground and other surfaces), 5: Snow, 6: Wet snow (i.e. snow particles which are starting to melt); 7: Mixture of rain and snow, 8: Ice pellets. These precipitation types are consistent with WMO Code Table 4.201. Other types in this WMO table are not defined in the IFS. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Sea surface temperature K This parameter (SST) is the temperature of sea water near the surface. In ERA5, this parameter is a foundation SST, which means there are no variations due to the daily cycle of the sun (diurnal variations). SST, in ERA5, is given by two external providers. Before September 2007, SST from the HadISST2 dataset is used and from September 2007 onwards, the OSTIA dataset is used. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Sea-ice cover Dimensionless This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land-sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover. In ERA5, sea-ice cover is given by two external providers. Before 1979 the HadISST2 dataset is used. From 1979 to August 2007 the OSI SAF (409a) dataset is used and from September 2007 the OSI SAF oper dataset is used. Sea ice is frozen sea water which floats on the surface of the ocean. Sea ice does not include ice which forms on land such as glaciers, icebergs and ice-sheets. It also excludes ice shelves which are anchored on land, but protrude out over the surface of the ocean. These phenomena are not modelled by the IFS. Long-term monitoring of sea ice is important for understanding climate change. Sea ice also affects shipping routes through the polar regions. Significant height of combined wind waves and swell m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. This parameter can be used to assess sea state and swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of total swell m This parameter represents the average height of the highest third of surface ocean/sea waves associated with swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of total swell only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional total swell spectrum. The total swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of wind waves m This parameter represents the average height of the highest third of surface ocean/sea waves generated by the local wind. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wind-sea wave spectrum. The wind-sea wave spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are still under the influence of the local wind. This parameter can be used to assess wind-sea waves. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of first swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the first swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of second swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the second swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of third swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the third swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Skin reservoir content m of water equivalent This parameter is the amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of "skin reservoir content" a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the "skin reservoir" by evaporation. Skin temperature K This parameter is the temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Slope of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, angle and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the slope of the sub-grid valleys, hills and mountains. A flat surface has a value of 0, and a 45 degree slope has a value of 0.5. This parameter does not vary in time. Snow albedo Dimensionless This parameter is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter changes with snow age and also depends on vegetation height. It has a range of values between 0 and 1. For low vegetation, it ranges between 0.52 for old snow and 0.88 for fresh snow. For high vegetation with snow underneath, it depends on vegetation type and has values between 0.27 and 0.38. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. Snow density kg m-3 This parameter is the mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. Snow depth m of water equivalent This parameter is the amount of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from snow from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the evaporated snow (from the snow-covered area of a grid box) were liquid and were spread evenly over the whole grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Snowmelt m of water equivalent This parameter is the accumulated amount of water that has melted from snow in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the melted snow (from the snow-covered area of a grid box) were spread evenly over the whole grid box. For example, if half the grid box were covered in snow with a water equivalent depth of 0.02m, this parameter would have a value of 0.01m. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Soil temperature level 1 K This parameter is the temperature of the soil at level 1 (in the middle of layer 1). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K This parameter is the temperature of the soil at level 2 (in the middle of layer 2). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 3 K This parameter is the temperature of the soil at level 3 (in the middle of layer 3). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 4 K This parameter is the temperature of the soil at level 4 (in the middle of layer 4). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil type Dimensionless This parameter is the texture (or classification) of soil used by the land surface scheme of the ECMWF Integrated Forecasting System (IFS) to predict the water holding capacity of soil in soil moisture and runoff calculations. It is derived from the root zone data (30-100 cm below the surface) of the FAO/UNESCO Digital Soil Map of the World, DSMW (FAO, 2003), which exists at a resolution of 5' X 5' (about 10 km). The seven soil types are: 1: Coarse, 2: Medium, 3: Medium fine, 4: Fine, 5: Very fine, 6: Organic, 7: Tropical organic. A value of 0 indicates a non-land point. This parameter does not vary in time. Standard deviation of filtered subgrid orography m Climatological parameter (scales between approximately 3 and 22 km are included). This parameter does not vary in time. Standard deviation of orography Dimensionless This parameter is one of four parameters (the others being angle of sub-gridscale orography, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the standard deviation of the height of the sub-grid valleys, hills and mountains within a grid box. This parameter does not vary in time. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface latent heat flux J m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation, clear sky J m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation, clear sky J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of land, sea and inland water. It is a measure of the weight of all the air in a column vertically above a point on the Earth's surface. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure weather systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa). Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface sensible heat flux J m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downward, clear sky J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downward, clear sky J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. TOA incident solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Temperature of snow layer K This parameter gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Top net solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation, clear sky J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation J m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation, clear sky J m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). Note that OLR is typically shown in units of watts per square metre (W m-2 ). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. Total cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights. Cloud fractions vary from 0 to 1. Total column cloud ice water kg m-2 This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud liquid water kg m-2 This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can also be referred to as total ozone, or vertically integrated ozone. The values are dominated by ozone within the stratosphere. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. In the IFS, the units for total ozone are kilograms per square metre, but before 12/06/2001 dobson units were used. Dobson units (DU) are still used extensively for total column ozone. 1 DU = 2.1415E-5 kg m-2 Total column rain water kg m-2 This parameter is the total amount of water in droplets of raindrop size (which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column snow water kg m-2 This parameter is the total amount of water in the form of snow (aggregated ice crystals which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column supercooled liquid water kg m-2 This parameter is the total amount of supercooled water in a column extending from the surface of the Earth to the top of the atmosphere. Supercooled water is water that exists in liquid form below 0oC. It is common in cold clouds and is important in the formation of precipitation. Also, supercooled water in clouds extending to the surface (i.e., fog) can cause icing/riming of various structures. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column water kg m-2 This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for. Total column water vapour kg m-2 This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Total precipitation m This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Total sky direct solar radiation at surface J m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Total totals index K This parameter gives an indication of the probability of occurrence of a thunderstorm and its severity by using the vertical gradient of temperature and humidity. The values of this index indicate the following: <44 Thunderstorms not likely, 44-50 Thunderstorms likely, 51-52 Isolated severe thunderstorms, 53-56 Widely scattered severe thunderstorms, 56-60 Scattered severe thunderstorms more likely. The total totals index is the temperature difference between 850 hPa (near surface) and 500 hPa (mid-troposphere) (lapse rate) plus a measure of the moisture content between 850 hPa and 500 hPa. The probability of deep convection tends to increase with increasing lapse rate and atmospheric moisture content. There are a number of limitations to this index. Also, the interpretation of the index value varies with season and location. Trapping layer base height m Trapping layer base height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer top height m Trapping layer top height as diagnosed from the vertical gradient of atmospheric refractivity. Type of high vegetation Dimensionless This parameter indicates the 6 types of high vegetation recognised by the ECMWF Integrated Forecasting System: 3 = Evergreen needleleaf trees, 4 = Deciduous needleleaf trees, 5 = Deciduous broadleaf trees, 6 = Evergreen broadleaf trees, 18 = Mixed forest/woodland, 19 = Interrupted forest. A value of 0 indicates a point without high vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of low vegetation Dimensionless This parameter indicates the 10 types of low vegetation recognised by the ECMWF Integrated Forecasting System: 1 = Crops, Mixed farming, 2 = Grass, 7 = Tall grass, 9 = Tundra, 10 = Irrigated crops, 11 = Semidesert, 13 = Bogs and marshes, 16 = Evergreen shrubs, 17 = Deciduous shrubs, 20 = Water and land mixtures. A value of 0 indicates a point without low vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. U-component stokes drift m s-1 This parameter is the eastward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. UV visible albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. This parameter varies between 0 and 1. UV visible albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. V-component stokes drift m s-1 This parameter is the northward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. Vertical integral of divergence of cloud frozen water flux kg m-2 s-1 The vertical integral of the cloud frozen water flux is the horizontal rate of flow of cloud frozen water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud frozen water spreading outward from a point, per square metre. This parameter is positive for cloud frozen water that is spreading out, or diverging, and negative for the opposite, for cloud frozen water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud frozen water. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of divergence of cloud liquid water flux kg m-2 s-1 The vertical integral of the cloud liquid water flux is the horizontal rate of flow of cloud liquid water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud liquid water spreading outward from a point, per square metre. This parameter is positive for cloud liquid water that is spreading out, or diverging, and negative for the opposite, for cloud liquid water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud liquid water. Vertical integral of divergence of geopotential flux W m-2 The vertical integral of the geopotential flux is the horizontal rate of flow of geopotential, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of geopotential spreading outward from a point, per square metre. This parameter is positive for geopotential that is spreading out, or diverging, and negative for the opposite, for geopotential that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of geopotential. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of kinetic energy flux W m-2 The vertical integral of the kinetic energy flux is the horizontal rate of flow of kinetic energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of kinetic energy spreading outward from a point, per square metre. This parameter is positive for kinetic energy that is spreading out, or diverging, and negative for the opposite, for kinetic energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of kinetic energy. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of mass flux kg m-2 s-1 The vertical integral of the mass flux is the horizontal rate of flow of mass, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of mass spreading outward from a point, per square metre. This parameter is positive for mass that is spreading out, or diverging, and negative for the opposite, for mass that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of mass. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of divergence of moisture flux kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Vertical integral of divergence of ozone flux kg m-2 s-1 The vertical integral of the ozone flux is the horizontal rate of flow of ozone, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of ozone spreading outward from a point, per square metre. This parameter is positive for ozone that is spreading out, or diverging, and negative for the opposite, for ozone that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of ozone. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of divergence of thermal energy flux W m-2 The vertical integral of the thermal energy flux is the horizontal rate of flow of thermal energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of thermal energy spreading outward from a point, per square metre. This parameter is positive for thermal energy that is spreading out, or diverging, and negative for the opposite, for thermal energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of thermal energy. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the flow of thermal energy through the climate system and to investigate the atmospheric energy budget. Vertical integral of divergence of total energy flux W m-2 The vertical integral of the total energy flux is the horizontal rate of flow of total energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of total energy spreading outward from a point, per square metre. This parameter is positive for total energy that is spreading out, or diverging, and negative for the opposite, for total energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of total energy. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of eastward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the eastward direction, per meter across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of eastward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from west to east. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of eastward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of energy conversion W m-2 This parameter is one contribution to the amount of energy being converted between kinetic energy, and internal plus potential energy, for a column of air extending from the surface of the Earth to the top of the atmosphere. Negative values indicate a conversion to kinetic energy from potential plus internal energy. This parameter can be used to study the atmospheric energy budget. The circulation of the atmosphere can also be considered in terms of energy conversions. Vertical integral of kinetic energy J m-2 This parameter is the vertical integral of kinetic energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of mass of atmosphere kg m-2 This parameter is the total mass of air for a column extending from the surface of the Earth to the top of the atmosphere, per square metre. This parameter is calculated by dividing surface pressure by the Earth's gravitational acceleration, g (=9.80665 m s-2 ), and has units of kilograms per square metre. This parameter can be used to study the atmospheric mass budget. Vertical integral of mass tendency kg m-2 s-1 This parameter is the rate of change of the mass of a column of air extending from the Earth's surface to the top of the atmosphere. An increasing mass of the column indicates rising surface pressure. In contrast, a decrease indicates a falling surface pressure. The mass of the column is calculated by dividing pressure at the Earth's surface by the gravitational acceleration, g (=9.80665 m s-2 ). This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of northward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from south to north. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of northward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of potential and internal energy J m-2 This parameter is the mass weighted vertical integral of potential and internal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential, internal and latent energy J m-2 This parameter is the mass weighted vertical integral of potential, internal and latent energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The latent energy refers to the energy associated with the water vapour in the atmosphere and is equal to the energy required to convert liquid water into water vapour. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of temperature K kg m-2 This parameter is the mass-weighted vertical integral of temperature for a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can be used to study the atmospheric energy budget. Vertical integral of thermal energy J m-2 This parameter is the mass-weighted vertical integral of thermal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Thermal energy is calculated from the product of temperature and the specific heat capacity of air at constant pressure. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of total energy J m-2 This parameter is the vertical integral of total energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertically integrated moisture divergence kg m-2 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm. Volumetric soil water layer 1 m3 m-3 This parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 This parameter is the volume of water in soil layer 2 (7 - 28cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 3 m3 m-3 This parameter is the volume of water in soil layer 3 (28 - 100cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 4 m3 m-3 This parameter is the volume of water in soil layer 4 (100 - 289cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Wave spectral directional width Dimensionless This parameter indicates whether waves (generated by local winds and associated with swell) are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for swell Dimensionless This parameter indicates whether waves associated with swell are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for wind waves Dimensionless This parameter indicates whether waves generated by the local wind are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral kurtosis Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It shows how much of the probability density function of the sea surface elevation exists in the tails of the distribution. So, a positive kurtosis (typical range 0.0 to 0.06) means more frequent occurrences of very extreme values (either above or below the mean), relative to a normal distribution. Wave spectral peakedness Dimensionless This parameter is a statistical measure used to forecast extreme or freak waves. It is a measure of the relative width of the ocean/sea wave frequency spectrum (i.e., whether the ocean/sea wave field is made up of a narrow or broad range of frequencies). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). When the wave field is more focussed around a narrow range of frequencies, the probability of freak/extreme waves increases. This parameter is Goda's peakedness factor and is used to calculate the Benjamin-Feir Index (BFI). The BFI is in turn used to estimate the probability and nature of extreme/freak waves. Wave spectral skewness Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It is a measure of the asymmetry of the probability density function of the sea surface elevation. So, a positive/negative skewness (typical range -0.2 to 0.12) means more frequent occurrences of extreme values above/below the mean, relative to a normal distribution. Zero degree level m The height above the Earth's surface where the temperature passes from positive to negative values, corresponding to the top of a warm layer, at the specified time. This parameter can be used to help forecast snow. If more than one warm layer is encountered, then the zero degree level corresponds to the top of the second atmospheric layer. This parameter is set to zero when the temperature in the whole atmosphere is below 0℃. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 100m u-component of wind m s-1 This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind. 100m u-component of wind m s-1 This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind. 100m v-component of wind m s-1 This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind. 100m v-component of wind m s-1 This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind. 10m u-component of neutral wind m s-1 This parameter is the eastward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m u-component of neutral wind m s-1 This parameter is the eastward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m u-component of wind m s-1 This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m u-component of wind m s-1 This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m v-component of neutral wind m s-1 This parameter is the northward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m v-component of neutral wind m s-1 This parameter is the northward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m v-component of wind m s-1 This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m v-component of wind m s-1 This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m wind gust since previous post-processing m s-1 Maximum 3 second wind at 10 m height as defined by WMO. Parametrization represents turbulence only before 01102008; thereafter effects of convection are included. The 3 s gust is computed every time step and and the maximum is kept since the last postprocessing. 10m wind gust since previous post-processing m s-1 Maximum 3 second wind at 10 m height as defined by WMO. Parametrization represents turbulence only before 01102008; thereafter effects of convection are included. The 3 s gust is computed every time step and and the maximum is kept since the last postprocessing. 2m dewpoint temperature K This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m dewpoint temperature K This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Air density over the oceans kg m-3 This parameter is the mass of air per cubic metre over the oceans, derived from the temperature, specific humidity and pressure at the lowest model level in the atmospheric model. This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Air density over the oceans kg m-3 This parameter is the mass of air per cubic metre over the oceans, derived from the temperature, specific humidity and pressure at the lowest model level in the atmospheric model. This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Angle of sub-gridscale orography radians This parameter is one of four parameters (the others being standard deviation, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. The angle of the sub-grid scale orography characterises the geographical orientation of the terrain in the horizontal plane (from a bird's-eye view) relative to an eastwards axis. This parameter does not vary in time. Angle of sub-gridscale orography radians This parameter is one of four parameters (the others being standard deviation, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. The angle of the sub-grid scale orography characterises the geographical orientation of the terrain in the horizontal plane (from a bird's-eye view) relative to an eastwards axis. This parameter does not vary in time. Anisotropy of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, slope and angle of sub-gridscale orography) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter is a measure of how much the shape of the terrain in the horizontal plane (from a bird's-eye view) is distorted from a circle. A value of one is a circle, less than one an ellipse, and 0 is a ridge. In the case of a ridge, wind blowing parallel to it does not exert any drag on the flow, but wind blowing perpendicular to it exerts the maximum drag. This parameter does not vary in time. Anisotropy of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, slope and angle of sub-gridscale orography) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter is a measure of how much the shape of the terrain in the horizontal plane (from a bird's-eye view) is distorted from a circle. A value of one is a circle, less than one an ellipse, and 0 is a ridge. In the case of a ridge, wind blowing parallel to it does not exert any drag on the flow, but wind blowing perpendicular to it exerts the maximum drag. This parameter does not vary in time. Benjamin-feir index Dimensionless This parameter is used to calculate the likelihood of freak ocean waves, which are waves that are higher than twice the mean height of the highest third of waves. Large values of this parameter (in practice of the order 1) indicate increased probability of the occurrence of freak waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived from the statistics of the two-dimensional wave spectrum. More precisely, it is the square of the ratio of the integral ocean wave steepness and the relative width of the frequency spectrum of the waves. Further information on the calculation of this parameter is given in Section 10.6 of the ECMWF Wave Model documentation. Benjamin-feir index Dimensionless This parameter is used to calculate the likelihood of freak ocean waves, which are waves that are higher than twice the mean height of the highest third of waves. Large values of this parameter (in practice of the order 1) indicate increased probability of the occurrence of freak waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived from the statistics of the two-dimensional wave spectrum. More precisely, it is the square of the ratio of the integral ocean wave steepness and the relative width of the frequency spectrum of the waves. Further information on the calculation of this parameter is given in Section 10.6 of the ECMWF Wave Model documentation. Boundary layer dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Boundary layer dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Boundary layer height m This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface. The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop. The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review. Boundary layer height m This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface. The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop. The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review. Charnock Dimensionless This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind. When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by the ECMWF Wave Model. Charnock Dimensionless This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind. When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by the ECMWF Wave Model. Clear-sky direct solar radiation at surface J m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Clear-sky direct solar radiation at surface J m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Cloud base height m The height above the Earth's surface of the base of the lowest cloud layer, at the specified time. This parameter is calculated by searching from the second lowest model level upwards, to the height of the level where cloud fraction becomes greater than 1% and condensate content greater than 1.E-6 kg kg-1. Fog (i.e., cloud in the lowest model layer) is not considered when defining cloud base height. Cloud base height m The height above the Earth's surface of the base of the lowest cloud layer, at the specified time. This parameter is calculated by searching from the second lowest model level upwards, to the height of the level where cloud fraction becomes greater than 1% and condensate content greater than 1.E-6 kg kg-1. Fog (i.e., cloud in the lowest model layer) is not considered when defining cloud base height. Coefficient of drag with waves Dimensionless This parameter is the resistance that ocean waves exert on the atmosphere. It is sometimes also called a "friction coefficient". It is calculated by the wave model as the ratio of the square of the friction velocity, to the square of the neutral wind speed at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. Coefficient of drag with waves Dimensionless This parameter is the resistance that ocean waves exert on the atmosphere. It is sometimes also called a "friction coefficient". It is calculated by the wave model as the ratio of the square of the friction velocity, to the square of the neutral wind speed at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. Convective available potential energy J kg-1 This is an indication of the instability (or stability) of the atmosphere and can be used to assess the potential for the development of convection, which can lead to heavy rainfall, thunderstorms and other severe weather. In the ECMWF Integrated Forecasting System (IFS), CAPE is calculated by considering parcels of air departing at different model levels below the 350 hPa level. If a parcel of air is more buoyant (warmer and/or with more moisture) than its surrounding environment, it will continue to rise (cooling as it rises) until it reaches a point where it no longer has positive buoyancy. CAPE is the potential energy represented by the total excess buoyancy. The maximum CAPE produced by the different parcels is the value retained. Large positive values of CAPE indicate that an air parcel would be much warmer than its surrounding environment and therefore, very buoyant. CAPE is related to the maximum potential vertical velocity of air within an updraft; thus, higher values indicate greater potential for severe weather. Observed values in thunderstorm environments often may exceed 1000 joules per kilogram (J kg-1), and in extreme cases may exceed 5000 J kg-1. The calculation of this parameter assumes: (i) the parcel of air does not mix with surrounding air; (ii) ascent is pseudo-adiabatic (all condensed water falls out) and (iii) other simplifications related to the mixed-phase condensational heating. Convective available potential energy J kg-1 This is an indication of the instability (or stability) of the atmosphere and can be used to assess the potential for the development of convection, which can lead to heavy rainfall, thunderstorms and other severe weather. In the ECMWF Integrated Forecasting System (IFS), CAPE is calculated by considering parcels of air departing at different model levels below the 350 hPa level. If a parcel of air is more buoyant (warmer and/or with more moisture) than its surrounding environment, it will continue to rise (cooling as it rises) until it reaches a point where it no longer has positive buoyancy. CAPE is the potential energy represented by the total excess buoyancy. The maximum CAPE produced by the different parcels is the value retained. Large positive values of CAPE indicate that an air parcel would be much warmer than its surrounding environment and therefore, very buoyant. CAPE is related to the maximum potential vertical velocity of air within an updraft; thus, higher values indicate greater potential for severe weather. Observed values in thunderstorm environments often may exceed 1000 joules per kilogram (J kg-1), and in extreme cases may exceed 5000 J kg-1. The calculation of this parameter assumes: (i) the parcel of air does not mix with surrounding air; (ii) ascent is pseudo-adiabatic (all condensed water falls out) and (iii) other simplifications related to the mixed-phase condensational heating. Convective inhibition J kg-1 This parameter is a measure of the amount of energy required for convection to commence. If the value of this parameter is too high, then deep, moist convection is unlikely to occur even if the convective available potential energy or convective available potential energy shear are large. CIN values greater than 200 J kg-1 would be considered high. An atmospheric layer where temperature increases with height (known as a temperature inversion) would inhibit convective uplift and is a situation in which convective inhibition would be large. Convective inhibition J kg-1 This parameter is a measure of the amount of energy required for convection to commence. If the value of this parameter is too high, then deep, moist convection is unlikely to occur even if the convective available potential energy or convective available potential energy shear are large. CIN values greater than 200 J kg-1 would be considered high. An atmospheric layer where temperature increases with height (known as a temperature inversion) would inhibit convective uplift and is a situation in which convective inhibition would be large. Convective precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Rainfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Rainfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Downward UV radiation at the surface J m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Downward UV radiation at the surface J m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Duct base height m Duct base height as diagnosed from the vertical gradient of atmospheric refractivity. Duct base height m Duct base height as diagnosed from the vertical gradient of atmospheric refractivity. Eastward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Eastward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Eastward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Eastward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Forecast albedo Dimensionless This parameter is a measure of the reflectivity of the Earth's surface. It is the fraction of short-wave (solar) radiation reflected by the Earth's surface, for diffuse radiation, assuming a fixed spectrum of downward short-wave radiation at the surface. The values of this parameter vary between zero and one. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Short-wave radiation from the Sun is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Forecast albedo Dimensionless This parameter is a measure of the reflectivity of the Earth's surface. It is the fraction of short-wave (solar) radiation reflected by the Earth's surface, for diffuse radiation, assuming a fixed spectrum of downward short-wave radiation at the surface. The values of this parameter vary between zero and one. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Short-wave radiation from the Sun is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Forecast logarithm of surface roughness for heat Dimensionless This parameter is the natural logarithm of the roughness length for heat. The surface roughness for heat is a measure of the surface resistance to heat transfer. This parameter is used to determine the air to surface transfer of heat. For given atmospheric conditions, a higher surface roughness for heat means that it is more difficult for the air to exchange heat with the surface. A lower surface roughness for heat means that it is easier for the air to exchange heat with the surface. Over the ocean, surface roughness for heat depends on the waves. Over sea-ice, it has a constant value of 0.001 m. Over land, it is derived from the vegetation type and snow cover. Forecast logarithm of surface roughness for heat Dimensionless This parameter is the natural logarithm of the roughness length for heat. The surface roughness for heat is a measure of the surface resistance to heat transfer. This parameter is used to determine the air to surface transfer of heat. For given atmospheric conditions, a higher surface roughness for heat means that it is more difficult for the air to exchange heat with the surface. A lower surface roughness for heat means that it is easier for the air to exchange heat with the surface. Over the ocean, surface roughness for heat depends on the waves. Over sea-ice, it has a constant value of 0.001 m. Over land, it is derived from the vegetation type and snow cover. Forecast surface roughness m This parameter is the aerodynamic roughness length in metres. It is a measure of the surface resistance. This parameter is used to determine the air to surface transfer of momentum. For given atmospheric conditions, a higher surface roughness causes a slower near-surface wind speed. Over ocean, surface roughness depends on the waves. Over land, surface roughness is derived from the vegetation type and snow cover. Forecast surface roughness m This parameter is the aerodynamic roughness length in metres. It is a measure of the surface resistance. This parameter is used to determine the air to surface transfer of momentum. For given atmospheric conditions, a higher surface roughness causes a slower near-surface wind speed. Over ocean, surface roughness depends on the waves. Over land, surface roughness is derived from the vegetation type and snow cover. Free convective velocity over the oceans m s-1 This parameter is an estimate of the vertical velocity of updraughts generated by free convection. Free convection is fluid motion induced by buoyancy forces, which are driven by density gradients. The free convective velocity is used to estimate the impact of wind gusts on ocean wave growth. It is calculated at the height of the lowest temperature inversion (the height above the surface of the Earth where the temperature increases with height). This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Free convective velocity over the oceans m s-1 This parameter is an estimate of the vertical velocity of updraughts generated by free convection. Free convection is fluid motion induced by buoyancy forces, which are driven by density gradients. The free convective velocity is used to estimate the impact of wind gusts on ocean wave growth. It is calculated at the height of the lowest temperature inversion (the height above the surface of the Earth where the temperature increases with height). This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Friction velocity m s-1 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is a theoretical wind speed at the Earth's surface that expresses the magnitude of stress. It is calculated by dividing the surface stress by air density and taking its square root. For turbulent flow, the friction velocity is approximately constant in the lowest few metres of the atmosphere. This parameter increases with the roughness of the surface. It is used to calculate the way wind changes with height in the lowest levels of the atmosphere. Friction velocity m s-1 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is a theoretical wind speed at the Earth's surface that expresses the magnitude of stress. It is calculated by dividing the surface stress by air density and taking its square root. For turbulent flow, the friction velocity is approximately constant in the lowest few metres of the atmosphere. This parameter increases with the roughness of the surface. It is used to calculate the way wind changes with height in the lowest levels of the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location at the surface of the Earth, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The (surface) geopotential height (orography) can be calculated by dividing the (surface) geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2 ). This parameter does not vary in time. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location at the surface of the Earth, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The (surface) geopotential height (orography) can be calculated by dividing the (surface) geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2 ). This parameter does not vary in time. Gravity wave dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Gravity wave dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. High cloud cover Dimensionless The proportion of a grid box covered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a "standard atmosphere")). The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. High cloud cover Dimensionless The proportion of a grid box covered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a "standard atmosphere")). The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. High vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "high". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. High vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "high". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Ice temperature layer 1 K This parameter is the sea-ice temperature in layer 1 (0 to 7cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 1 K This parameter is the sea-ice temperature in layer 1 (0 to 7cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 2 K This parameter is the sea-ice temperature in layer 2 (7 to 28cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 2 K This parameter is the sea-ice temperature in layer 2 (7 to 28cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 3 K This parameter is the sea-ice temperature in layer 3 (28 to 100cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 3 K This parameter is the sea-ice temperature in layer 3 (28 to 100cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 4 K This parameter is the sea-ice temperature in layer 4 (100 to 150cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 4 K This parameter is the sea-ice temperature in layer 4 (100 to 150cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Instantaneous 10m wind gust m s-1 This parameter is the maximum wind gust at the specified time, at a height of ten metres above the surface of the Earth. The WMO defines a wind gust as the maximum of the wind averaged over 3 second intervals. This duration is shorter than a model time step, and so the ECMWF Integrated Forecasting System (IFS) deduces the magnitude of a gust within each time step from the time-step-averaged surface stress, surface friction, wind shear and stability. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Instantaneous 10m wind gust m s-1 This parameter is the maximum wind gust at the specified time, at a height of ten metres above the surface of the Earth. The WMO defines a wind gust as the maximum of the wind averaged over 3 second intervals. This duration is shorter than a model time step, and so the ECMWF Integrated Forecasting System (IFS) deduces the magnitude of a gust within each time step from the time-step-averaged surface stress, surface friction, wind shear and stability. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Instantaneous eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. Instantaneous eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. Instantaneous large-scale surface precipitation fraction Dimensionless This parameter is the fraction of the grid box (0-1) covered by large-scale precipitation at the specified time. Large-scale precipitation is rain and snow that falls to the Earth's surface, and is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. Precipitation can also be due to convection generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box. Instantaneous large-scale surface precipitation fraction Dimensionless This parameter is the fraction of the grid box (0-1) covered by large-scale precipitation at the specified time. Large-scale precipitation is rain and snow that falls to the Earth's surface, and is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. Precipitation can also be due to convection generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box. Instantaneous moisture flux kg m-2 s-1 This parameter is the net rate of moisture exchange between the land/ocean surface and the atmosphere, due to the processes of evaporation (including evapotranspiration) and condensation, at the specified time. By convention, downward fluxes are positive, which means that evaporation is represented by negative values and condensation by positive values. Instantaneous moisture flux kg m-2 s-1 This parameter is the net rate of moisture exchange between the land/ocean surface and the atmosphere, due to the processes of evaporation (including evapotranspiration) and condensation, at the specified time. By convention, downward fluxes are positive, which means that evaporation is represented by negative values and condensation by positive values. Instantaneous northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. Instantaneous northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. Instantaneous surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere, at the specified time, through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. The ECMWF convention for vertical fluxes is positive downwards. Instantaneous surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere, at the specified time, through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. The ECMWF convention for vertical fluxes is positive downwards. K index K This parameter is a measure of the potential for a thunderstorm to develop, calculated from the temperature and dew point temperature in the lower part of the atmosphere. The calculation uses the temperature at 850, 700 and 500 hPa and dewpoint temperature at 850 and 700 hPa. Higher values of K indicate a higher potential for the development of thunderstorms. This parameter is related to the probability of occurrence of a thunderstorm: <20 K No thunderstorm, 20-25 K Isolated thunderstorms, 26-30 K Widely scattered thunderstorms, 31-35 K Scattered thunderstorms, >35 K Numerous thunderstorms. K index K This parameter is a measure of the potential for a thunderstorm to develop, calculated from the temperature and dew point temperature in the lower part of the atmosphere. The calculation uses the temperature at 850, 700 and 500 hPa and dewpoint temperature at 850 and 700 hPa. Higher values of K indicate a higher potential for the development of thunderstorms. This parameter is related to the probability of occurrence of a thunderstorm: <20 K No thunderstorm, 20-25 K Isolated thunderstorms, 26-30 K Widely scattered thunderstorms, 31-35 K Scattered thunderstorms, >35 K Numerous thunderstorms. Lake bottom temperature K This parameter is the temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake bottom temperature K This parameter is the temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake cover Dimensionless This parameter is the proportion of a grid box covered by inland water bodies (lakes, reservoirs, rivers and coastal waters). Values vary between 0: no inland water, and 1: grid box is fully covered with inland water. This parameter is specified from observations and does not vary in time. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake cover Dimensionless This parameter is the proportion of a grid box covered by inland water bodies (lakes, reservoirs, rivers and coastal waters). Values vary between 0: no inland water, and 1: grid box is fully covered with inland water. This parameter is specified from observations and does not vary in time. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth m This parameter is the mean depth of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is specified from in-situ measurements and indirect estimates and does not vary in time. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth m This parameter is the mean depth of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is specified from in-situ measurements and indirect estimates and does not vary in time. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake ice depth m This parameter is the thickness of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter is the thickness of that ice layer. Lake ice depth m This parameter is the thickness of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter is the thickness of that ice layer. Lake ice temperature K This parameter is the temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). It is the temperature at the ice/atmosphere or ice/snow interface. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake ice temperature K This parameter is the temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). It is the temperature at the ice/atmosphere or ice/snow interface. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer depth m This parameter is the thickness of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake mix-layer depth m This parameter is the thickness of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake mix-layer temperature K This parameter is the temperature of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer temperature K This parameter is the temperature of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake shape factor Dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) i.e., it describes the shape of the vertical temperature profile. It is used to calculate the lake bottom temperature and other lake-related parameters. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake shape factor Dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) i.e., it describes the shape of the vertical temperature profile. It is used to calculate the lake bottom temperature and other lake-related parameters. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake total layer temperature K This parameter is the mean temperature of the total water column in inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. This parameter is the mean temperature over the two layers. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake total layer temperature K This parameter is the mean temperature of the total water column in inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. This parameter is the mean temperature over the two layers. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Land-sea mask Dimensionless This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless. In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water. In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water. This parameter does not vary in time. Land-sea mask Dimensionless This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless. In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water. In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water. This parameter does not vary in time. Large scale rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Rainfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Rainfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation fraction s This parameter is the accumulation of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Large-scale precipitation fraction s This parameter is the accumulation of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Large-scale snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Leaf area index, high vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "high". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Leaf area index, high vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "high". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Leaf area index, low vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "low". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Leaf area index, low vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "low". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Low cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a "standard atmosphere")). Assumptions are made about the degree of overlap/randomness between clouds in different model levels. This parameter has values from 0 to 1. Low cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a "standard atmosphere")). Assumptions are made about the degree of overlap/randomness between clouds in different model levels. This parameter has values from 0 to 1. Low vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "low". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Low vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "low". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Maximum 2m temperature since previous post-processing K This parameter is the highest temperature of air at 2m above the surface of land, sea or inland water since the parameter was last archived in a particular forecast. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Maximum 2m temperature since previous post-processing K This parameter is the highest temperature of air at 2m above the surface of land, sea or inland water since the parameter was last archived in a particular forecast. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Maximum individual wave height m This parameter is an estimate of the height of the expected highest individual wave within a 20 minute time window. It can be used as a guide to the likelihood of extreme or freak waves. The interactions between waves are non-linear and occasionally concentrate wave energy giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered as a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Maximum individual wave height m This parameter is an estimate of the height of the expected highest individual wave within a 20 minute time window. It can be used as a guide to the likelihood of extreme or freak waves. The interactions between waves are non-linear and occasionally concentrate wave energy giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered as a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Maximum total precipitation rate since previous post-processing kg m-2 s-1 The total precipitation is calculated from the combined large-scale and convective rainfall and snowfall rates every time step and the maximum is kept since the last postprocessing. Maximum total precipitation rate since previous post-processing kg m-2 s-1 The total precipitation is calculated from the combined large-scale and convective rainfall and snowfall rates every time step and the maximum is kept since the last postprocessing. Mean boundary layer dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean boundary layer dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean convective precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean direction of total swell degrees This parameter is the mean direction of waves associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of total swell degrees This parameter is the mean direction of waves associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of wind waves degrees The mean direction of waves generated by local winds. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea wave spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of wind waves degrees The mean direction of waves generated by local winds. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea wave spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean eastward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean eastward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean evaporation rate kg m-2 s-1 This parameter is the amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Mean evaporation rate kg m-2 s-1 This parameter is the amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Mean gravity wave dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean gravity wave dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean large-scale precipitation fraction Dimensionless This parameter is the mean of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean large-scale precipitation fraction Dimensionless This parameter is the mean of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean large-scale precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean northward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean northward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean period of total swell s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea associated with swell, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. Mean period of total swell s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea associated with swell, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. Mean period of wind waves s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea generated by local winds, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea spectrum. Mean period of wind waves s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea generated by local winds, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea spectrum. Mean potential evaporation rate kg m-2 s-1 This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean potential evaporation rate kg m-2 s-1 This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean sea level pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of mean sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at mean sea level. It is calculated over all surfaces - land, sea and inland water. Maps of mean sea level pressure are used to identify the locations of low and high pressure weather systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds. The units of this parameter are pascals (Pa). Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Mean sea level pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of mean sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at mean sea level. It is calculated over all surfaces - land, sea and inland water. Maps of mean sea level pressure are used to identify the locations of low and high pressure weather systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds. The units of this parameter are pascals (Pa). Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Mean snow evaporation rate kg m-2 s-1 This parameter is the average rate of snow evaporation from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snow evaporation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Mean snow evaporation rate kg m-2 s-1 This parameter is the average rate of snow evaporation from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snow evaporation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Mean snowfall rate kg m-2 s-1 This parameter is the rate of snowfall at the Earth's surface. It is the sum of large-scale and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean snowfall rate kg m-2 s-1 This parameter is the rate of snowfall at the Earth's surface. It is the sum of large-scale and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean snowmelt rate kg m-2 s-1 This parameter is the rate of snow melt in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the melting would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean snowmelt rate kg m-2 s-1 This parameter is the rate of snow melt in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the melting would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean square slope of waves Dimensionless This parameter can be related analytically to the average slope of combined wind-sea and swell waves. It can also be expressed as a function of wind speed under some statistical assumptions. The higher the slope, the steeper the waves. This parameter indicates the roughness of the sea/ocean surface which affects the interaction between ocean and atmosphere. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. Mean square slope of waves Dimensionless This parameter can be related analytically to the average slope of combined wind-sea and swell waves. It can also be expressed as a function of wind speed under some statistical assumptions. The higher the slope, the steeper the waves. This parameter indicates the roughness of the sea/ocean surface which affects the interaction between ocean and atmosphere. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. Mean sub-surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean sub-surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface direct short-wave radiation flux W m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux W m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux, clear sky W m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux, clear sky W m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward UV radiation flux W m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward UV radiation flux W m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux, clear sky W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux, clear sky W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface latent heat flux W m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface latent heat flux W m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux, clear sky W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux, clear sky W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top downward short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top downward short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux W m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux W m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux, clear sky W m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean top net long-wave radiation flux, clear sky W m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean top net short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux, clear sky W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux, clear sky W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean total precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface. It is the sum of the rates due to large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean total precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface. It is the sum of the rates due to large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean vertical gradient of refractivity inside trapping layer m-1 Mean vertical gradient of atmospheric refractivity inside the trapping layer. Mean vertical gradient of refractivity inside trapping layer m-1 Mean vertical gradient of atmospheric refractivity inside the trapping layer. Mean vertically integrated moisture divergence kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean vertically integrated moisture divergence kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean wave direction degree true This parameter is the mean direction of ocean/sea surface waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use this type of wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction degree true This parameter is the mean direction of ocean/sea surface waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use this type of wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of first swell partition degrees This parameter is the mean direction of waves in the first swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of first swell partition degrees This parameter is the mean direction of waves in the first swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of second swell partition degrees This parameter is the mean direction of waves in the second swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of second swell partition degrees This parameter is the mean direction of waves in the second swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of third swell partition degrees This parameter is the mean direction of waves in the third swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of third swell partition degrees This parameter is the mean direction of waves in the third swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave period s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use such wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. Mean wave period s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use such wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. Mean wave period based on first moment s This parameter is the reciprocal of the mean frequency of the wave components that represent the sea state. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment s This parameter is the reciprocal of the mean frequency of the wave components that represent the sea state. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for swell s This parameter is the reciprocal of the mean frequency of the wave components associated with swell. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for swell s This parameter is the reciprocal of the mean frequency of the wave components associated with swell. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for wind waves s This parameter is the reciprocal of the mean frequency of the wave components generated by local winds. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with wind waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for wind waves s This parameter is the reciprocal of the mean frequency of the wave components generated by local winds. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with wind waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for swell s This parameter is equivalent to the zero-crossing mean wave period for swell. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for swell s This parameter is equivalent to the zero-crossing mean wave period for swell. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for wind waves s This parameter is equivalent to the zero-crossing mean wave period for waves generated by local winds. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for wind waves s This parameter is equivalent to the zero-crossing mean wave period for waves generated by local winds. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period of first swell partition s This parameter is the mean period of waves in the first swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of first swell partition s This parameter is the mean period of waves in the first swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of second swell partition s This parameter is the mean period of waves in the second swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of second swell partition s This parameter is the mean period of waves in the second swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of third swell partition s This parameter is the mean period of waves in the third swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of third swell partition s This parameter is the mean period of waves in the third swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third swell partition might be from one system at one location and a different system at the neighbouring location). Mean zero-crossing wave period s This parameter represents the mean length of time between occasions where the sea/ocean surface crosses mean sea level. In combination with wave height information, it could be used to assess the length of time that a coastal structure might be under water, for example. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). In the ECMWF Integrated Forecasting System (IFS) this parameter is calculated from the characteristics of the two-dimensional wave spectrum. Mean zero-crossing wave period s This parameter represents the mean length of time between occasions where the sea/ocean surface crosses mean sea level. In combination with wave height information, it could be used to assess the length of time that a coastal structure might be under water, for example. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). In the ECMWF Integrated Forecasting System (IFS) this parameter is calculated from the characteristics of the two-dimensional wave spectrum. Medium cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a "standard atmosphere")). The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. Medium cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a "standard atmosphere")). The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. Minimum 2m temperature since previous post-processing K This parameter is the lowest temperature of air at 2m above the surface of land, sea or inland waters since the parameter was last archived in a particular forecast. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. See further information. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Minimum 2m temperature since previous post-processing K This parameter is the lowest temperature of air at 2m above the surface of land, sea or inland waters since the parameter was last archived in a particular forecast. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. See further information. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Minimum total precipitation rate since previous post-processing kg m-2 s-1 The total precipitation is calculated from the combined large-scale and convective rainfall and snowfall rates every time step and the minimum is kept since the last postprocessing. Minimum total precipitation rate since previous post-processing kg m-2 s-1 The total precipitation is calculated from the combined large-scale and convective rainfall and snowfall rates every time step and the minimum is kept since the last postprocessing. Minimum vertical gradient of refractivity inside trapping layer m-1 Minimum vertical gradient of atmospheric refractivity inside the trapping layer. Minimum vertical gradient of refractivity inside trapping layer m-1 Minimum vertical gradient of atmospheric refractivity inside the trapping layer. Model bathymetry m This parameter is the depth of water from the surface to the bottom of the ocean. It is used by the ocean wave model to specify the propagation properties of the different waves that could be present. Note that the ocean wave model grid is too coarse to resolve some small islands and mountains on the bottom of the ocean, but they can have an impact on surface ocean waves. The ocean wave model has been modified to reduce the wave energy flowing around or over features at spatial scales smaller than the grid box. Model bathymetry m This parameter is the depth of water from the surface to the bottom of the ocean. It is used by the ocean wave model to specify the propagation properties of the different waves that could be present. Note that the ocean wave model grid is too coarse to resolve some small islands and mountains on the bottom of the ocean, but they can have an impact on surface ocean waves. The ocean wave model has been modified to reduce the wave energy flowing around or over features at spatial scales smaller than the grid box. Near IR albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Normalized energy flux into ocean Dimensionless This parameter is the normalised vertical flux of turbulent kinetic energy from ocean waves into the ocean. The energy flux is calculated from an estimation of the loss of wave energy due to white capping waves. A white capping wave is one that appears white at its crest as it breaks, due to air being mixed into the water. When waves break in this way, there is a transfer of energy from the waves to the ocean. Such a flux is defined to be negative. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into ocean Dimensionless This parameter is the normalised vertical flux of turbulent kinetic energy from ocean waves into the ocean. The energy flux is calculated from an estimation of the loss of wave energy due to white capping waves. A white capping wave is one that appears white at its crest as it breaks, due to air being mixed into the water. When waves break in this way, there is a transfer of energy from the waves to the ocean. Such a flux is defined to be negative. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into waves Dimensionless This parameter is the normalised vertical flux of energy from wind into the ocean waves. A positive flux implies a flux into the waves. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into waves Dimensionless This parameter is the normalised vertical flux of energy from wind into the ocean waves. A positive flux implies a flux into the waves. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized stress into ocean Dimensionless This parameter is the normalised surface stress, or momentum flux, from the air into the ocean due to turbulence at the air-sea interface and breaking waves. It does not include the flux used to generate waves. The ECMWF convention for vertical fluxes is positive downwards. The stress has units of Newtons per metre squared, and this is normalised by being divided by the product of air density and the square of the friction velocity. Normalized stress into ocean Dimensionless This parameter is the normalised surface stress, or momentum flux, from the air into the ocean due to turbulence at the air-sea interface and breaking waves. It does not include the flux used to generate waves. The ECMWF convention for vertical fluxes is positive downwards. The stress has units of Newtons per metre squared, and this is normalised by being divided by the product of air density and the square of the friction velocity. Northward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Northward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Northward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Northward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Ocean surface stress equivalent 10m neutral wind direction degrees This parameter is the direction from which the "neutral wind" blows, in degrees clockwise from true north, at a height of ten metres above the surface of the Earth. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind direction used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind direction degrees This parameter is the direction from which the "neutral wind" blows, in degrees clockwise from true north, at a height of ten metres above the surface of the Earth. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind direction used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind speed m s-1 This parameter is the horizontal speed of the "neutral wind", at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind speed used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind speed m s-1 This parameter is the horizontal speed of the "neutral wind", at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind speed used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Period corresponding to maximum individual wave height s This parameter is the period of the expected highest individual wave within a 20-minute time window. It can be used as a guide to the characteristics of extreme or freak waves. Wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. Occasionally waves of different periods reinforce and interact non-linearly giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered to be a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Period corresponding to maximum individual wave height s This parameter is the period of the expected highest individual wave within a 20-minute time window. It can be used as a guide to the characteristics of extreme or freak waves. Wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. Occasionally waves of different periods reinforce and interact non-linearly giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered to be a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Potential evaporation m This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Potential evaporation m This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Precipitation type Dimensionless This parameter describes the type of precipitation at the surface, at the specified time. A precipitation type is assigned wherever there is a non-zero value of precipitation. In the ECMWF Integrated Forecasting System (IFS) there are only two predicted precipitation variables: rain and snow. Precipitation type is derived from these two predicted variables in combination with atmospheric conditions, such as temperature. Values of precipitation type defined in the IFS: 0: No precipitation, 1: Rain, 3: Freezing rain (i.e. supercooled raindrops which freeze on contact with the ground and other surfaces), 5: Snow, 6: Wet snow (i.e. snow particles which are starting to melt); 7: Mixture of rain and snow, 8: Ice pellets. These precipitation types are consistent with WMO Code Table 4.201. Other types in this WMO table are not defined in the IFS. Precipitation type Dimensionless This parameter describes the type of precipitation at the surface, at the specified time. A precipitation type is assigned wherever there is a non-zero value of precipitation. In the ECMWF Integrated Forecasting System (IFS) there are only two predicted precipitation variables: rain and snow. Precipitation type is derived from these two predicted variables in combination with atmospheric conditions, such as temperature. Values of precipitation type defined in the IFS: 0: No precipitation, 1: Rain, 3: Freezing rain (i.e. supercooled raindrops which freeze on contact with the ground and other surfaces), 5: Snow, 6: Wet snow (i.e. snow particles which are starting to melt); 7: Mixture of rain and snow, 8: Ice pellets. These precipitation types are consistent with WMO Code Table 4.201. Other types in this WMO table are not defined in the IFS. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Sea surface temperature K This parameter (SST) is the temperature of sea water near the surface. In ERA5, this parameter is a foundation SST, which means there are no variations due to the daily cycle of the sun (diurnal variations). SST, in ERA5, is given by two external providers. Before September 2007, SST from the HadISST2 dataset is used and from September 2007 onwards, the OSTIA dataset is used. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Sea surface temperature K This parameter (SST) is the temperature of sea water near the surface. In ERA5, this parameter is a foundation SST, which means there are no variations due to the daily cycle of the sun (diurnal variations). SST, in ERA5, is given by two external providers. Before September 2007, SST from the HadISST2 dataset is used and from September 2007 onwards, the OSTIA dataset is used. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Sea-ice cover Dimensionless This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land-sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover. In ERA5, sea-ice cover is given by two external providers. Before 1979 the HadISST2 dataset is used. From 1979 to August 2007 the OSI SAF (409a) dataset is used and from September 2007 the OSI SAF oper dataset is used. Sea ice is frozen sea water which floats on the surface of the ocean. Sea ice does not include ice which forms on land such as glaciers, icebergs and ice-sheets. It also excludes ice shelves which are anchored on land, but protrude out over the surface of the ocean. These phenomena are not modelled by the IFS. Long-term monitoring of sea ice is important for understanding climate change. Sea ice also affects shipping routes through the polar regions. Sea-ice cover Dimensionless This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land-sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover. In ERA5, sea-ice cover is given by two external providers. Before 1979 the HadISST2 dataset is used. From 1979 to August 2007 the OSI SAF (409a) dataset is used and from September 2007 the OSI SAF oper dataset is used. Sea ice is frozen sea water which floats on the surface of the ocean. Sea ice does not include ice which forms on land such as glaciers, icebergs and ice-sheets. It also excludes ice shelves which are anchored on land, but protrude out over the surface of the ocean. These phenomena are not modelled by the IFS. Long-term monitoring of sea ice is important for understanding climate change. Sea ice also affects shipping routes through the polar regions. Significant height of combined wind waves and swell m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. This parameter can be used to assess sea state and swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of combined wind waves and swell m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. This parameter can be used to assess sea state and swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of total swell m This parameter represents the average height of the highest third of surface ocean/sea waves associated with swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of total swell only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional total swell spectrum. The total swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of total swell m This parameter represents the average height of the highest third of surface ocean/sea waves associated with swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of total swell only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional total swell spectrum. The total swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of wind waves m This parameter represents the average height of the highest third of surface ocean/sea waves generated by the local wind. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wind-sea wave spectrum. The wind-sea wave spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are still under the influence of the local wind. This parameter can be used to assess wind-sea waves. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of wind waves m This parameter represents the average height of the highest third of surface ocean/sea waves generated by the local wind. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wind-sea wave spectrum. The wind-sea wave spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are still under the influence of the local wind. This parameter can be used to assess wind-sea waves. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of first swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the first swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of first swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the first swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of second swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the second swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of second swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the second swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of third swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the third swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of third swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the third swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Skin reservoir content m of water equivalent This parameter is the amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of "skin reservoir content" a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the "skin reservoir" by evaporation. Skin reservoir content m of water equivalent This parameter is the amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of "skin reservoir content" a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the "skin reservoir" by evaporation. Skin temperature K This parameter is the temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Skin temperature K This parameter is the temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Slope of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, angle and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the slope of the sub-grid valleys, hills and mountains. A flat surface has a value of 0, and a 45 degree slope has a value of 0.5. This parameter does not vary in time. Slope of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, angle and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the slope of the sub-grid valleys, hills and mountains. A flat surface has a value of 0, and a 45 degree slope has a value of 0.5. This parameter does not vary in time. Snow albedo Dimensionless This parameter is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter changes with snow age and also depends on vegetation height. It has a range of values between 0 and 1. For low vegetation, it ranges between 0.52 for old snow and 0.88 for fresh snow. For high vegetation with snow underneath, it depends on vegetation type and has values between 0.27 and 0.38. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. Snow albedo Dimensionless This parameter is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter changes with snow age and also depends on vegetation height. It has a range of values between 0 and 1. For low vegetation, it ranges between 0.52 for old snow and 0.88 for fresh snow. For high vegetation with snow underneath, it depends on vegetation type and has values between 0.27 and 0.38. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. Snow density kg m-3 This parameter is the mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. Snow density kg m-3 This parameter is the mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. Snow depth m of water equivalent This parameter is the amount of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow depth m of water equivalent This parameter is the amount of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from snow from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the evaporated snow (from the snow-covered area of a grid box) were liquid and were spread evenly over the whole grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Snow evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from snow from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the evaporated snow (from the snow-covered area of a grid box) were liquid and were spread evenly over the whole grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Snowmelt m of water equivalent This parameter is the accumulated amount of water that has melted from snow in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the melted snow (from the snow-covered area of a grid box) were spread evenly over the whole grid box. For example, if half the grid box were covered in snow with a water equivalent depth of 0.02m, this parameter would have a value of 0.01m. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Snowmelt m of water equivalent This parameter is the accumulated amount of water that has melted from snow in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the melted snow (from the snow-covered area of a grid box) were spread evenly over the whole grid box. For example, if half the grid box were covered in snow with a water equivalent depth of 0.02m, this parameter would have a value of 0.01m. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Soil temperature level 1 K This parameter is the temperature of the soil at level 1 (in the middle of layer 1). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 1 K This parameter is the temperature of the soil at level 1 (in the middle of layer 1). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K This parameter is the temperature of the soil at level 2 (in the middle of layer 2). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K This parameter is the temperature of the soil at level 2 (in the middle of layer 2). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 3 K This parameter is the temperature of the soil at level 3 (in the middle of layer 3). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 3 K This parameter is the temperature of the soil at level 3 (in the middle of layer 3). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 4 K This parameter is the temperature of the soil at level 4 (in the middle of layer 4). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 4 K This parameter is the temperature of the soil at level 4 (in the middle of layer 4). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil type Dimensionless This parameter is the texture (or classification) of soil used by the land surface scheme of the ECMWF Integrated Forecasting System (IFS) to predict the water holding capacity of soil in soil moisture and runoff calculations. It is derived from the root zone data (30-100 cm below the surface) of the FAO/UNESCO Digital Soil Map of the World, DSMW (FAO, 2003), which exists at a resolution of 5' X 5' (about 10 km). The seven soil types are: 1: Coarse, 2: Medium, 3: Medium fine, 4: Fine, 5: Very fine, 6: Organic, 7: Tropical organic. A value of 0 indicates a non-land point. This parameter does not vary in time. Soil type Dimensionless This parameter is the texture (or classification) of soil used by the land surface scheme of the ECMWF Integrated Forecasting System (IFS) to predict the water holding capacity of soil in soil moisture and runoff calculations. It is derived from the root zone data (30-100 cm below the surface) of the FAO/UNESCO Digital Soil Map of the World, DSMW (FAO, 2003), which exists at a resolution of 5' X 5' (about 10 km). The seven soil types are: 1: Coarse, 2: Medium, 3: Medium fine, 4: Fine, 5: Very fine, 6: Organic, 7: Tropical organic. A value of 0 indicates a non-land point. This parameter does not vary in time. Standard deviation of filtered subgrid orography m Climatological parameter (scales between approximately 3 and 22 km are included). This parameter does not vary in time. Standard deviation of filtered subgrid orography m Climatological parameter (scales between approximately 3 and 22 km are included). This parameter does not vary in time. Standard deviation of orography Dimensionless This parameter is one of four parameters (the others being angle of sub-gridscale orography, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the standard deviation of the height of the sub-grid valleys, hills and mountains within a grid box. This parameter does not vary in time. Standard deviation of orography Dimensionless This parameter is one of four parameters (the others being angle of sub-gridscale orography, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the standard deviation of the height of the sub-grid valleys, hills and mountains within a grid box. This parameter does not vary in time. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface latent heat flux J m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface latent heat flux J m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation, clear sky J m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation, clear sky J m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation, clear sky J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation, clear sky J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of land, sea and inland water. It is a measure of the weight of all the air in a column vertically above a point on the Earth's surface. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure weather systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa). Surface pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of land, sea and inland water. It is a measure of the weight of all the air in a column vertically above a point on the Earth's surface. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure weather systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa). Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface sensible heat flux J m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface sensible heat flux J m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downward, clear sky J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downward, clear sky J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downward, clear sky J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downward, clear sky J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. TOA incident solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. TOA incident solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Temperature of snow layer K This parameter gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Temperature of snow layer K This parameter gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Top net solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation, clear sky J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation, clear sky J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation J m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation J m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation, clear sky J m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). Note that OLR is typically shown in units of watts per square metre (W m-2 ). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. Top net thermal radiation, clear sky J m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). Note that OLR is typically shown in units of watts per square metre (W m-2 ). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. Total cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights. Cloud fractions vary from 0 to 1. Total cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights. Cloud fractions vary from 0 to 1. Total column cloud ice water kg m-2 This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud ice water kg m-2 This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud liquid water kg m-2 This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud liquid water kg m-2 This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can also be referred to as total ozone, or vertically integrated ozone. The values are dominated by ozone within the stratosphere. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. In the IFS, the units for total ozone are kilograms per square metre, but before 12/06/2001 dobson units were used. Dobson units (DU) are still used extensively for total column ozone. 1 DU = 2.1415E-5 kg m-2 Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can also be referred to as total ozone, or vertically integrated ozone. The values are dominated by ozone within the stratosphere. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. In the IFS, the units for total ozone are kilograms per square metre, but before 12/06/2001 dobson units were used. Dobson units (DU) are still used extensively for total column ozone. 1 DU = 2.1415E-5 kg m-2 Total column rain water kg m-2 This parameter is the total amount of water in droplets of raindrop size (which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column rain water kg m-2 This parameter is the total amount of water in droplets of raindrop size (which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column snow water kg m-2 This parameter is the total amount of water in the form of snow (aggregated ice crystals which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column snow water kg m-2 This parameter is the total amount of water in the form of snow (aggregated ice crystals which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column supercooled liquid water kg m-2 This parameter is the total amount of supercooled water in a column extending from the surface of the Earth to the top of the atmosphere. Supercooled water is water that exists in liquid form below 0oC. It is common in cold clouds and is important in the formation of precipitation. Also, supercooled water in clouds extending to the surface (i.e., fog) can cause icing/riming of various structures. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column supercooled liquid water kg m-2 This parameter is the total amount of supercooled water in a column extending from the surface of the Earth to the top of the atmosphere. Supercooled water is water that exists in liquid form below 0oC. It is common in cold clouds and is important in the formation of precipitation. Also, supercooled water in clouds extending to the surface (i.e., fog) can cause icing/riming of various structures. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column water kg m-2 This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for. Total column water kg m-2 This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for. Total column water vapour kg m-2 This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Total column water vapour kg m-2 This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Total precipitation m This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Total precipitation m This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Total sky direct solar radiation at surface J m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Total sky direct solar radiation at surface J m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Total totals index K This parameter gives an indication of the probability of occurrence of a thunderstorm and its severity by using the vertical gradient of temperature and humidity. The values of this index indicate the following: <44 Thunderstorms not likely, 44-50 Thunderstorms likely, 51-52 Isolated severe thunderstorms, 53-56 Widely scattered severe thunderstorms, 56-60 Scattered severe thunderstorms more likely. The total totals index is the temperature difference between 850 hPa (near surface) and 500 hPa (mid-troposphere) (lapse rate) plus a measure of the moisture content between 850 hPa and 500 hPa. The probability of deep convection tends to increase with increasing lapse rate and atmospheric moisture content. There are a number of limitations to this index. Also, the interpretation of the index value varies with season and location. Total totals index K This parameter gives an indication of the probability of occurrence of a thunderstorm and its severity by using the vertical gradient of temperature and humidity. The values of this index indicate the following: <44 Thunderstorms not likely, 44-50 Thunderstorms likely, 51-52 Isolated severe thunderstorms, 53-56 Widely scattered severe thunderstorms, 56-60 Scattered severe thunderstorms more likely. The total totals index is the temperature difference between 850 hPa (near surface) and 500 hPa (mid-troposphere) (lapse rate) plus a measure of the moisture content between 850 hPa and 500 hPa. The probability of deep convection tends to increase with increasing lapse rate and atmospheric moisture content. There are a number of limitations to this index. Also, the interpretation of the index value varies with season and location. Trapping layer base height m Trapping layer base height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer base height m Trapping layer base height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer top height m Trapping layer top height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer top height m Trapping layer top height as diagnosed from the vertical gradient of atmospheric refractivity. Type of high vegetation Dimensionless This parameter indicates the 6 types of high vegetation recognised by the ECMWF Integrated Forecasting System: 3 = Evergreen needleleaf trees, 4 = Deciduous needleleaf trees, 5 = Deciduous broadleaf trees, 6 = Evergreen broadleaf trees, 18 = Mixed forest/woodland, 19 = Interrupted forest. A value of 0 indicates a point without high vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of high vegetation Dimensionless This parameter indicates the 6 types of high vegetation recognised by the ECMWF Integrated Forecasting System: 3 = Evergreen needleleaf trees, 4 = Deciduous needleleaf trees, 5 = Deciduous broadleaf trees, 6 = Evergreen broadleaf trees, 18 = Mixed forest/woodland, 19 = Interrupted forest. A value of 0 indicates a point without high vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of low vegetation Dimensionless This parameter indicates the 10 types of low vegetation recognised by the ECMWF Integrated Forecasting System: 1 = Crops, Mixed farming, 2 = Grass, 7 = Tall grass, 9 = Tundra, 10 = Irrigated crops, 11 = Semidesert, 13 = Bogs and marshes, 16 = Evergreen shrubs, 17 = Deciduous shrubs, 20 = Water and land mixtures. A value of 0 indicates a point without low vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of low vegetation Dimensionless This parameter indicates the 10 types of low vegetation recognised by the ECMWF Integrated Forecasting System: 1 = Crops, Mixed farming, 2 = Grass, 7 = Tall grass, 9 = Tundra, 10 = Irrigated crops, 11 = Semidesert, 13 = Bogs and marshes, 16 = Evergreen shrubs, 17 = Deciduous shrubs, 20 = Water and land mixtures. A value of 0 indicates a point without low vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. U-component stokes drift m s-1 This parameter is the eastward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. U-component stokes drift m s-1 This parameter is the eastward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. UV visible albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. This parameter varies between 0 and 1. UV visible albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. This parameter varies between 0 and 1. UV visible albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. UV visible albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. V-component stokes drift m s-1 This parameter is the northward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. V-component stokes drift m s-1 This parameter is the northward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. Vertical integral of divergence of cloud frozen water flux kg m-2 s-1 The vertical integral of the cloud frozen water flux is the horizontal rate of flow of cloud frozen water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud frozen water spreading outward from a point, per square metre. This parameter is positive for cloud frozen water that is spreading out, or diverging, and negative for the opposite, for cloud frozen water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud frozen water. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of divergence of cloud frozen water flux kg m-2 s-1 The vertical integral of the cloud frozen water flux is the horizontal rate of flow of cloud frozen water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud frozen water spreading outward from a point, per square metre. This parameter is positive for cloud frozen water that is spreading out, or diverging, and negative for the opposite, for cloud frozen water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud frozen water. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of divergence of cloud liquid water flux kg m-2 s-1 The vertical integral of the cloud liquid water flux is the horizontal rate of flow of cloud liquid water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud liquid water spreading outward from a point, per square metre. This parameter is positive for cloud liquid water that is spreading out, or diverging, and negative for the opposite, for cloud liquid water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud liquid water. Vertical integral of divergence of cloud liquid water flux kg m-2 s-1 The vertical integral of the cloud liquid water flux is the horizontal rate of flow of cloud liquid water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud liquid water spreading outward from a point, per square metre. This parameter is positive for cloud liquid water that is spreading out, or diverging, and negative for the opposite, for cloud liquid water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud liquid water. Vertical integral of divergence of geopotential flux W m-2 The vertical integral of the geopotential flux is the horizontal rate of flow of geopotential, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of geopotential spreading outward from a point, per square metre. This parameter is positive for geopotential that is spreading out, or diverging, and negative for the opposite, for geopotential that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of geopotential. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of geopotential flux W m-2 The vertical integral of the geopotential flux is the horizontal rate of flow of geopotential, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of geopotential spreading outward from a point, per square metre. This parameter is positive for geopotential that is spreading out, or diverging, and negative for the opposite, for geopotential that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of geopotential. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of kinetic energy flux W m-2 The vertical integral of the kinetic energy flux is the horizontal rate of flow of kinetic energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of kinetic energy spreading outward from a point, per square metre. This parameter is positive for kinetic energy that is spreading out, or diverging, and negative for the opposite, for kinetic energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of kinetic energy. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of kinetic energy flux W m-2 The vertical integral of the kinetic energy flux is the horizontal rate of flow of kinetic energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of kinetic energy spreading outward from a point, per square metre. This parameter is positive for kinetic energy that is spreading out, or diverging, and negative for the opposite, for kinetic energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of kinetic energy. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of mass flux kg m-2 s-1 The vertical integral of the mass flux is the horizontal rate of flow of mass, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of mass spreading outward from a point, per square metre. This parameter is positive for mass that is spreading out, or diverging, and negative for the opposite, for mass that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of mass. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of divergence of mass flux kg m-2 s-1 The vertical integral of the mass flux is the horizontal rate of flow of mass, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of mass spreading outward from a point, per square metre. This parameter is positive for mass that is spreading out, or diverging, and negative for the opposite, for mass that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of mass. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of divergence of moisture flux kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Vertical integral of divergence of moisture flux kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Vertical integral of divergence of ozone flux kg m-2 s-1 The vertical integral of the ozone flux is the horizontal rate of flow of ozone, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of ozone spreading outward from a point, per square metre. This parameter is positive for ozone that is spreading out, or diverging, and negative for the opposite, for ozone that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of ozone. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of divergence of ozone flux kg m-2 s-1 The vertical integral of the ozone flux is the horizontal rate of flow of ozone, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of ozone spreading outward from a point, per square metre. This parameter is positive for ozone that is spreading out, or diverging, and negative for the opposite, for ozone that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of ozone. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of divergence of thermal energy flux W m-2 The vertical integral of the thermal energy flux is the horizontal rate of flow of thermal energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of thermal energy spreading outward from a point, per square metre. This parameter is positive for thermal energy that is spreading out, or diverging, and negative for the opposite, for thermal energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of thermal energy. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the flow of thermal energy through the climate system and to investigate the atmospheric energy budget. Vertical integral of divergence of thermal energy flux W m-2 The vertical integral of the thermal energy flux is the horizontal rate of flow of thermal energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of thermal energy spreading outward from a point, per square metre. This parameter is positive for thermal energy that is spreading out, or diverging, and negative for the opposite, for thermal energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of thermal energy. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the flow of thermal energy through the climate system and to investigate the atmospheric energy budget. Vertical integral of divergence of total energy flux W m-2 The vertical integral of the total energy flux is the horizontal rate of flow of total energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of total energy spreading outward from a point, per square metre. This parameter is positive for total energy that is spreading out, or diverging, and negative for the opposite, for total energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of total energy. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of total energy flux W m-2 The vertical integral of the total energy flux is the horizontal rate of flow of total energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of total energy spreading outward from a point, per square metre. This parameter is positive for total energy that is spreading out, or diverging, and negative for the opposite, for total energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of total energy. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of eastward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of eastward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the eastward direction, per meter across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the eastward direction, per meter across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of eastward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of eastward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from west to east. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of eastward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from west to east. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of eastward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of energy conversion W m-2 This parameter is one contribution to the amount of energy being converted between kinetic energy, and internal plus potential energy, for a column of air extending from the surface of the Earth to the top of the atmosphere. Negative values indicate a conversion to kinetic energy from potential plus internal energy. This parameter can be used to study the atmospheric energy budget. The circulation of the atmosphere can also be considered in terms of energy conversions. Vertical integral of energy conversion W m-2 This parameter is one contribution to the amount of energy being converted between kinetic energy, and internal plus potential energy, for a column of air extending from the surface of the Earth to the top of the atmosphere. Negative values indicate a conversion to kinetic energy from potential plus internal energy. This parameter can be used to study the atmospheric energy budget. The circulation of the atmosphere can also be considered in terms of energy conversions. Vertical integral of kinetic energy J m-2 This parameter is the vertical integral of kinetic energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of kinetic energy J m-2 This parameter is the vertical integral of kinetic energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of mass of atmosphere kg m-2 This parameter is the total mass of air for a column extending from the surface of the Earth to the top of the atmosphere, per square metre. This parameter is calculated by dividing surface pressure by the Earth's gravitational acceleration, g (=9.80665 m s-2 ), and has units of kilograms per square metre. This parameter can be used to study the atmospheric mass budget. Vertical integral of mass of atmosphere kg m-2 This parameter is the total mass of air for a column extending from the surface of the Earth to the top of the atmosphere, per square metre. This parameter is calculated by dividing surface pressure by the Earth's gravitational acceleration, g (=9.80665 m s-2 ), and has units of kilograms per square metre. This parameter can be used to study the atmospheric mass budget. Vertical integral of mass tendency kg m-2 s-1 This parameter is the rate of change of the mass of a column of air extending from the Earth's surface to the top of the atmosphere. An increasing mass of the column indicates rising surface pressure. In contrast, a decrease indicates a falling surface pressure. The mass of the column is calculated by dividing pressure at the Earth's surface by the gravitational acceleration, g (=9.80665 m s-2 ). This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of mass tendency kg m-2 s-1 This parameter is the rate of change of the mass of a column of air extending from the Earth's surface to the top of the atmosphere. An increasing mass of the column indicates rising surface pressure. In contrast, a decrease indicates a falling surface pressure. The mass of the column is calculated by dividing pressure at the Earth's surface by the gravitational acceleration, g (=9.80665 m s-2 ). This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of northward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of northward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from south to north. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of northward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from south to north. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of northward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of potential and internal energy J m-2 This parameter is the mass weighted vertical integral of potential and internal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential and internal energy J m-2 This parameter is the mass weighted vertical integral of potential and internal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential, internal and latent energy J m-2 This parameter is the mass weighted vertical integral of potential, internal and latent energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The latent energy refers to the energy associated with the water vapour in the atmosphere and is equal to the energy required to convert liquid water into water vapour. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential, internal and latent energy J m-2 This parameter is the mass weighted vertical integral of potential, internal and latent energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The latent energy refers to the energy associated with the water vapour in the atmosphere and is equal to the energy required to convert liquid water into water vapour. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of temperature K kg m-2 This parameter is the mass-weighted vertical integral of temperature for a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can be used to study the atmospheric energy budget. Vertical integral of temperature K kg m-2 This parameter is the mass-weighted vertical integral of temperature for a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can be used to study the atmospheric energy budget. Vertical integral of thermal energy J m-2 This parameter is the mass-weighted vertical integral of thermal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Thermal energy is calculated from the product of temperature and the specific heat capacity of air at constant pressure. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of thermal energy J m-2 This parameter is the mass-weighted vertical integral of thermal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Thermal energy is calculated from the product of temperature and the specific heat capacity of air at constant pressure. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of total energy J m-2 This parameter is the vertical integral of total energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of total energy J m-2 This parameter is the vertical integral of total energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertically integrated moisture divergence kg m-2 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm. Vertically integrated moisture divergence kg m-2 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm. Volumetric soil water layer 1 m3 m-3 This parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 m3 m-3 This parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 This parameter is the volume of water in soil layer 2 (7 - 28cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 This parameter is the volume of water in soil layer 2 (7 - 28cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 3 m3 m-3 This parameter is the volume of water in soil layer 3 (28 - 100cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 3 m3 m-3 This parameter is the volume of water in soil layer 3 (28 - 100cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 4 m3 m-3 This parameter is the volume of water in soil layer 4 (100 - 289cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 4 m3 m-3 This parameter is the volume of water in soil layer 4 (100 - 289cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Wave spectral directional width Dimensionless This parameter indicates whether waves (generated by local winds and associated with swell) are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width Dimensionless This parameter indicates whether waves (generated by local winds and associated with swell) are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for swell Dimensionless This parameter indicates whether waves associated with swell are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for swell Dimensionless This parameter indicates whether waves associated with swell are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for wind waves Dimensionless This parameter indicates whether waves generated by the local wind are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for wind waves Dimensionless This parameter indicates whether waves generated by the local wind are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral kurtosis Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It shows how much of the probability density function of the sea surface elevation exists in the tails of the distribution. So, a positive kurtosis (typical range 0.0 to 0.06) means more frequent occurrences of very extreme values (either above or below the mean), relative to a normal distribution. Wave spectral kurtosis Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It shows how much of the probability density function of the sea surface elevation exists in the tails of the distribution. So, a positive kurtosis (typical range 0.0 to 0.06) means more frequent occurrences of very extreme values (either above or below the mean), relative to a normal distribution. Wave spectral peakedness Dimensionless This parameter is a statistical measure used to forecast extreme or freak waves. It is a measure of the relative width of the ocean/sea wave frequency spectrum (i.e., whether the ocean/sea wave field is made up of a narrow or broad range of frequencies). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). When the wave field is more focussed around a narrow range of frequencies, the probability of freak/extreme waves increases. This parameter is Goda's peakedness factor and is used to calculate the Benjamin-Feir Index (BFI). The BFI is in turn used to estimate the probability and nature of extreme/freak waves. Wave spectral peakedness Dimensionless This parameter is a statistical measure used to forecast extreme or freak waves. It is a measure of the relative width of the ocean/sea wave frequency spectrum (i.e., whether the ocean/sea wave field is made up of a narrow or broad range of frequencies). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). When the wave field is more focussed around a narrow range of frequencies, the probability of freak/extreme waves increases. This parameter is Goda's peakedness factor and is used to calculate the Benjamin-Feir Index (BFI). The BFI is in turn used to estimate the probability and nature of extreme/freak waves. Wave spectral skewness Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It is a measure of the asymmetry of the probability density function of the sea surface elevation. So, a positive/negative skewness (typical range -0.2 to 0.12) means more frequent occurrences of extreme values above/below the mean, relative to a normal distribution. Wave spectral skewness Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It is a measure of the asymmetry of the probability density function of the sea surface elevation. So, a positive/negative skewness (typical range -0.2 to 0.12) means more frequent occurrences of extreme values above/below the mean, relative to a normal distribution. Zero degree level m The height above the Earth's surface where the temperature passes from positive to negative values, corresponding to the top of a warm layer, at the specified time. This parameter can be used to help forecast snow. If more than one warm layer is encountered, then the zero degree level corresponds to the top of the second atmospheric layer. This parameter is set to zero when the temperature in the whole atmosphere is below 0℃. Zero degree level m The height above the Earth's surface where the temperature passes from positive to negative values, corresponding to the top of a warm layer, at the specified time. This parameter can be used to help forecast snow. If more than one warm layer is encountered, then the zero degree level corresponds to the top of the second atmospheric layer. This parameter is set to zero when the temperature in the whole atmosphere is below 0℃. 364 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-era5-single-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels reanalysis-era5-single-levels ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. ERA5 Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. this article these guidelines Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present". DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Temporal coverage 1940 to present Temporal resolution Hourly File format GRIB Update frequency Daily DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Horizontal resolution Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Temporal coverage 1940 to present Temporal coverage 1940 to present Temporal resolution Hourly Temporal resolution Hourly File format GRIB File format GRIB Update frequency Daily Update frequency Daily MAIN VARIABLES Name Units Description 100m u-component of wind m s-1 This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind. 100m v-component of wind m s-1 This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind. 10m u-component of neutral wind m s-1 This parameter is the eastward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m u-component of wind m s-1 This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m v-component of neutral wind m s-1 This parameter is the northward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m v-component of wind m s-1 This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m wind gust since previous post-processing m s-1 Maximum 3 second wind at 10 m height as defined by WMO. Parametrization represents turbulence only before 01102008; thereafter effects of convection are included. The 3 s gust is computed every time step and and the maximum is kept since the last postprocessing. 2m dewpoint temperature K This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Air density over the oceans kg m-3 This parameter is the mass of air per cubic metre over the oceans, derived from the temperature, specific humidity and pressure at the lowest model level in the atmospheric model. This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Angle of sub-gridscale orography radians This parameter is one of four parameters (the others being standard deviation, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. The angle of the sub-grid scale orography characterises the geographical orientation of the terrain in the horizontal plane (from a bird's-eye view) relative to an eastwards axis. This parameter does not vary in time. Anisotropy of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, slope and angle of sub-gridscale orography) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter is a measure of how much the shape of the terrain in the horizontal plane (from a bird's-eye view) is distorted from a circle. A value of one is a circle, less than one an ellipse, and 0 is a ridge. In the case of a ridge, wind blowing parallel to it does not exert any drag on the flow, but wind blowing perpendicular to it exerts the maximum drag. This parameter does not vary in time. Benjamin-feir index Dimensionless This parameter is used to calculate the likelihood of freak ocean waves, which are waves that are higher than twice the mean height of the highest third of waves. Large values of this parameter (in practice of the order 1) indicate increased probability of the occurrence of freak waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived from the statistics of the two-dimensional wave spectrum. More precisely, it is the square of the ratio of the integral ocean wave steepness and the relative width of the frequency spectrum of the waves. Further information on the calculation of this parameter is given in Section 10.6 of the ECMWF Wave Model documentation. Boundary layer dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Boundary layer height m This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface. The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop. The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review. Charnock Dimensionless This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind. When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by the ECMWF Wave Model. Clear-sky direct solar radiation at surface J m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Cloud base height m The height above the Earth's surface of the base of the lowest cloud layer, at the specified time. This parameter is calculated by searching from the second lowest model level upwards, to the height of the level where cloud fraction becomes greater than 1% and condensate content greater than 1.E-6 kg kg-1. Fog (i.e., cloud in the lowest model layer) is not considered when defining cloud base height. Coefficient of drag with waves Dimensionless This parameter is the resistance that ocean waves exert on the atmosphere. It is sometimes also called a "friction coefficient". It is calculated by the wave model as the ratio of the square of the friction velocity, to the square of the neutral wind speed at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. Convective available potential energy J kg-1 This is an indication of the instability (or stability) of the atmosphere and can be used to assess the potential for the development of convection, which can lead to heavy rainfall, thunderstorms and other severe weather. In the ECMWF Integrated Forecasting System (IFS), CAPE is calculated by considering parcels of air departing at different model levels below the 350 hPa level. If a parcel of air is more buoyant (warmer and/or with more moisture) than its surrounding environment, it will continue to rise (cooling as it rises) until it reaches a point where it no longer has positive buoyancy. CAPE is the potential energy represented by the total excess buoyancy. The maximum CAPE produced by the different parcels is the value retained. Large positive values of CAPE indicate that an air parcel would be much warmer than its surrounding environment and therefore, very buoyant. CAPE is related to the maximum potential vertical velocity of air within an updraft; thus, higher values indicate greater potential for severe weather. Observed values in thunderstorm environments often may exceed 1000 joules per kilogram (J kg-1), and in extreme cases may exceed 5000 J kg-1. The calculation of this parameter assumes: (i) the parcel of air does not mix with surrounding air; (ii) ascent is pseudo-adiabatic (all condensed water falls out) and (iii) other simplifications related to the mixed-phase condensational heating. Convective inhibition J kg-1 This parameter is a measure of the amount of energy required for convection to commence. If the value of this parameter is too high, then deep, moist convection is unlikely to occur even if the convective available potential energy or convective available potential energy shear are large. CIN values greater than 200 J kg-1 would be considered high. An atmospheric layer where temperature increases with height (known as a temperature inversion) would inhibit convective uplift and is a situation in which convective inhibition would be large. Convective precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Rainfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Downward UV radiation at the surface J m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Duct base height m Duct base height as diagnosed from the vertical gradient of atmospheric refractivity. Eastward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Eastward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Forecast albedo Dimensionless This parameter is a measure of the reflectivity of the Earth's surface. It is the fraction of short-wave (solar) radiation reflected by the Earth's surface, for diffuse radiation, assuming a fixed spectrum of downward short-wave radiation at the surface. The values of this parameter vary between zero and one. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Short-wave radiation from the Sun is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Forecast logarithm of surface roughness for heat Dimensionless This parameter is the natural logarithm of the roughness length for heat. The surface roughness for heat is a measure of the surface resistance to heat transfer. This parameter is used to determine the air to surface transfer of heat. For given atmospheric conditions, a higher surface roughness for heat means that it is more difficult for the air to exchange heat with the surface. A lower surface roughness for heat means that it is easier for the air to exchange heat with the surface. Over the ocean, surface roughness for heat depends on the waves. Over sea-ice, it has a constant value of 0.001 m. Over land, it is derived from the vegetation type and snow cover. Forecast surface roughness m This parameter is the aerodynamic roughness length in metres. It is a measure of the surface resistance. This parameter is used to determine the air to surface transfer of momentum. For given atmospheric conditions, a higher surface roughness causes a slower near-surface wind speed. Over ocean, surface roughness depends on the waves. Over land, surface roughness is derived from the vegetation type and snow cover. Free convective velocity over the oceans m s-1 This parameter is an estimate of the vertical velocity of updraughts generated by free convection. Free convection is fluid motion induced by buoyancy forces, which are driven by density gradients. The free convective velocity is used to estimate the impact of wind gusts on ocean wave growth. It is calculated at the height of the lowest temperature inversion (the height above the surface of the Earth where the temperature increases with height). This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Friction velocity m s-1 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is a theoretical wind speed at the Earth's surface that expresses the magnitude of stress. It is calculated by dividing the surface stress by air density and taking its square root. For turbulent flow, the friction velocity is approximately constant in the lowest few metres of the atmosphere. This parameter increases with the roughness of the surface. It is used to calculate the way wind changes with height in the lowest levels of the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location at the surface of the Earth, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The (surface) geopotential height (orography) can be calculated by dividing the (surface) geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2 ). This parameter does not vary in time. Gravity wave dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. High cloud cover Dimensionless The proportion of a grid box covered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a "standard atmosphere")). The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. High vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "high". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Ice temperature layer 1 K This parameter is the sea-ice temperature in layer 1 (0 to 7cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 2 K This parameter is the sea-ice temperature in layer 2 (7 to 28cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 3 K This parameter is the sea-ice temperature in layer 3 (28 to 100cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 4 K This parameter is the sea-ice temperature in layer 4 (100 to 150cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Instantaneous 10m wind gust m s-1 This parameter is the maximum wind gust at the specified time, at a height of ten metres above the surface of the Earth. The WMO defines a wind gust as the maximum of the wind averaged over 3 second intervals. This duration is shorter than a model time step, and so the ECMWF Integrated Forecasting System (IFS) deduces the magnitude of a gust within each time step from the time-step-averaged surface stress, surface friction, wind shear and stability. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Instantaneous eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. Instantaneous large-scale surface precipitation fraction Dimensionless This parameter is the fraction of the grid box (0-1) covered by large-scale precipitation at the specified time. Large-scale precipitation is rain and snow that falls to the Earth's surface, and is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. Precipitation can also be due to convection generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box. Instantaneous moisture flux kg m-2 s-1 This parameter is the net rate of moisture exchange between the land/ocean surface and the atmosphere, due to the processes of evaporation (including evapotranspiration) and condensation, at the specified time. By convention, downward fluxes are positive, which means that evaporation is represented by negative values and condensation by positive values. Instantaneous northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. Instantaneous surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere, at the specified time, through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. The ECMWF convention for vertical fluxes is positive downwards. K index K This parameter is a measure of the potential for a thunderstorm to develop, calculated from the temperature and dew point temperature in the lower part of the atmosphere. The calculation uses the temperature at 850, 700 and 500 hPa and dewpoint temperature at 850 and 700 hPa. Higher values of K indicate a higher potential for the development of thunderstorms. This parameter is related to the probability of occurrence of a thunderstorm: <20 K No thunderstorm, 20-25 K Isolated thunderstorms, 26-30 K Widely scattered thunderstorms, 31-35 K Scattered thunderstorms, >35 K Numerous thunderstorms. Lake bottom temperature K This parameter is the temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake cover Dimensionless This parameter is the proportion of a grid box covered by inland water bodies (lakes, reservoirs, rivers and coastal waters). Values vary between 0: no inland water, and 1: grid box is fully covered with inland water. This parameter is specified from observations and does not vary in time. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth m This parameter is the mean depth of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is specified from in-situ measurements and indirect estimates and does not vary in time. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake ice depth m This parameter is the thickness of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter is the thickness of that ice layer. Lake ice temperature K This parameter is the temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). It is the temperature at the ice/atmosphere or ice/snow interface. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer depth m This parameter is the thickness of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake mix-layer temperature K This parameter is the temperature of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake shape factor Dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) i.e., it describes the shape of the vertical temperature profile. It is used to calculate the lake bottom temperature and other lake-related parameters. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake total layer temperature K This parameter is the mean temperature of the total water column in inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. This parameter is the mean temperature over the two layers. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Land-sea mask Dimensionless This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless. In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water. In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water. This parameter does not vary in time. Large scale rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Rainfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation fraction s This parameter is the accumulation of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Large-scale snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Leaf area index, high vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "high". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Leaf area index, low vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "low". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Low cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a "standard atmosphere")). Assumptions are made about the degree of overlap/randomness between clouds in different model levels. This parameter has values from 0 to 1. Low vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "low". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Maximum 2m temperature since previous post-processing K This parameter is the highest temperature of air at 2m above the surface of land, sea or inland water since the parameter was last archived in a particular forecast. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Maximum individual wave height m This parameter is an estimate of the height of the expected highest individual wave within a 20 minute time window. It can be used as a guide to the likelihood of extreme or freak waves. The interactions between waves are non-linear and occasionally concentrate wave energy giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered as a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Maximum total precipitation rate since previous post-processing kg m-2 s-1 The total precipitation is calculated from the combined large-scale and convective rainfall and snowfall rates every time step and the maximum is kept since the last postprocessing. Mean boundary layer dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean convective precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean direction of total swell degrees This parameter is the mean direction of waves associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of wind waves degrees The mean direction of waves generated by local winds. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea wave spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean eastward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean evaporation rate kg m-2 s-1 This parameter is the amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Mean gravity wave dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean large-scale precipitation fraction Dimensionless This parameter is the mean of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean large-scale precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean northward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean period of total swell s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea associated with swell, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. Mean period of wind waves s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea generated by local winds, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea spectrum. Mean potential evaporation rate kg m-2 s-1 This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean sea level pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of mean sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at mean sea level. It is calculated over all surfaces - land, sea and inland water. Maps of mean sea level pressure are used to identify the locations of low and high pressure weather systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds. The units of this parameter are pascals (Pa). Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Mean snow evaporation rate kg m-2 s-1 This parameter is the average rate of snow evaporation from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snow evaporation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Mean snowfall rate kg m-2 s-1 This parameter is the rate of snowfall at the Earth's surface. It is the sum of large-scale and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean snowmelt rate kg m-2 s-1 This parameter is the rate of snow melt in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the melting would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean square slope of waves Dimensionless This parameter can be related analytically to the average slope of combined wind-sea and swell waves. It can also be expressed as a function of wind speed under some statistical assumptions. The higher the slope, the steeper the waves. This parameter indicates the roughness of the sea/ocean surface which affects the interaction between ocean and atmosphere. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. Mean sub-surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface direct short-wave radiation flux W m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux, clear sky W m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward UV radiation flux W m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux, clear sky W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface latent heat flux W m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux, clear sky W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top downward short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux W m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux, clear sky W m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean top net short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux, clear sky W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean total precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface. It is the sum of the rates due to large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean vertical gradient of refractivity inside trapping layer m-1 Mean vertical gradient of atmospheric refractivity inside the trapping layer. Mean vertically integrated moisture divergence kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean wave direction degree true This parameter is the mean direction of ocean/sea surface waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use this type of wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of first swell partition degrees This parameter is the mean direction of waves in the first swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of second swell partition degrees This parameter is the mean direction of waves in the second swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of third swell partition degrees This parameter is the mean direction of waves in the third swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave period s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use such wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. Mean wave period based on first moment s This parameter is the reciprocal of the mean frequency of the wave components that represent the sea state. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for swell s This parameter is the reciprocal of the mean frequency of the wave components associated with swell. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for wind waves s This parameter is the reciprocal of the mean frequency of the wave components generated by local winds. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with wind waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for swell s This parameter is equivalent to the zero-crossing mean wave period for swell. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for wind waves s This parameter is equivalent to the zero-crossing mean wave period for waves generated by local winds. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period of first swell partition s This parameter is the mean period of waves in the first swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of second swell partition s This parameter is the mean period of waves in the second swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of third swell partition s This parameter is the mean period of waves in the third swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third swell partition might be from one system at one location and a different system at the neighbouring location). Mean zero-crossing wave period s This parameter represents the mean length of time between occasions where the sea/ocean surface crosses mean sea level. In combination with wave height information, it could be used to assess the length of time that a coastal structure might be under water, for example. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). In the ECMWF Integrated Forecasting System (IFS) this parameter is calculated from the characteristics of the two-dimensional wave spectrum. Medium cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a "standard atmosphere")). The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. Minimum 2m temperature since previous post-processing K This parameter is the lowest temperature of air at 2m above the surface of land, sea or inland waters since the parameter was last archived in a particular forecast. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. See further information. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Minimum total precipitation rate since previous post-processing kg m-2 s-1 The total precipitation is calculated from the combined large-scale and convective rainfall and snowfall rates every time step and the minimum is kept since the last postprocessing. Minimum vertical gradient of refractivity inside trapping layer m-1 Minimum vertical gradient of atmospheric refractivity inside the trapping layer. Model bathymetry m This parameter is the depth of water from the surface to the bottom of the ocean. It is used by the ocean wave model to specify the propagation properties of the different waves that could be present. Note that the ocean wave model grid is too coarse to resolve some small islands and mountains on the bottom of the ocean, but they can have an impact on surface ocean waves. The ocean wave model has been modified to reduce the wave energy flowing around or over features at spatial scales smaller than the grid box. Near IR albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Normalized energy flux into ocean Dimensionless This parameter is the normalised vertical flux of turbulent kinetic energy from ocean waves into the ocean. The energy flux is calculated from an estimation of the loss of wave energy due to white capping waves. A white capping wave is one that appears white at its crest as it breaks, due to air being mixed into the water. When waves break in this way, there is a transfer of energy from the waves to the ocean. Such a flux is defined to be negative. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into waves Dimensionless This parameter is the normalised vertical flux of energy from wind into the ocean waves. A positive flux implies a flux into the waves. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized stress into ocean Dimensionless This parameter is the normalised surface stress, or momentum flux, from the air into the ocean due to turbulence at the air-sea interface and breaking waves. It does not include the flux used to generate waves. The ECMWF convention for vertical fluxes is positive downwards. The stress has units of Newtons per metre squared, and this is normalised by being divided by the product of air density and the square of the friction velocity. Northward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Northward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Ocean surface stress equivalent 10m neutral wind direction degrees This parameter is the direction from which the "neutral wind" blows, in degrees clockwise from true north, at a height of ten metres above the surface of the Earth. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind direction used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind speed m s-1 This parameter is the horizontal speed of the "neutral wind", at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind speed used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Period corresponding to maximum individual wave height s This parameter is the period of the expected highest individual wave within a 20-minute time window. It can be used as a guide to the characteristics of extreme or freak waves. Wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. Occasionally waves of different periods reinforce and interact non-linearly giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered to be a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Potential evaporation m This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Precipitation type Dimensionless This parameter describes the type of precipitation at the surface, at the specified time. A precipitation type is assigned wherever there is a non-zero value of precipitation. In the ECMWF Integrated Forecasting System (IFS) there are only two predicted precipitation variables: rain and snow. Precipitation type is derived from these two predicted variables in combination with atmospheric conditions, such as temperature. Values of precipitation type defined in the IFS: 0: No precipitation, 1: Rain, 3: Freezing rain (i.e. supercooled raindrops which freeze on contact with the ground and other surfaces), 5: Snow, 6: Wet snow (i.e. snow particles which are starting to melt); 7: Mixture of rain and snow, 8: Ice pellets. These precipitation types are consistent with WMO Code Table 4.201. Other types in this WMO table are not defined in the IFS. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Sea surface temperature K This parameter (SST) is the temperature of sea water near the surface. In ERA5, this parameter is a foundation SST, which means there are no variations due to the daily cycle of the sun (diurnal variations). SST, in ERA5, is given by two external providers. Before September 2007, SST from the HadISST2 dataset is used and from September 2007 onwards, the OSTIA dataset is used. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Sea-ice cover Dimensionless This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land-sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover. In ERA5, sea-ice cover is given by two external providers. Before 1979 the HadISST2 dataset is used. From 1979 to August 2007 the OSI SAF (409a) dataset is used and from September 2007 the OSI SAF oper dataset is used. Sea ice is frozen sea water which floats on the surface of the ocean. Sea ice does not include ice which forms on land such as glaciers, icebergs and ice-sheets. It also excludes ice shelves which are anchored on land, but protrude out over the surface of the ocean. These phenomena are not modelled by the IFS. Long-term monitoring of sea ice is important for understanding climate change. Sea ice also affects shipping routes through the polar regions. Significant height of combined wind waves and swell m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. This parameter can be used to assess sea state and swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of total swell m This parameter represents the average height of the highest third of surface ocean/sea waves associated with swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of total swell only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional total swell spectrum. The total swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of wind waves m This parameter represents the average height of the highest third of surface ocean/sea waves generated by the local wind. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wind-sea wave spectrum. The wind-sea wave spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are still under the influence of the local wind. This parameter can be used to assess wind-sea waves. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of first swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the first swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of second swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the second swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of third swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the third swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Skin reservoir content m of water equivalent This parameter is the amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of "skin reservoir content" a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the "skin reservoir" by evaporation. Skin temperature K This parameter is the temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Slope of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, angle and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the slope of the sub-grid valleys, hills and mountains. A flat surface has a value of 0, and a 45 degree slope has a value of 0.5. This parameter does not vary in time. Snow albedo Dimensionless This parameter is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter changes with snow age and also depends on vegetation height. It has a range of values between 0 and 1. For low vegetation, it ranges between 0.52 for old snow and 0.88 for fresh snow. For high vegetation with snow underneath, it depends on vegetation type and has values between 0.27 and 0.38. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. Snow density kg m-3 This parameter is the mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. Snow depth m of water equivalent This parameter is the amount of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from snow from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the evaporated snow (from the snow-covered area of a grid box) were liquid and were spread evenly over the whole grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Snowmelt m of water equivalent This parameter is the accumulated amount of water that has melted from snow in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the melted snow (from the snow-covered area of a grid box) were spread evenly over the whole grid box. For example, if half the grid box were covered in snow with a water equivalent depth of 0.02m, this parameter would have a value of 0.01m. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Soil temperature level 1 K This parameter is the temperature of the soil at level 1 (in the middle of layer 1). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K This parameter is the temperature of the soil at level 2 (in the middle of layer 2). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 3 K This parameter is the temperature of the soil at level 3 (in the middle of layer 3). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 4 K This parameter is the temperature of the soil at level 4 (in the middle of layer 4). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil type Dimensionless This parameter is the texture (or classification) of soil used by the land surface scheme of the ECMWF Integrated Forecasting System (IFS) to predict the water holding capacity of soil in soil moisture and runoff calculations. It is derived from the root zone data (30-100 cm below the surface) of the FAO/UNESCO Digital Soil Map of the World, DSMW (FAO, 2003), which exists at a resolution of 5' X 5' (about 10 km). The seven soil types are: 1: Coarse, 2: Medium, 3: Medium fine, 4: Fine, 5: Very fine, 6: Organic, 7: Tropical organic. A value of 0 indicates a non-land point. This parameter does not vary in time. Standard deviation of filtered subgrid orography m Climatological parameter (scales between approximately 3 and 22 km are included). This parameter does not vary in time. Standard deviation of orography Dimensionless This parameter is one of four parameters (the others being angle of sub-gridscale orography, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the standard deviation of the height of the sub-grid valleys, hills and mountains within a grid box. This parameter does not vary in time. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface latent heat flux J m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation, clear sky J m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation, clear sky J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of land, sea and inland water. It is a measure of the weight of all the air in a column vertically above a point on the Earth's surface. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure weather systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa). Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface sensible heat flux J m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downward, clear sky J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downward, clear sky J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. TOA incident solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Temperature of snow layer K This parameter gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Top net solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation, clear sky J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation J m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation, clear sky J m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). Note that OLR is typically shown in units of watts per square metre (W m-2 ). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. Total cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights. Cloud fractions vary from 0 to 1. Total column cloud ice water kg m-2 This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud liquid water kg m-2 This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can also be referred to as total ozone, or vertically integrated ozone. The values are dominated by ozone within the stratosphere. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. In the IFS, the units for total ozone are kilograms per square metre, but before 12/06/2001 dobson units were used. Dobson units (DU) are still used extensively for total column ozone. 1 DU = 2.1415E-5 kg m-2 Total column rain water kg m-2 This parameter is the total amount of water in droplets of raindrop size (which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column snow water kg m-2 This parameter is the total amount of water in the form of snow (aggregated ice crystals which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column supercooled liquid water kg m-2 This parameter is the total amount of supercooled water in a column extending from the surface of the Earth to the top of the atmosphere. Supercooled water is water that exists in liquid form below 0oC. It is common in cold clouds and is important in the formation of precipitation. Also, supercooled water in clouds extending to the surface (i.e., fog) can cause icing/riming of various structures. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column water kg m-2 This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for. Total column water vapour kg m-2 This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Total precipitation m This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Total sky direct solar radiation at surface J m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Total totals index K This parameter gives an indication of the probability of occurrence of a thunderstorm and its severity by using the vertical gradient of temperature and humidity. The values of this index indicate the following: <44 Thunderstorms not likely, 44-50 Thunderstorms likely, 51-52 Isolated severe thunderstorms, 53-56 Widely scattered severe thunderstorms, 56-60 Scattered severe thunderstorms more likely. The total totals index is the temperature difference between 850 hPa (near surface) and 500 hPa (mid-troposphere) (lapse rate) plus a measure of the moisture content between 850 hPa and 500 hPa. The probability of deep convection tends to increase with increasing lapse rate and atmospheric moisture content. There are a number of limitations to this index. Also, the interpretation of the index value varies with season and location. Trapping layer base height m Trapping layer base height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer top height m Trapping layer top height as diagnosed from the vertical gradient of atmospheric refractivity. Type of high vegetation Dimensionless This parameter indicates the 6 types of high vegetation recognised by the ECMWF Integrated Forecasting System: 3 = Evergreen needleleaf trees, 4 = Deciduous needleleaf trees, 5 = Deciduous broadleaf trees, 6 = Evergreen broadleaf trees, 18 = Mixed forest/woodland, 19 = Interrupted forest. A value of 0 indicates a point without high vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of low vegetation Dimensionless This parameter indicates the 10 types of low vegetation recognised by the ECMWF Integrated Forecasting System: 1 = Crops, Mixed farming, 2 = Grass, 7 = Tall grass, 9 = Tundra, 10 = Irrigated crops, 11 = Semidesert, 13 = Bogs and marshes, 16 = Evergreen shrubs, 17 = Deciduous shrubs, 20 = Water and land mixtures. A value of 0 indicates a point without low vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. U-component stokes drift m s-1 This parameter is the eastward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. UV visible albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. This parameter varies between 0 and 1. UV visible albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. V-component stokes drift m s-1 This parameter is the northward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. Vertical integral of divergence of cloud frozen water flux kg m-2 s-1 The vertical integral of the cloud frozen water flux is the horizontal rate of flow of cloud frozen water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud frozen water spreading outward from a point, per square metre. This parameter is positive for cloud frozen water that is spreading out, or diverging, and negative for the opposite, for cloud frozen water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud frozen water. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of divergence of cloud liquid water flux kg m-2 s-1 The vertical integral of the cloud liquid water flux is the horizontal rate of flow of cloud liquid water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud liquid water spreading outward from a point, per square metre. This parameter is positive for cloud liquid water that is spreading out, or diverging, and negative for the opposite, for cloud liquid water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud liquid water. Vertical integral of divergence of geopotential flux W m-2 The vertical integral of the geopotential flux is the horizontal rate of flow of geopotential, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of geopotential spreading outward from a point, per square metre. This parameter is positive for geopotential that is spreading out, or diverging, and negative for the opposite, for geopotential that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of geopotential. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of kinetic energy flux W m-2 The vertical integral of the kinetic energy flux is the horizontal rate of flow of kinetic energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of kinetic energy spreading outward from a point, per square metre. This parameter is positive for kinetic energy that is spreading out, or diverging, and negative for the opposite, for kinetic energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of kinetic energy. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of mass flux kg m-2 s-1 The vertical integral of the mass flux is the horizontal rate of flow of mass, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of mass spreading outward from a point, per square metre. This parameter is positive for mass that is spreading out, or diverging, and negative for the opposite, for mass that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of mass. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of divergence of moisture flux kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Vertical integral of divergence of ozone flux kg m-2 s-1 The vertical integral of the ozone flux is the horizontal rate of flow of ozone, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of ozone spreading outward from a point, per square metre. This parameter is positive for ozone that is spreading out, or diverging, and negative for the opposite, for ozone that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of ozone. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of divergence of thermal energy flux W m-2 The vertical integral of the thermal energy flux is the horizontal rate of flow of thermal energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of thermal energy spreading outward from a point, per square metre. This parameter is positive for thermal energy that is spreading out, or diverging, and negative for the opposite, for thermal energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of thermal energy. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the flow of thermal energy through the climate system and to investigate the atmospheric energy budget. Vertical integral of divergence of total energy flux W m-2 The vertical integral of the total energy flux is the horizontal rate of flow of total energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of total energy spreading outward from a point, per square metre. This parameter is positive for total energy that is spreading out, or diverging, and negative for the opposite, for total energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of total energy. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of eastward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the eastward direction, per meter across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of eastward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from west to east. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of eastward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of energy conversion W m-2 This parameter is one contribution to the amount of energy being converted between kinetic energy, and internal plus potential energy, for a column of air extending from the surface of the Earth to the top of the atmosphere. Negative values indicate a conversion to kinetic energy from potential plus internal energy. This parameter can be used to study the atmospheric energy budget. The circulation of the atmosphere can also be considered in terms of energy conversions. Vertical integral of kinetic energy J m-2 This parameter is the vertical integral of kinetic energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of mass of atmosphere kg m-2 This parameter is the total mass of air for a column extending from the surface of the Earth to the top of the atmosphere, per square metre. This parameter is calculated by dividing surface pressure by the Earth's gravitational acceleration, g (=9.80665 m s-2 ), and has units of kilograms per square metre. This parameter can be used to study the atmospheric mass budget. Vertical integral of mass tendency kg m-2 s-1 This parameter is the rate of change of the mass of a column of air extending from the Earth's surface to the top of the atmosphere. An increasing mass of the column indicates rising surface pressure. In contrast, a decrease indicates a falling surface pressure. The mass of the column is calculated by dividing pressure at the Earth's surface by the gravitational acceleration, g (=9.80665 m s-2 ). This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of northward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from south to north. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of northward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of potential and internal energy J m-2 This parameter is the mass weighted vertical integral of potential and internal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential, internal and latent energy J m-2 This parameter is the mass weighted vertical integral of potential, internal and latent energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The latent energy refers to the energy associated with the water vapour in the atmosphere and is equal to the energy required to convert liquid water into water vapour. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of temperature K kg m-2 This parameter is the mass-weighted vertical integral of temperature for a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can be used to study the atmospheric energy budget. Vertical integral of thermal energy J m-2 This parameter is the mass-weighted vertical integral of thermal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Thermal energy is calculated from the product of temperature and the specific heat capacity of air at constant pressure. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of total energy J m-2 This parameter is the vertical integral of total energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertically integrated moisture divergence kg m-2 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm. Volumetric soil water layer 1 m3 m-3 This parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 This parameter is the volume of water in soil layer 2 (7 - 28cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 3 m3 m-3 This parameter is the volume of water in soil layer 3 (28 - 100cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 4 m3 m-3 This parameter is the volume of water in soil layer 4 (100 - 289cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Wave spectral directional width Dimensionless This parameter indicates whether waves (generated by local winds and associated with swell) are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for swell Dimensionless This parameter indicates whether waves associated with swell are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for wind waves Dimensionless This parameter indicates whether waves generated by the local wind are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral kurtosis Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It shows how much of the probability density function of the sea surface elevation exists in the tails of the distribution. So, a positive kurtosis (typical range 0.0 to 0.06) means more frequent occurrences of very extreme values (either above or below the mean), relative to a normal distribution. Wave spectral peakedness Dimensionless This parameter is a statistical measure used to forecast extreme or freak waves. It is a measure of the relative width of the ocean/sea wave frequency spectrum (i.e., whether the ocean/sea wave field is made up of a narrow or broad range of frequencies). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). When the wave field is more focussed around a narrow range of frequencies, the probability of freak/extreme waves increases. This parameter is Goda's peakedness factor and is used to calculate the Benjamin-Feir Index (BFI). The BFI is in turn used to estimate the probability and nature of extreme/freak waves. Wave spectral skewness Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It is a measure of the asymmetry of the probability density function of the sea surface elevation. So, a positive/negative skewness (typical range -0.2 to 0.12) means more frequent occurrences of extreme values above/below the mean, relative to a normal distribution. Zero degree level m The height above the Earth's surface where the temperature passes from positive to negative values, corresponding to the top of a warm layer, at the specified time. This parameter can be used to help forecast snow. If more than one warm layer is encountered, then the zero degree level corresponds to the top of the second atmospheric layer. This parameter is set to zero when the temperature in the whole atmosphere is below 0℃. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 100m u-component of wind m s-1 This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind. 100m u-component of wind m s-1 This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind. 100m v-component of wind m s-1 This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind. 100m v-component of wind m s-1 This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind. 10m u-component of neutral wind m s-1 This parameter is the eastward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m u-component of neutral wind m s-1 This parameter is the eastward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m u-component of wind m s-1 This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m u-component of wind m s-1 This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m v-component of neutral wind m s-1 This parameter is the northward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m v-component of neutral wind m s-1 This parameter is the northward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m v-component of wind m s-1 This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m v-component of wind m s-1 This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind. 10m wind gust since previous post-processing m s-1 Maximum 3 second wind at 10 m height as defined by WMO. Parametrization represents turbulence only before 01102008; thereafter effects of convection are included. The 3 s gust is computed every time step and and the maximum is kept since the last postprocessing. 10m wind gust since previous post-processing m s-1 Maximum 3 second wind at 10 m height as defined by WMO. Parametrization represents turbulence only before 01102008; thereafter effects of convection are included. The 3 s gust is computed every time step and and the maximum is kept since the last postprocessing. 2m dewpoint temperature K This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m dewpoint temperature K This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Air density over the oceans kg m-3 This parameter is the mass of air per cubic metre over the oceans, derived from the temperature, specific humidity and pressure at the lowest model level in the atmospheric model. This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Air density over the oceans kg m-3 This parameter is the mass of air per cubic metre over the oceans, derived from the temperature, specific humidity and pressure at the lowest model level in the atmospheric model. This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Angle of sub-gridscale orography radians This parameter is one of four parameters (the others being standard deviation, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. The angle of the sub-grid scale orography characterises the geographical orientation of the terrain in the horizontal plane (from a bird's-eye view) relative to an eastwards axis. This parameter does not vary in time. Angle of sub-gridscale orography radians This parameter is one of four parameters (the others being standard deviation, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. The angle of the sub-grid scale orography characterises the geographical orientation of the terrain in the horizontal plane (from a bird's-eye view) relative to an eastwards axis. This parameter does not vary in time. Anisotropy of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, slope and angle of sub-gridscale orography) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter is a measure of how much the shape of the terrain in the horizontal plane (from a bird's-eye view) is distorted from a circle. A value of one is a circle, less than one an ellipse, and 0 is a ridge. In the case of a ridge, wind blowing parallel to it does not exert any drag on the flow, but wind blowing perpendicular to it exerts the maximum drag. This parameter does not vary in time. Anisotropy of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, slope and angle of sub-gridscale orography) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter is a measure of how much the shape of the terrain in the horizontal plane (from a bird's-eye view) is distorted from a circle. A value of one is a circle, less than one an ellipse, and 0 is a ridge. In the case of a ridge, wind blowing parallel to it does not exert any drag on the flow, but wind blowing perpendicular to it exerts the maximum drag. This parameter does not vary in time. Benjamin-feir index Dimensionless This parameter is used to calculate the likelihood of freak ocean waves, which are waves that are higher than twice the mean height of the highest third of waves. Large values of this parameter (in practice of the order 1) indicate increased probability of the occurrence of freak waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived from the statistics of the two-dimensional wave spectrum. More precisely, it is the square of the ratio of the integral ocean wave steepness and the relative width of the frequency spectrum of the waves. Further information on the calculation of this parameter is given in Section 10.6 of the ECMWF Wave Model documentation. Benjamin-feir index Dimensionless This parameter is used to calculate the likelihood of freak ocean waves, which are waves that are higher than twice the mean height of the highest third of waves. Large values of this parameter (in practice of the order 1) indicate increased probability of the occurrence of freak waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived from the statistics of the two-dimensional wave spectrum. More precisely, it is the square of the ratio of the integral ocean wave steepness and the relative width of the frequency spectrum of the waves. Further information on the calculation of this parameter is given in Section 10.6 of the ECMWF Wave Model documentation. Boundary layer dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Boundary layer dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Boundary layer height m This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface. The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop. The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review. Boundary layer height m This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface. The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop. The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review. Charnock Dimensionless This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind. When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by the ECMWF Wave Model. Charnock Dimensionless This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind. When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by the ECMWF Wave Model. Clear-sky direct solar radiation at surface J m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Clear-sky direct solar radiation at surface J m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Cloud base height m The height above the Earth's surface of the base of the lowest cloud layer, at the specified time. This parameter is calculated by searching from the second lowest model level upwards, to the height of the level where cloud fraction becomes greater than 1% and condensate content greater than 1.E-6 kg kg-1. Fog (i.e., cloud in the lowest model layer) is not considered when defining cloud base height. Cloud base height m The height above the Earth's surface of the base of the lowest cloud layer, at the specified time. This parameter is calculated by searching from the second lowest model level upwards, to the height of the level where cloud fraction becomes greater than 1% and condensate content greater than 1.E-6 kg kg-1. Fog (i.e., cloud in the lowest model layer) is not considered when defining cloud base height. Coefficient of drag with waves Dimensionless This parameter is the resistance that ocean waves exert on the atmosphere. It is sometimes also called a "friction coefficient". It is calculated by the wave model as the ratio of the square of the friction velocity, to the square of the neutral wind speed at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. Coefficient of drag with waves Dimensionless This parameter is the resistance that ocean waves exert on the atmosphere. It is sometimes also called a "friction coefficient". It is calculated by the wave model as the ratio of the square of the friction velocity, to the square of the neutral wind speed at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. Convective available potential energy J kg-1 This is an indication of the instability (or stability) of the atmosphere and can be used to assess the potential for the development of convection, which can lead to heavy rainfall, thunderstorms and other severe weather. In the ECMWF Integrated Forecasting System (IFS), CAPE is calculated by considering parcels of air departing at different model levels below the 350 hPa level. If a parcel of air is more buoyant (warmer and/or with more moisture) than its surrounding environment, it will continue to rise (cooling as it rises) until it reaches a point where it no longer has positive buoyancy. CAPE is the potential energy represented by the total excess buoyancy. The maximum CAPE produced by the different parcels is the value retained. Large positive values of CAPE indicate that an air parcel would be much warmer than its surrounding environment and therefore, very buoyant. CAPE is related to the maximum potential vertical velocity of air within an updraft; thus, higher values indicate greater potential for severe weather. Observed values in thunderstorm environments often may exceed 1000 joules per kilogram (J kg-1), and in extreme cases may exceed 5000 J kg-1. The calculation of this parameter assumes: (i) the parcel of air does not mix with surrounding air; (ii) ascent is pseudo-adiabatic (all condensed water falls out) and (iii) other simplifications related to the mixed-phase condensational heating. Convective available potential energy J kg-1 This is an indication of the instability (or stability) of the atmosphere and can be used to assess the potential for the development of convection, which can lead to heavy rainfall, thunderstorms and other severe weather. In the ECMWF Integrated Forecasting System (IFS), CAPE is calculated by considering parcels of air departing at different model levels below the 350 hPa level. If a parcel of air is more buoyant (warmer and/or with more moisture) than its surrounding environment, it will continue to rise (cooling as it rises) until it reaches a point where it no longer has positive buoyancy. CAPE is the potential energy represented by the total excess buoyancy. The maximum CAPE produced by the different parcels is the value retained. Large positive values of CAPE indicate that an air parcel would be much warmer than its surrounding environment and therefore, very buoyant. CAPE is related to the maximum potential vertical velocity of air within an updraft; thus, higher values indicate greater potential for severe weather. Observed values in thunderstorm environments often may exceed 1000 joules per kilogram (J kg-1), and in extreme cases may exceed 5000 J kg-1. The calculation of this parameter assumes: (i) the parcel of air does not mix with surrounding air; (ii) ascent is pseudo-adiabatic (all condensed water falls out) and (iii) other simplifications related to the mixed-phase condensational heating. Convective inhibition J kg-1 This parameter is a measure of the amount of energy required for convection to commence. If the value of this parameter is too high, then deep, moist convection is unlikely to occur even if the convective available potential energy or convective available potential energy shear are large. CIN values greater than 200 J kg-1 would be considered high. An atmospheric layer where temperature increases with height (known as a temperature inversion) would inhibit convective uplift and is a situation in which convective inhibition would be large. Convective inhibition J kg-1 This parameter is a measure of the amount of energy required for convection to commence. If the value of this parameter is too high, then deep, moist convection is unlikely to occur even if the convective available potential energy or convective available potential energy shear are large. CIN values greater than 200 J kg-1 would be considered high. An atmospheric layer where temperature increases with height (known as a temperature inversion) would inhibit convective uplift and is a situation in which convective inhibition would be large. Convective precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Rainfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Rainfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Downward UV radiation at the surface J m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Downward UV radiation at the surface J m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Duct base height m Duct base height as diagnosed from the vertical gradient of atmospheric refractivity. Duct base height m Duct base height as diagnosed from the vertical gradient of atmospheric refractivity. Eastward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Eastward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Eastward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Eastward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Forecast albedo Dimensionless This parameter is a measure of the reflectivity of the Earth's surface. It is the fraction of short-wave (solar) radiation reflected by the Earth's surface, for diffuse radiation, assuming a fixed spectrum of downward short-wave radiation at the surface. The values of this parameter vary between zero and one. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Short-wave radiation from the Sun is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Forecast albedo Dimensionless This parameter is a measure of the reflectivity of the Earth's surface. It is the fraction of short-wave (solar) radiation reflected by the Earth's surface, for diffuse radiation, assuming a fixed spectrum of downward short-wave radiation at the surface. The values of this parameter vary between zero and one. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Short-wave radiation from the Sun is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Forecast logarithm of surface roughness for heat Dimensionless This parameter is the natural logarithm of the roughness length for heat. The surface roughness for heat is a measure of the surface resistance to heat transfer. This parameter is used to determine the air to surface transfer of heat. For given atmospheric conditions, a higher surface roughness for heat means that it is more difficult for the air to exchange heat with the surface. A lower surface roughness for heat means that it is easier for the air to exchange heat with the surface. Over the ocean, surface roughness for heat depends on the waves. Over sea-ice, it has a constant value of 0.001 m. Over land, it is derived from the vegetation type and snow cover. Forecast logarithm of surface roughness for heat Dimensionless This parameter is the natural logarithm of the roughness length for heat. The surface roughness for heat is a measure of the surface resistance to heat transfer. This parameter is used to determine the air to surface transfer of heat. For given atmospheric conditions, a higher surface roughness for heat means that it is more difficult for the air to exchange heat with the surface. A lower surface roughness for heat means that it is easier for the air to exchange heat with the surface. Over the ocean, surface roughness for heat depends on the waves. Over sea-ice, it has a constant value of 0.001 m. Over land, it is derived from the vegetation type and snow cover. Forecast surface roughness m This parameter is the aerodynamic roughness length in metres. It is a measure of the surface resistance. This parameter is used to determine the air to surface transfer of momentum. For given atmospheric conditions, a higher surface roughness causes a slower near-surface wind speed. Over ocean, surface roughness depends on the waves. Over land, surface roughness is derived from the vegetation type and snow cover. Forecast surface roughness m This parameter is the aerodynamic roughness length in metres. It is a measure of the surface resistance. This parameter is used to determine the air to surface transfer of momentum. For given atmospheric conditions, a higher surface roughness causes a slower near-surface wind speed. Over ocean, surface roughness depends on the waves. Over land, surface roughness is derived from the vegetation type and snow cover. Free convective velocity over the oceans m s-1 This parameter is an estimate of the vertical velocity of updraughts generated by free convection. Free convection is fluid motion induced by buoyancy forces, which are driven by density gradients. The free convective velocity is used to estimate the impact of wind gusts on ocean wave growth. It is calculated at the height of the lowest temperature inversion (the height above the surface of the Earth where the temperature increases with height). This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Free convective velocity over the oceans m s-1 This parameter is an estimate of the vertical velocity of updraughts generated by free convection. Free convection is fluid motion induced by buoyancy forces, which are driven by density gradients. The free convective velocity is used to estimate the impact of wind gusts on ocean wave growth. It is calculated at the height of the lowest temperature inversion (the height above the surface of the Earth where the temperature increases with height). This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Friction velocity m s-1 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is a theoretical wind speed at the Earth's surface that expresses the magnitude of stress. It is calculated by dividing the surface stress by air density and taking its square root. For turbulent flow, the friction velocity is approximately constant in the lowest few metres of the atmosphere. This parameter increases with the roughness of the surface. It is used to calculate the way wind changes with height in the lowest levels of the atmosphere. Friction velocity m s-1 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is a theoretical wind speed at the Earth's surface that expresses the magnitude of stress. It is calculated by dividing the surface stress by air density and taking its square root. For turbulent flow, the friction velocity is approximately constant in the lowest few metres of the atmosphere. This parameter increases with the roughness of the surface. It is used to calculate the way wind changes with height in the lowest levels of the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location at the surface of the Earth, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The (surface) geopotential height (orography) can be calculated by dividing the (surface) geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2 ). This parameter does not vary in time. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location at the surface of the Earth, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The (surface) geopotential height (orography) can be calculated by dividing the (surface) geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2 ). This parameter does not vary in time. Gravity wave dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Gravity wave dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. High cloud cover Dimensionless The proportion of a grid box covered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a "standard atmosphere")). The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. High cloud cover Dimensionless The proportion of a grid box covered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a "standard atmosphere")). The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. High vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "high". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. High vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "high". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Ice temperature layer 1 K This parameter is the sea-ice temperature in layer 1 (0 to 7cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 1 K This parameter is the sea-ice temperature in layer 1 (0 to 7cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 2 K This parameter is the sea-ice temperature in layer 2 (7 to 28cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 2 K This parameter is the sea-ice temperature in layer 2 (7 to 28cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 3 K This parameter is the sea-ice temperature in layer 3 (28 to 100cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 3 K This parameter is the sea-ice temperature in layer 3 (28 to 100cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 4 K This parameter is the sea-ice temperature in layer 4 (100 to 150cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Ice temperature layer 4 K This parameter is the sea-ice temperature in layer 4 (100 to 150cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the sea-ice cover does not have a missing value and is greater than 0.0. Instantaneous 10m wind gust m s-1 This parameter is the maximum wind gust at the specified time, at a height of ten metres above the surface of the Earth. The WMO defines a wind gust as the maximum of the wind averaged over 3 second intervals. This duration is shorter than a model time step, and so the ECMWF Integrated Forecasting System (IFS) deduces the magnitude of a gust within each time step from the time-step-averaged surface stress, surface friction, wind shear and stability. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Instantaneous 10m wind gust m s-1 This parameter is the maximum wind gust at the specified time, at a height of ten metres above the surface of the Earth. The WMO defines a wind gust as the maximum of the wind averaged over 3 second intervals. This duration is shorter than a model time step, and so the ECMWF Integrated Forecasting System (IFS) deduces the magnitude of a gust within each time step from the time-step-averaged surface stress, surface friction, wind shear and stability. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Instantaneous eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. Instantaneous eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. Instantaneous large-scale surface precipitation fraction Dimensionless This parameter is the fraction of the grid box (0-1) covered by large-scale precipitation at the specified time. Large-scale precipitation is rain and snow that falls to the Earth's surface, and is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. Precipitation can also be due to convection generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box. Instantaneous large-scale surface precipitation fraction Dimensionless This parameter is the fraction of the grid box (0-1) covered by large-scale precipitation at the specified time. Large-scale precipitation is rain and snow that falls to the Earth's surface, and is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. Precipitation can also be due to convection generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box. Instantaneous moisture flux kg m-2 s-1 This parameter is the net rate of moisture exchange between the land/ocean surface and the atmosphere, due to the processes of evaporation (including evapotranspiration) and condensation, at the specified time. By convention, downward fluxes are positive, which means that evaporation is represented by negative values and condensation by positive values. Instantaneous moisture flux kg m-2 s-1 This parameter is the net rate of moisture exchange between the land/ocean surface and the atmosphere, due to the processes of evaporation (including evapotranspiration) and condensation, at the specified time. By convention, downward fluxes are positive, which means that evaporation is represented by negative values and condensation by positive values. Instantaneous northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. Instantaneous northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. Instantaneous surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere, at the specified time, through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. The ECMWF convention for vertical fluxes is positive downwards. Instantaneous surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere, at the specified time, through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. The ECMWF convention for vertical fluxes is positive downwards. K index K This parameter is a measure of the potential for a thunderstorm to develop, calculated from the temperature and dew point temperature in the lower part of the atmosphere. The calculation uses the temperature at 850, 700 and 500 hPa and dewpoint temperature at 850 and 700 hPa. Higher values of K indicate a higher potential for the development of thunderstorms. This parameter is related to the probability of occurrence of a thunderstorm: <20 K No thunderstorm, 20-25 K Isolated thunderstorms, 26-30 K Widely scattered thunderstorms, 31-35 K Scattered thunderstorms, >35 K Numerous thunderstorms. K index K This parameter is a measure of the potential for a thunderstorm to develop, calculated from the temperature and dew point temperature in the lower part of the atmosphere. The calculation uses the temperature at 850, 700 and 500 hPa and dewpoint temperature at 850 and 700 hPa. Higher values of K indicate a higher potential for the development of thunderstorms. This parameter is related to the probability of occurrence of a thunderstorm: <20 K No thunderstorm, 20-25 K Isolated thunderstorms, 26-30 K Widely scattered thunderstorms, 31-35 K Scattered thunderstorms, >35 K Numerous thunderstorms. Lake bottom temperature K This parameter is the temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake bottom temperature K This parameter is the temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake cover Dimensionless This parameter is the proportion of a grid box covered by inland water bodies (lakes, reservoirs, rivers and coastal waters). Values vary between 0: no inland water, and 1: grid box is fully covered with inland water. This parameter is specified from observations and does not vary in time. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake cover Dimensionless This parameter is the proportion of a grid box covered by inland water bodies (lakes, reservoirs, rivers and coastal waters). Values vary between 0: no inland water, and 1: grid box is fully covered with inland water. This parameter is specified from observations and does not vary in time. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth m This parameter is the mean depth of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is specified from in-situ measurements and indirect estimates and does not vary in time. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth m This parameter is the mean depth of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is specified from in-situ measurements and indirect estimates and does not vary in time. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake ice depth m This parameter is the thickness of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter is the thickness of that ice layer. Lake ice depth m This parameter is the thickness of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter is the thickness of that ice layer. Lake ice temperature K This parameter is the temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). It is the temperature at the ice/atmosphere or ice/snow interface. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake ice temperature K This parameter is the temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). It is the temperature at the ice/atmosphere or ice/snow interface. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer depth m This parameter is the thickness of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake mix-layer depth m This parameter is the thickness of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake mix-layer temperature K This parameter is the temperature of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer temperature K This parameter is the temperature of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake shape factor Dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) i.e., it describes the shape of the vertical temperature profile. It is used to calculate the lake bottom temperature and other lake-related parameters. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake shape factor Dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) i.e., it describes the shape of the vertical temperature profile. It is used to calculate the lake bottom temperature and other lake-related parameters. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake total layer temperature K This parameter is the mean temperature of the total water column in inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. This parameter is the mean temperature over the two layers. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake total layer temperature K This parameter is the mean temperature of the total water column in inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. This parameter is the mean temperature over the two layers. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Land-sea mask Dimensionless This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless. In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water. In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water. This parameter does not vary in time. Land-sea mask Dimensionless This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless. In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water. In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water. This parameter does not vary in time. Large scale rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Rainfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Rainfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation fraction s This parameter is the accumulation of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Large-scale precipitation fraction s This parameter is the accumulation of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Large-scale snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Leaf area index, high vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "high". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Leaf area index, high vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "high". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Leaf area index, low vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "low". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Leaf area index, low vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "low". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Low cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a "standard atmosphere")). Assumptions are made about the degree of overlap/randomness between clouds in different model levels. This parameter has values from 0 to 1. Low cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a "standard atmosphere")). Assumptions are made about the degree of overlap/randomness between clouds in different model levels. This parameter has values from 0 to 1. Low vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "low". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Low vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "low". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Maximum 2m temperature since previous post-processing K This parameter is the highest temperature of air at 2m above the surface of land, sea or inland water since the parameter was last archived in a particular forecast. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Maximum 2m temperature since previous post-processing K This parameter is the highest temperature of air at 2m above the surface of land, sea or inland water since the parameter was last archived in a particular forecast. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Maximum individual wave height m This parameter is an estimate of the height of the expected highest individual wave within a 20 minute time window. It can be used as a guide to the likelihood of extreme or freak waves. The interactions between waves are non-linear and occasionally concentrate wave energy giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered as a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Maximum individual wave height m This parameter is an estimate of the height of the expected highest individual wave within a 20 minute time window. It can be used as a guide to the likelihood of extreme or freak waves. The interactions between waves are non-linear and occasionally concentrate wave energy giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered as a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Maximum total precipitation rate since previous post-processing kg m-2 s-1 The total precipitation is calculated from the combined large-scale and convective rainfall and snowfall rates every time step and the maximum is kept since the last postprocessing. Maximum total precipitation rate since previous post-processing kg m-2 s-1 The total precipitation is calculated from the combined large-scale and convective rainfall and snowfall rates every time step and the maximum is kept since the last postprocessing. Mean boundary layer dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean boundary layer dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean convective precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean direction of total swell degrees This parameter is the mean direction of waves associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of total swell degrees This parameter is the mean direction of waves associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of wind waves degrees The mean direction of waves generated by local winds. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea wave spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of wind waves degrees The mean direction of waves generated by local winds. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea wave spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean eastward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean eastward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean evaporation rate kg m-2 s-1 This parameter is the amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Mean evaporation rate kg m-2 s-1 This parameter is the amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Mean gravity wave dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean gravity wave dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean large-scale precipitation fraction Dimensionless This parameter is the mean of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean large-scale precipitation fraction Dimensionless This parameter is the mean of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean large-scale precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean northward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean northward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean period of total swell s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea associated with swell, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. Mean period of total swell s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea associated with swell, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. Mean period of wind waves s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea generated by local winds, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea spectrum. Mean period of wind waves s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea generated by local winds, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea spectrum. Mean potential evaporation rate kg m-2 s-1 This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean potential evaporation rate kg m-2 s-1 This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean sea level pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of mean sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at mean sea level. It is calculated over all surfaces - land, sea and inland water. Maps of mean sea level pressure are used to identify the locations of low and high pressure weather systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds. The units of this parameter are pascals (Pa). Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Mean sea level pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of mean sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at mean sea level. It is calculated over all surfaces - land, sea and inland water. Maps of mean sea level pressure are used to identify the locations of low and high pressure weather systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds. The units of this parameter are pascals (Pa). Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Mean snow evaporation rate kg m-2 s-1 This parameter is the average rate of snow evaporation from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snow evaporation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Mean snow evaporation rate kg m-2 s-1 This parameter is the average rate of snow evaporation from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snow evaporation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Mean snowfall rate kg m-2 s-1 This parameter is the rate of snowfall at the Earth's surface. It is the sum of large-scale and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean snowfall rate kg m-2 s-1 This parameter is the rate of snowfall at the Earth's surface. It is the sum of large-scale and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the snowfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean snowmelt rate kg m-2 s-1 This parameter is the rate of snow melt in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the melting would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean snowmelt rate kg m-2 s-1 This parameter is the rate of snow melt in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the melting would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean square slope of waves Dimensionless This parameter can be related analytically to the average slope of combined wind-sea and swell waves. It can also be expressed as a function of wind speed under some statistical assumptions. The higher the slope, the steeper the waves. This parameter indicates the roughness of the sea/ocean surface which affects the interaction between ocean and atmosphere. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. Mean square slope of waves Dimensionless This parameter can be related analytically to the average slope of combined wind-sea and swell waves. It can also be expressed as a function of wind speed under some statistical assumptions. The higher the slope, the steeper the waves. This parameter indicates the roughness of the sea/ocean surface which affects the interaction between ocean and atmosphere. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. Mean sub-surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean sub-surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface direct short-wave radiation flux W m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux W m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux, clear sky W m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux, clear sky W m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward UV radiation flux W m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward UV radiation flux W m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux, clear sky W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux, clear sky W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface latent heat flux W m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface latent heat flux W m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux, clear sky W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux, clear sky W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top downward short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top downward short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux W m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux W m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux, clear sky W m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean top net long-wave radiation flux, clear sky W m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. Mean top net short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux, clear sky W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux, clear sky W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. The ECMWF convention for vertical fluxes is positive downwards. Mean total precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface. It is the sum of the rates due to large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean total precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface. It is the sum of the rates due to large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean vertical gradient of refractivity inside trapping layer m-1 Mean vertical gradient of atmospheric refractivity inside the trapping layer. Mean vertical gradient of refractivity inside trapping layer m-1 Mean vertical gradient of atmospheric refractivity inside the trapping layer. Mean vertically integrated moisture divergence kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean vertically integrated moisture divergence kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the reanalysis, the processing period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the processing period is over the 3 hours ending at the validity date and time. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean wave direction degree true This parameter is the mean direction of ocean/sea surface waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use this type of wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction degree true This parameter is the mean direction of ocean/sea surface waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use this type of wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of first swell partition degrees This parameter is the mean direction of waves in the first swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of first swell partition degrees This parameter is the mean direction of waves in the first swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of second swell partition degrees This parameter is the mean direction of waves in the second swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of second swell partition degrees This parameter is the mean direction of waves in the second swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of third swell partition degrees This parameter is the mean direction of waves in the third swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of third swell partition degrees This parameter is the mean direction of waves in the third swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave period s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use such wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. Mean wave period s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use such wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. Mean wave period based on first moment s This parameter is the reciprocal of the mean frequency of the wave components that represent the sea state. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment s This parameter is the reciprocal of the mean frequency of the wave components that represent the sea state. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for swell s This parameter is the reciprocal of the mean frequency of the wave components associated with swell. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for swell s This parameter is the reciprocal of the mean frequency of the wave components associated with swell. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for wind waves s This parameter is the reciprocal of the mean frequency of the wave components generated by local winds. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with wind waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for wind waves s This parameter is the reciprocal of the mean frequency of the wave components generated by local winds. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with wind waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for swell s This parameter is equivalent to the zero-crossing mean wave period for swell. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for swell s This parameter is equivalent to the zero-crossing mean wave period for swell. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for wind waves s This parameter is equivalent to the zero-crossing mean wave period for waves generated by local winds. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for wind waves s This parameter is equivalent to the zero-crossing mean wave period for waves generated by local winds. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period of first swell partition s This parameter is the mean period of waves in the first swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of first swell partition s This parameter is the mean period of waves in the first swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of second swell partition s This parameter is the mean period of waves in the second swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of second swell partition s This parameter is the mean period of waves in the second swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of third swell partition s This parameter is the mean period of waves in the third swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of third swell partition s This parameter is the mean period of waves in the third swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third swell partition might be from one system at one location and a different system at the neighbouring location). Mean zero-crossing wave period s This parameter represents the mean length of time between occasions where the sea/ocean surface crosses mean sea level. In combination with wave height information, it could be used to assess the length of time that a coastal structure might be under water, for example. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). In the ECMWF Integrated Forecasting System (IFS) this parameter is calculated from the characteristics of the two-dimensional wave spectrum. Mean zero-crossing wave period s This parameter represents the mean length of time between occasions where the sea/ocean surface crosses mean sea level. In combination with wave height information, it could be used to assess the length of time that a coastal structure might be under water, for example. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). In the ECMWF Integrated Forecasting System (IFS) this parameter is calculated from the characteristics of the two-dimensional wave spectrum. Medium cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a "standard atmosphere")). The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. Medium cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a "standard atmosphere")). The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. Minimum 2m temperature since previous post-processing K This parameter is the lowest temperature of air at 2m above the surface of land, sea or inland waters since the parameter was last archived in a particular forecast. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. See further information. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Minimum 2m temperature since previous post-processing K This parameter is the lowest temperature of air at 2m above the surface of land, sea or inland waters since the parameter was last archived in a particular forecast. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. See further information. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Minimum total precipitation rate since previous post-processing kg m-2 s-1 The total precipitation is calculated from the combined large-scale and convective rainfall and snowfall rates every time step and the minimum is kept since the last postprocessing. Minimum total precipitation rate since previous post-processing kg m-2 s-1 The total precipitation is calculated from the combined large-scale and convective rainfall and snowfall rates every time step and the minimum is kept since the last postprocessing. Minimum vertical gradient of refractivity inside trapping layer m-1 Minimum vertical gradient of atmospheric refractivity inside the trapping layer. Minimum vertical gradient of refractivity inside trapping layer m-1 Minimum vertical gradient of atmospheric refractivity inside the trapping layer. Model bathymetry m This parameter is the depth of water from the surface to the bottom of the ocean. It is used by the ocean wave model to specify the propagation properties of the different waves that could be present. Note that the ocean wave model grid is too coarse to resolve some small islands and mountains on the bottom of the ocean, but they can have an impact on surface ocean waves. The ocean wave model has been modified to reduce the wave energy flowing around or over features at spatial scales smaller than the grid box. Model bathymetry m This parameter is the depth of water from the surface to the bottom of the ocean. It is used by the ocean wave model to specify the propagation properties of the different waves that could be present. Note that the ocean wave model grid is too coarse to resolve some small islands and mountains on the bottom of the ocean, but they can have an impact on surface ocean waves. The ocean wave model has been modified to reduce the wave energy flowing around or over features at spatial scales smaller than the grid box. Near IR albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Normalized energy flux into ocean Dimensionless This parameter is the normalised vertical flux of turbulent kinetic energy from ocean waves into the ocean. The energy flux is calculated from an estimation of the loss of wave energy due to white capping waves. A white capping wave is one that appears white at its crest as it breaks, due to air being mixed into the water. When waves break in this way, there is a transfer of energy from the waves to the ocean. Such a flux is defined to be negative. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into ocean Dimensionless This parameter is the normalised vertical flux of turbulent kinetic energy from ocean waves into the ocean. The energy flux is calculated from an estimation of the loss of wave energy due to white capping waves. A white capping wave is one that appears white at its crest as it breaks, due to air being mixed into the water. When waves break in this way, there is a transfer of energy from the waves to the ocean. Such a flux is defined to be negative. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into waves Dimensionless This parameter is the normalised vertical flux of energy from wind into the ocean waves. A positive flux implies a flux into the waves. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into waves Dimensionless This parameter is the normalised vertical flux of energy from wind into the ocean waves. A positive flux implies a flux into the waves. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized stress into ocean Dimensionless This parameter is the normalised surface stress, or momentum flux, from the air into the ocean due to turbulence at the air-sea interface and breaking waves. It does not include the flux used to generate waves. The ECMWF convention for vertical fluxes is positive downwards. The stress has units of Newtons per metre squared, and this is normalised by being divided by the product of air density and the square of the friction velocity. Normalized stress into ocean Dimensionless This parameter is the normalised surface stress, or momentum flux, from the air into the ocean due to turbulence at the air-sea interface and breaking waves. It does not include the flux used to generate waves. The ECMWF convention for vertical fluxes is positive downwards. The stress has units of Newtons per metre squared, and this is normalised by being divided by the product of air density and the square of the friction velocity. Northward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Northward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Northward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Northward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Ocean surface stress equivalent 10m neutral wind direction degrees This parameter is the direction from which the "neutral wind" blows, in degrees clockwise from true north, at a height of ten metres above the surface of the Earth. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind direction used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind direction degrees This parameter is the direction from which the "neutral wind" blows, in degrees clockwise from true north, at a height of ten metres above the surface of the Earth. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind direction used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind speed m s-1 This parameter is the horizontal speed of the "neutral wind", at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind speed used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind speed m s-1 This parameter is the horizontal speed of the "neutral wind", at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind speed used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Period corresponding to maximum individual wave height s This parameter is the period of the expected highest individual wave within a 20-minute time window. It can be used as a guide to the characteristics of extreme or freak waves. Wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. Occasionally waves of different periods reinforce and interact non-linearly giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered to be a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Period corresponding to maximum individual wave height s This parameter is the period of the expected highest individual wave within a 20-minute time window. It can be used as a guide to the characteristics of extreme or freak waves. Wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. Occasionally waves of different periods reinforce and interact non-linearly giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered to be a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Potential evaporation m This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Potential evaporation m This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Precipitation type Dimensionless This parameter describes the type of precipitation at the surface, at the specified time. A precipitation type is assigned wherever there is a non-zero value of precipitation. In the ECMWF Integrated Forecasting System (IFS) there are only two predicted precipitation variables: rain and snow. Precipitation type is derived from these two predicted variables in combination with atmospheric conditions, such as temperature. Values of precipitation type defined in the IFS: 0: No precipitation, 1: Rain, 3: Freezing rain (i.e. supercooled raindrops which freeze on contact with the ground and other surfaces), 5: Snow, 6: Wet snow (i.e. snow particles which are starting to melt); 7: Mixture of rain and snow, 8: Ice pellets. These precipitation types are consistent with WMO Code Table 4.201. Other types in this WMO table are not defined in the IFS. Precipitation type Dimensionless This parameter describes the type of precipitation at the surface, at the specified time. A precipitation type is assigned wherever there is a non-zero value of precipitation. In the ECMWF Integrated Forecasting System (IFS) there are only two predicted precipitation variables: rain and snow. Precipitation type is derived from these two predicted variables in combination with atmospheric conditions, such as temperature. Values of precipitation type defined in the IFS: 0: No precipitation, 1: Rain, 3: Freezing rain (i.e. supercooled raindrops which freeze on contact with the ground and other surfaces), 5: Snow, 6: Wet snow (i.e. snow particles which are starting to melt); 7: Mixture of rain and snow, 8: Ice pellets. These precipitation types are consistent with WMO Code Table 4.201. Other types in this WMO table are not defined in the IFS. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Sea surface temperature K This parameter (SST) is the temperature of sea water near the surface. In ERA5, this parameter is a foundation SST, which means there are no variations due to the daily cycle of the sun (diurnal variations). SST, in ERA5, is given by two external providers. Before September 2007, SST from the HadISST2 dataset is used and from September 2007 onwards, the OSTIA dataset is used. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Sea surface temperature K This parameter (SST) is the temperature of sea water near the surface. In ERA5, this parameter is a foundation SST, which means there are no variations due to the daily cycle of the sun (diurnal variations). SST, in ERA5, is given by two external providers. Before September 2007, SST from the HadISST2 dataset is used and from September 2007 onwards, the OSTIA dataset is used. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Sea-ice cover Dimensionless This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land-sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover. In ERA5, sea-ice cover is given by two external providers. Before 1979 the HadISST2 dataset is used. From 1979 to August 2007 the OSI SAF (409a) dataset is used and from September 2007 the OSI SAF oper dataset is used. Sea ice is frozen sea water which floats on the surface of the ocean. Sea ice does not include ice which forms on land such as glaciers, icebergs and ice-sheets. It also excludes ice shelves which are anchored on land, but protrude out over the surface of the ocean. These phenomena are not modelled by the IFS. Long-term monitoring of sea ice is important for understanding climate change. Sea ice also affects shipping routes through the polar regions. Sea-ice cover Dimensionless This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land-sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover. In ERA5, sea-ice cover is given by two external providers. Before 1979 the HadISST2 dataset is used. From 1979 to August 2007 the OSI SAF (409a) dataset is used and from September 2007 the OSI SAF oper dataset is used. Sea ice is frozen sea water which floats on the surface of the ocean. Sea ice does not include ice which forms on land such as glaciers, icebergs and ice-sheets. It also excludes ice shelves which are anchored on land, but protrude out over the surface of the ocean. These phenomena are not modelled by the IFS. Long-term monitoring of sea ice is important for understanding climate change. Sea ice also affects shipping routes through the polar regions. Significant height of combined wind waves and swell m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. This parameter can be used to assess sea state and swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of combined wind waves and swell m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. This parameter can be used to assess sea state and swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of total swell m This parameter represents the average height of the highest third of surface ocean/sea waves associated with swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of total swell only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional total swell spectrum. The total swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of total swell m This parameter represents the average height of the highest third of surface ocean/sea waves associated with swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of total swell only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional total swell spectrum. The total swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of wind waves m This parameter represents the average height of the highest third of surface ocean/sea waves generated by the local wind. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wind-sea wave spectrum. The wind-sea wave spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are still under the influence of the local wind. This parameter can be used to assess wind-sea waves. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of wind waves m This parameter represents the average height of the highest third of surface ocean/sea waves generated by the local wind. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wind-sea wave spectrum. The wind-sea wave spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are still under the influence of the local wind. This parameter can be used to assess wind-sea waves. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of first swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the first swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of first swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the first swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of second swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the second swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of second swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the second swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of third swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the third swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of third swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the third swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Skin reservoir content m of water equivalent This parameter is the amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of "skin reservoir content" a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the "skin reservoir" by evaporation. Skin reservoir content m of water equivalent This parameter is the amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of "skin reservoir content" a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the "skin reservoir" by evaporation. Skin temperature K This parameter is the temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Skin temperature K This parameter is the temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Slope of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, angle and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the slope of the sub-grid valleys, hills and mountains. A flat surface has a value of 0, and a 45 degree slope has a value of 0.5. This parameter does not vary in time. Slope of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, angle and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the slope of the sub-grid valleys, hills and mountains. A flat surface has a value of 0, and a 45 degree slope has a value of 0.5. This parameter does not vary in time. Snow albedo Dimensionless This parameter is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter changes with snow age and also depends on vegetation height. It has a range of values between 0 and 1. For low vegetation, it ranges between 0.52 for old snow and 0.88 for fresh snow. For high vegetation with snow underneath, it depends on vegetation type and has values between 0.27 and 0.38. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. Snow albedo Dimensionless This parameter is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter changes with snow age and also depends on vegetation height. It has a range of values between 0 and 1. For low vegetation, it ranges between 0.52 for old snow and 0.88 for fresh snow. For high vegetation with snow underneath, it depends on vegetation type and has values between 0.27 and 0.38. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. Snow density kg m-3 This parameter is the mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. Snow density kg m-3 This parameter is the mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. Snow depth m of water equivalent This parameter is the amount of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow depth m of water equivalent This parameter is the amount of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from snow from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the evaporated snow (from the snow-covered area of a grid box) were liquid and were spread evenly over the whole grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Snow evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from snow from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the evaporated snow (from the snow-covered area of a grid box) were liquid and were spread evenly over the whole grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Snowmelt m of water equivalent This parameter is the accumulated amount of water that has melted from snow in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the melted snow (from the snow-covered area of a grid box) were spread evenly over the whole grid box. For example, if half the grid box were covered in snow with a water equivalent depth of 0.02m, this parameter would have a value of 0.01m. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Snowmelt m of water equivalent This parameter is the accumulated amount of water that has melted from snow in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the melted snow (from the snow-covered area of a grid box) were spread evenly over the whole grid box. For example, if half the grid box were covered in snow with a water equivalent depth of 0.02m, this parameter would have a value of 0.01m. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. Soil temperature level 1 K This parameter is the temperature of the soil at level 1 (in the middle of layer 1). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 1 K This parameter is the temperature of the soil at level 1 (in the middle of layer 1). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K This parameter is the temperature of the soil at level 2 (in the middle of layer 2). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K This parameter is the temperature of the soil at level 2 (in the middle of layer 2). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 3 K This parameter is the temperature of the soil at level 3 (in the middle of layer 3). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 3 K This parameter is the temperature of the soil at level 3 (in the middle of layer 3). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 4 K This parameter is the temperature of the soil at level 4 (in the middle of layer 4). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 4 K This parameter is the temperature of the soil at level 4 (in the middle of layer 4). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil type Dimensionless This parameter is the texture (or classification) of soil used by the land surface scheme of the ECMWF Integrated Forecasting System (IFS) to predict the water holding capacity of soil in soil moisture and runoff calculations. It is derived from the root zone data (30-100 cm below the surface) of the FAO/UNESCO Digital Soil Map of the World, DSMW (FAO, 2003), which exists at a resolution of 5' X 5' (about 10 km). The seven soil types are: 1: Coarse, 2: Medium, 3: Medium fine, 4: Fine, 5: Very fine, 6: Organic, 7: Tropical organic. A value of 0 indicates a non-land point. This parameter does not vary in time. Soil type Dimensionless This parameter is the texture (or classification) of soil used by the land surface scheme of the ECMWF Integrated Forecasting System (IFS) to predict the water holding capacity of soil in soil moisture and runoff calculations. It is derived from the root zone data (30-100 cm below the surface) of the FAO/UNESCO Digital Soil Map of the World, DSMW (FAO, 2003), which exists at a resolution of 5' X 5' (about 10 km). The seven soil types are: 1: Coarse, 2: Medium, 3: Medium fine, 4: Fine, 5: Very fine, 6: Organic, 7: Tropical organic. A value of 0 indicates a non-land point. This parameter does not vary in time. Standard deviation of filtered subgrid orography m Climatological parameter (scales between approximately 3 and 22 km are included). This parameter does not vary in time. Standard deviation of filtered subgrid orography m Climatological parameter (scales between approximately 3 and 22 km are included). This parameter does not vary in time. Standard deviation of orography Dimensionless This parameter is one of four parameters (the others being angle of sub-gridscale orography, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the standard deviation of the height of the sub-grid valleys, hills and mountains within a grid box. This parameter does not vary in time. Standard deviation of orography Dimensionless This parameter is one of four parameters (the others being angle of sub-gridscale orography, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the standard deviation of the height of the sub-grid valleys, hills and mountains within a grid box. This parameter does not vary in time. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface latent heat flux J m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface latent heat flux J m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation, clear sky J m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation, clear sky J m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation, clear sky J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation, clear sky J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of land, sea and inland water. It is a measure of the weight of all the air in a column vertically above a point on the Earth's surface. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure weather systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa). Surface pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of land, sea and inland water. It is a measure of the weight of all the air in a column vertically above a point on the Earth's surface. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure weather systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa). Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface sensible heat flux J m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface sensible heat flux J m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downward, clear sky J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downward, clear sky J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downward, clear sky J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downward, clear sky J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. TOA incident solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. TOA incident solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Temperature of snow layer K This parameter gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Temperature of snow layer K This parameter gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the snow depth (m of water equivalent) is greater than 0.0. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Top net solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation, clear sky J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation, clear sky J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation J m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation J m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation, clear sky J m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). Note that OLR is typically shown in units of watts per square metre (W m-2 ). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. Top net thermal radiation, clear sky J m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). Note that OLR is typically shown in units of watts per square metre (W m-2 ). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. Total cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights. Cloud fractions vary from 0 to 1. Total cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights. Cloud fractions vary from 0 to 1. Total column cloud ice water kg m-2 This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud ice water kg m-2 This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud liquid water kg m-2 This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud liquid water kg m-2 This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can also be referred to as total ozone, or vertically integrated ozone. The values are dominated by ozone within the stratosphere. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. In the IFS, the units for total ozone are kilograms per square metre, but before 12/06/2001 dobson units were used. Dobson units (DU) are still used extensively for total column ozone. 1 DU = 2.1415E-5 kg m-2 Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can also be referred to as total ozone, or vertically integrated ozone. The values are dominated by ozone within the stratosphere. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. In the IFS, the units for total ozone are kilograms per square metre, but before 12/06/2001 dobson units were used. Dobson units (DU) are still used extensively for total column ozone. 1 DU = 2.1415E-5 kg m-2 Total column rain water kg m-2 This parameter is the total amount of water in droplets of raindrop size (which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column rain water kg m-2 This parameter is the total amount of water in droplets of raindrop size (which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column snow water kg m-2 This parameter is the total amount of water in the form of snow (aggregated ice crystals which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column snow water kg m-2 This parameter is the total amount of water in the form of snow (aggregated ice crystals which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column supercooled liquid water kg m-2 This parameter is the total amount of supercooled water in a column extending from the surface of the Earth to the top of the atmosphere. Supercooled water is water that exists in liquid form below 0oC. It is common in cold clouds and is important in the formation of precipitation. Also, supercooled water in clouds extending to the surface (i.e., fog) can cause icing/riming of various structures. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column supercooled liquid water kg m-2 This parameter is the total amount of supercooled water in a column extending from the surface of the Earth to the top of the atmosphere. Supercooled water is water that exists in liquid form below 0oC. It is common in cold clouds and is important in the formation of precipitation. Also, supercooled water in clouds extending to the surface (i.e., fog) can cause icing/riming of various structures. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column water kg m-2 This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for. Total column water kg m-2 This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for. Total column water vapour kg m-2 This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Total column water vapour kg m-2 This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Total precipitation m This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Total precipitation m This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Total sky direct solar radiation at surface J m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Total sky direct solar radiation at surface J m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Total totals index K This parameter gives an indication of the probability of occurrence of a thunderstorm and its severity by using the vertical gradient of temperature and humidity. The values of this index indicate the following: <44 Thunderstorms not likely, 44-50 Thunderstorms likely, 51-52 Isolated severe thunderstorms, 53-56 Widely scattered severe thunderstorms, 56-60 Scattered severe thunderstorms more likely. The total totals index is the temperature difference between 850 hPa (near surface) and 500 hPa (mid-troposphere) (lapse rate) plus a measure of the moisture content between 850 hPa and 500 hPa. The probability of deep convection tends to increase with increasing lapse rate and atmospheric moisture content. There are a number of limitations to this index. Also, the interpretation of the index value varies with season and location. Total totals index K This parameter gives an indication of the probability of occurrence of a thunderstorm and its severity by using the vertical gradient of temperature and humidity. The values of this index indicate the following: <44 Thunderstorms not likely, 44-50 Thunderstorms likely, 51-52 Isolated severe thunderstorms, 53-56 Widely scattered severe thunderstorms, 56-60 Scattered severe thunderstorms more likely. The total totals index is the temperature difference between 850 hPa (near surface) and 500 hPa (mid-troposphere) (lapse rate) plus a measure of the moisture content between 850 hPa and 500 hPa. The probability of deep convection tends to increase with increasing lapse rate and atmospheric moisture content. There are a number of limitations to this index. Also, the interpretation of the index value varies with season and location. Trapping layer base height m Trapping layer base height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer base height m Trapping layer base height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer top height m Trapping layer top height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer top height m Trapping layer top height as diagnosed from the vertical gradient of atmospheric refractivity. Type of high vegetation Dimensionless This parameter indicates the 6 types of high vegetation recognised by the ECMWF Integrated Forecasting System: 3 = Evergreen needleleaf trees, 4 = Deciduous needleleaf trees, 5 = Deciduous broadleaf trees, 6 = Evergreen broadleaf trees, 18 = Mixed forest/woodland, 19 = Interrupted forest. A value of 0 indicates a point without high vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of high vegetation Dimensionless This parameter indicates the 6 types of high vegetation recognised by the ECMWF Integrated Forecasting System: 3 = Evergreen needleleaf trees, 4 = Deciduous needleleaf trees, 5 = Deciduous broadleaf trees, 6 = Evergreen broadleaf trees, 18 = Mixed forest/woodland, 19 = Interrupted forest. A value of 0 indicates a point without high vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of low vegetation Dimensionless This parameter indicates the 10 types of low vegetation recognised by the ECMWF Integrated Forecasting System: 1 = Crops, Mixed farming, 2 = Grass, 7 = Tall grass, 9 = Tundra, 10 = Irrigated crops, 11 = Semidesert, 13 = Bogs and marshes, 16 = Evergreen shrubs, 17 = Deciduous shrubs, 20 = Water and land mixtures. A value of 0 indicates a point without low vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of low vegetation Dimensionless This parameter indicates the 10 types of low vegetation recognised by the ECMWF Integrated Forecasting System: 1 = Crops, Mixed farming, 2 = Grass, 7 = Tall grass, 9 = Tundra, 10 = Irrigated crops, 11 = Semidesert, 13 = Bogs and marshes, 16 = Evergreen shrubs, 17 = Deciduous shrubs, 20 = Water and land mixtures. A value of 0 indicates a point without low vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. U-component stokes drift m s-1 This parameter is the eastward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. U-component stokes drift m s-1 This parameter is the eastward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. UV visible albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. This parameter varies between 0 and 1. UV visible albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. This parameter varies between 0 and 1. UV visible albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. UV visible albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. V-component stokes drift m s-1 This parameter is the northward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. V-component stokes drift m s-1 This parameter is the northward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. Vertical integral of divergence of cloud frozen water flux kg m-2 s-1 The vertical integral of the cloud frozen water flux is the horizontal rate of flow of cloud frozen water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud frozen water spreading outward from a point, per square metre. This parameter is positive for cloud frozen water that is spreading out, or diverging, and negative for the opposite, for cloud frozen water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud frozen water. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of divergence of cloud frozen water flux kg m-2 s-1 The vertical integral of the cloud frozen water flux is the horizontal rate of flow of cloud frozen water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud frozen water spreading outward from a point, per square metre. This parameter is positive for cloud frozen water that is spreading out, or diverging, and negative for the opposite, for cloud frozen water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud frozen water. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of divergence of cloud liquid water flux kg m-2 s-1 The vertical integral of the cloud liquid water flux is the horizontal rate of flow of cloud liquid water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud liquid water spreading outward from a point, per square metre. This parameter is positive for cloud liquid water that is spreading out, or diverging, and negative for the opposite, for cloud liquid water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud liquid water. Vertical integral of divergence of cloud liquid water flux kg m-2 s-1 The vertical integral of the cloud liquid water flux is the horizontal rate of flow of cloud liquid water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud liquid water spreading outward from a point, per square metre. This parameter is positive for cloud liquid water that is spreading out, or diverging, and negative for the opposite, for cloud liquid water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud liquid water. Vertical integral of divergence of geopotential flux W m-2 The vertical integral of the geopotential flux is the horizontal rate of flow of geopotential, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of geopotential spreading outward from a point, per square metre. This parameter is positive for geopotential that is spreading out, or diverging, and negative for the opposite, for geopotential that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of geopotential. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of geopotential flux W m-2 The vertical integral of the geopotential flux is the horizontal rate of flow of geopotential, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of geopotential spreading outward from a point, per square metre. This parameter is positive for geopotential that is spreading out, or diverging, and negative for the opposite, for geopotential that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of geopotential. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of kinetic energy flux W m-2 The vertical integral of the kinetic energy flux is the horizontal rate of flow of kinetic energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of kinetic energy spreading outward from a point, per square metre. This parameter is positive for kinetic energy that is spreading out, or diverging, and negative for the opposite, for kinetic energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of kinetic energy. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of kinetic energy flux W m-2 The vertical integral of the kinetic energy flux is the horizontal rate of flow of kinetic energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of kinetic energy spreading outward from a point, per square metre. This parameter is positive for kinetic energy that is spreading out, or diverging, and negative for the opposite, for kinetic energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of kinetic energy. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of mass flux kg m-2 s-1 The vertical integral of the mass flux is the horizontal rate of flow of mass, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of mass spreading outward from a point, per square metre. This parameter is positive for mass that is spreading out, or diverging, and negative for the opposite, for mass that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of mass. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of divergence of mass flux kg m-2 s-1 The vertical integral of the mass flux is the horizontal rate of flow of mass, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of mass spreading outward from a point, per square metre. This parameter is positive for mass that is spreading out, or diverging, and negative for the opposite, for mass that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of mass. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of divergence of moisture flux kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Vertical integral of divergence of moisture flux kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Vertical integral of divergence of ozone flux kg m-2 s-1 The vertical integral of the ozone flux is the horizontal rate of flow of ozone, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of ozone spreading outward from a point, per square metre. This parameter is positive for ozone that is spreading out, or diverging, and negative for the opposite, for ozone that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of ozone. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of divergence of ozone flux kg m-2 s-1 The vertical integral of the ozone flux is the horizontal rate of flow of ozone, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of ozone spreading outward from a point, per square metre. This parameter is positive for ozone that is spreading out, or diverging, and negative for the opposite, for ozone that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of ozone. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of divergence of thermal energy flux W m-2 The vertical integral of the thermal energy flux is the horizontal rate of flow of thermal energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of thermal energy spreading outward from a point, per square metre. This parameter is positive for thermal energy that is spreading out, or diverging, and negative for the opposite, for thermal energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of thermal energy. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the flow of thermal energy through the climate system and to investigate the atmospheric energy budget. Vertical integral of divergence of thermal energy flux W m-2 The vertical integral of the thermal energy flux is the horizontal rate of flow of thermal energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of thermal energy spreading outward from a point, per square metre. This parameter is positive for thermal energy that is spreading out, or diverging, and negative for the opposite, for thermal energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of thermal energy. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the flow of thermal energy through the climate system and to investigate the atmospheric energy budget. Vertical integral of divergence of total energy flux W m-2 The vertical integral of the total energy flux is the horizontal rate of flow of total energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of total energy spreading outward from a point, per square metre. This parameter is positive for total energy that is spreading out, or diverging, and negative for the opposite, for total energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of total energy. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of total energy flux W m-2 The vertical integral of the total energy flux is the horizontal rate of flow of total energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of total energy spreading outward from a point, per square metre. This parameter is positive for total energy that is spreading out, or diverging, and negative for the opposite, for total energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of total energy. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of eastward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of eastward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the eastward direction, per meter across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the eastward direction, per meter across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of eastward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of eastward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from west to east. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of eastward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from west to east. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of eastward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of energy conversion W m-2 This parameter is one contribution to the amount of energy being converted between kinetic energy, and internal plus potential energy, for a column of air extending from the surface of the Earth to the top of the atmosphere. Negative values indicate a conversion to kinetic energy from potential plus internal energy. This parameter can be used to study the atmospheric energy budget. The circulation of the atmosphere can also be considered in terms of energy conversions. Vertical integral of energy conversion W m-2 This parameter is one contribution to the amount of energy being converted between kinetic energy, and internal plus potential energy, for a column of air extending from the surface of the Earth to the top of the atmosphere. Negative values indicate a conversion to kinetic energy from potential plus internal energy. This parameter can be used to study the atmospheric energy budget. The circulation of the atmosphere can also be considered in terms of energy conversions. Vertical integral of kinetic energy J m-2 This parameter is the vertical integral of kinetic energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of kinetic energy J m-2 This parameter is the vertical integral of kinetic energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of mass of atmosphere kg m-2 This parameter is the total mass of air for a column extending from the surface of the Earth to the top of the atmosphere, per square metre. This parameter is calculated by dividing surface pressure by the Earth's gravitational acceleration, g (=9.80665 m s-2 ), and has units of kilograms per square metre. This parameter can be used to study the atmospheric mass budget. Vertical integral of mass of atmosphere kg m-2 This parameter is the total mass of air for a column extending from the surface of the Earth to the top of the atmosphere, per square metre. This parameter is calculated by dividing surface pressure by the Earth's gravitational acceleration, g (=9.80665 m s-2 ), and has units of kilograms per square metre. This parameter can be used to study the atmospheric mass budget. Vertical integral of mass tendency kg m-2 s-1 This parameter is the rate of change of the mass of a column of air extending from the Earth's surface to the top of the atmosphere. An increasing mass of the column indicates rising surface pressure. In contrast, a decrease indicates a falling surface pressure. The mass of the column is calculated by dividing pressure at the Earth's surface by the gravitational acceleration, g (=9.80665 m s-2 ). This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of mass tendency kg m-2 s-1 This parameter is the rate of change of the mass of a column of air extending from the Earth's surface to the top of the atmosphere. An increasing mass of the column indicates rising surface pressure. In contrast, a decrease indicates a falling surface pressure. The mass of the column is calculated by dividing pressure at the Earth's surface by the gravitational acceleration, g (=9.80665 m s-2 ). This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of northward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of northward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from south to north. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of northward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from south to north. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of northward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of potential and internal energy J m-2 This parameter is the mass weighted vertical integral of potential and internal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential and internal energy J m-2 This parameter is the mass weighted vertical integral of potential and internal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential, internal and latent energy J m-2 This parameter is the mass weighted vertical integral of potential, internal and latent energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The latent energy refers to the energy associated with the water vapour in the atmosphere and is equal to the energy required to convert liquid water into water vapour. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential, internal and latent energy J m-2 This parameter is the mass weighted vertical integral of potential, internal and latent energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The latent energy refers to the energy associated with the water vapour in the atmosphere and is equal to the energy required to convert liquid water into water vapour. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of temperature K kg m-2 This parameter is the mass-weighted vertical integral of temperature for a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can be used to study the atmospheric energy budget. Vertical integral of temperature K kg m-2 This parameter is the mass-weighted vertical integral of temperature for a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can be used to study the atmospheric energy budget. Vertical integral of thermal energy J m-2 This parameter is the mass-weighted vertical integral of thermal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Thermal energy is calculated from the product of temperature and the specific heat capacity of air at constant pressure. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of thermal energy J m-2 This parameter is the mass-weighted vertical integral of thermal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Thermal energy is calculated from the product of temperature and the specific heat capacity of air at constant pressure. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of total energy J m-2 This parameter is the vertical integral of total energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of total energy J m-2 This parameter is the vertical integral of total energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertically integrated moisture divergence kg m-2 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm. Vertically integrated moisture divergence kg m-2 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is accumulated over a particular time period which depends on the data extracted. For the reanalysis, the accumulation period is over the 1 hour ending at the validity date and time. For the ensemble members, ensemble mean and ensemble spread, the accumulation period is over the 3 hours ending at the validity date and time. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm. Volumetric soil water layer 1 m3 m-3 This parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 m3 m-3 This parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 This parameter is the volume of water in soil layer 2 (7 - 28cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 This parameter is the volume of water in soil layer 2 (7 - 28cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 3 m3 m-3 This parameter is the volume of water in soil layer 3 (28 - 100cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 3 m3 m-3 This parameter is the volume of water in soil layer 3 (28 - 100cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 4 m3 m-3 This parameter is the volume of water in soil layer 4 (100 - 289cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 4 m3 m-3 This parameter is the volume of water in soil layer 4 (100 - 289cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Wave spectral directional width Dimensionless This parameter indicates whether waves (generated by local winds and associated with swell) are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width Dimensionless This parameter indicates whether waves (generated by local winds and associated with swell) are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for swell Dimensionless This parameter indicates whether waves associated with swell are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for swell Dimensionless This parameter indicates whether waves associated with swell are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for wind waves Dimensionless This parameter indicates whether waves generated by the local wind are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for wind waves Dimensionless This parameter indicates whether waves generated by the local wind are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral kurtosis Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It shows how much of the probability density function of the sea surface elevation exists in the tails of the distribution. So, a positive kurtosis (typical range 0.0 to 0.06) means more frequent occurrences of very extreme values (either above or below the mean), relative to a normal distribution. Wave spectral kurtosis Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It shows how much of the probability density function of the sea surface elevation exists in the tails of the distribution. So, a positive kurtosis (typical range 0.0 to 0.06) means more frequent occurrences of very extreme values (either above or below the mean), relative to a normal distribution. Wave spectral peakedness Dimensionless This parameter is a statistical measure used to forecast extreme or freak waves. It is a measure of the relative width of the ocean/sea wave frequency spectrum (i.e., whether the ocean/sea wave field is made up of a narrow or broad range of frequencies). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). When the wave field is more focussed around a narrow range of frequencies, the probability of freak/extreme waves increases. This parameter is Goda's peakedness factor and is used to calculate the Benjamin-Feir Index (BFI). The BFI is in turn used to estimate the probability and nature of extreme/freak waves. Wave spectral peakedness Dimensionless This parameter is a statistical measure used to forecast extreme or freak waves. It is a measure of the relative width of the ocean/sea wave frequency spectrum (i.e., whether the ocean/sea wave field is made up of a narrow or broad range of frequencies). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). When the wave field is more focussed around a narrow range of frequencies, the probability of freak/extreme waves increases. This parameter is Goda's peakedness factor and is used to calculate the Benjamin-Feir Index (BFI). The BFI is in turn used to estimate the probability and nature of extreme/freak waves. Wave spectral skewness Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It is a measure of the asymmetry of the probability density function of the sea surface elevation. So, a positive/negative skewness (typical range -0.2 to 0.12) means more frequent occurrences of extreme values above/below the mean, relative to a normal distribution. Wave spectral skewness Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It is a measure of the asymmetry of the probability density function of the sea surface elevation. So, a positive/negative skewness (typical range -0.2 to 0.12) means more frequent occurrences of extreme values above/below the mean, relative to a normal distribution. Zero degree level m The height above the Earth's surface where the temperature passes from positive to negative values, corresponding to the top of a warm layer, at the specified time. This parameter can be used to help forecast snow. If more than one warm layer is encountered, then the zero degree level corresponds to the top of the second atmospheric layer. This parameter is set to zero when the temperature in the whole atmosphere is below 0℃. Zero degree level m The height above the Earth's surface where the temperature passes from positive to negative values, corresponding to the top of a warm layer, at the specified time. This parameter can be used to help forecast snow. If more than one warm layer is encountered, then the zero degree level corresponds to the top of the second atmospheric layer. This parameter is set to zero when the temperature in the whole atmosphere is below 0℃. 365 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-waves-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_ANALYSISFORECAST_WAV_001_027 Global Ocean Waves Analysis and Forecast Short description: The operational global ocean analysis and forecast system of Météo-France with a resolution of 1/12 degree is providing daily analyses and 10 days forecasts for the global ocean sea surface waves. This product includes 3-hourly instantaneous fields of integrated wave parameters from the total spectrum (significant height, period, direction, Stokes drift,...etc), as well as the following partitions: the wind wave, the primary and secondary swell waves. The global wave system of Météo-France is based on the wave model MFWAM which is a third generation wave model. MFWAM uses the computing code ECWAM-IFS-38R2 with a dissipation terms developed by Ardhuin et al. (2010). The model MFWAM was upgraded on november 2014 thanks to improvements obtained from the european research project « my wave » (Janssen et al. 2014). The model mean bathymetry is generated by using 2-minute gridded global topography data ETOPO2/NOAA. Native model grid is irregular with decreasing distance in the latitudinal direction close to the poles. At the equator the distance in the latitudinal direction is more or less fixed with grid size 1/10°. The operational model MFWAM is driven by 6-hourly analysis and 3-hourly forecasted winds from the IFS-ECMWF atmospheric system. The wave spectrum is discretized in 24 directions and 30 frequencies starting from 0.035 Hz to 0.58 Hz. The model MFWAM uses the assimilation of altimeters with a time step of 6 hours. The global wave system provides analysis 4 times a day, and a forecast of 10 days at 0:00 UTC. The wave model MFWAM uses the partitioning to split the swell spectrum in primary and secondary swells. DOI (product) :https://doi.org/10.48670/moi-00017 https://doi.org/10.48670/moi-00017 366 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/clcbackbone-2018-raster-10-m-europe-3-yearly-feb-2023 CLC+Backbone 2018 (raster 10 m), Europe, 3-yearly, Feb. 2023 This metadata refer to the 'Corine Land Cover + Backbone' (CLC+ Backbone) which is a spatially detailed, large scale, Earth Observation-based land cover inventory. The CLC+ Backbone Raster Product is a 10m pixel-based land cover map based on Sentinel satelitte time series from July 2017 to June 2019. For each pixel it shows the dominant land cover among the 11 basic land cover classes. Thematic pixel values: 1: Sealed 2: Woody – needle leaved trees 3: Woody – Broadleaved deciduous trees 4: Woody – Broadleaved evergreen trees 5: Low-growing woody plants (bushes, shrubs) 6: Permanent herbaceous 7: Periodically herbaceous 8: Lichens and mosses 9: Non- and sparsely-vegetated 10: Water 11: Snow and ice 254: outside area 255: No data The product has a three years update cycle and is available for the 2018 reference year. 367 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/high-resolution-vegetation-phenology-and-productivity-3 https://www.wekeo.eu/data?view=viewer&t=1566840390697&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1JTkRJQ0VTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVklfTEFJXzEwTSIsInpJbmRleCI6NjB9LHsiaWQiOiJjMSIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1JTkRJQ0VTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVklfUUZMQUcyXzEwTSIsInpJbmRleCI6ODAsImlzSGlkZGVuIjp0cnVlfV0%3D&initial=1 High Resolution Vegetation Phenology and Productivity: Leaf Area Index (raster 10m) version 1 revision 1, Sep. 2021 This metadata refers to the Leaf Area Index (LAI) dataset, one of the near real-time (NRT) Vegetation Index products of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP), component of the Copernicus Land Monitoring Service (CLMS). The Leaf Area Index (LAI) is defined as half the total area of green elements of the canopy per unit horizontal ground area. The satellite-derived value corresponds to the total green LAI of all the canopy layers, including the understory which may represent a very significant contribution, particularly for forests. The LAI dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for the period from October 2016 until today, with daily updates. Each file has an associated quality indicator (QFLAG2) to assist users with the screening of clouds, shadows from clouds and topography, snow and water surfaces. 368 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cems-glofas-seasonal-reforecast https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-seasonal-reforecast cems-glofas-seasonal-reforecast This dataset provides a gridded modelled time series of river discharge forced with seasonal range meteorological reforecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation). This dataset was produced by forcing the open-source LISFLOOD hydrological model with input from the European Centre for Medium-range Weather Forecasts (ECMWF) ensemble seasonal forecasting system, SEAS5. For the period of 1981 to 2016 the number of ensemble members is 25, whilst reforecasts produced for 2017 onwards use a 51-member ensemble. Reforecasts are forecasts run over past dates, with those presented here used for producing the seasonal river discharge thresholds. In addition, they provide a suitably long time period against which the skill of the seasonal forecast can be assessed. The reforecasts are initialised monthly and run for 123 days, with a 24-hourly time step. For more specific information on the how the seasonal reforecast dataset is produced we refer to the documentation. Companion datasets, also available through the Climate Data Store (CDS), include the seasonal forecasts, for which the dataset provided here can be useful for local skill assessment and post-processing. For users looking for shorter term forecasts there are also medium-range forecasts and reforecasts available, as well as historical simulations that can be used to derive the hydrological climatology. For users looking specifically for European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS), which is managed, technically implemented and developed by the European Commission’s Joint Research Centre. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal resolution 0.05° x 0.05° for version 4.0, 0.1° x 0.1° for version 3.1 and older Vertical resolution Surface level for river discharge Temporal coverage 1 January 1981 to 1 December 2016 for the operational version, various dates for legacy versions Temporal resolution Reforecasts are initialised on the first of each month and run for 123 days, with a 24-hourly time step File format GRIB2 Conventions WMO standards for GRIB2 Versions Current version - GloFAS v4.0 released 2023-07-26. For more information on versions we refer to the documentation Update frequency GloFAS seasonal reforecasts are published at regular intervals on CDS, typically with every major GloFAS upgrade DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal resolution 0.05° x 0.05° for version 4.0, 0.1° x 0.1° for version 3.1 and older Horizontal resolution 0.05° x 0.05° for version 4.0, 0.1° x 0.1° for version 3.1 and older Vertical resolution Surface level for river discharge Vertical resolution Surface level for river discharge Temporal coverage 1 January 1981 to 1 December 2016 for the operational version, various dates for legacy versions Temporal coverage 1 January 1981 to 1 December 2016 for the operational version, various dates for legacy versions Temporal resolution Reforecasts are initialised on the first of each month and run for 123 days, with a 24-hourly time step Temporal resolution Reforecasts are initialised on the first of each month and run for 123 days, with a 24-hourly time step File format GRIB2 File format GRIB2 Conventions WMO standards for GRIB2 Conventions WMO standards for GRIB2 Versions Current version - GloFAS v4.0 released 2023-07-26. For more information on versions we refer to the documentation Versions Current version - GloFAS v4.0 released 2023-07-26. For more information on versions we refer to the documentation Update frequency GloFAS seasonal reforecasts are published at regular intervals on CDS, typically with every major GloFAS upgrade Update frequency GloFAS seasonal reforecasts are published at regular intervals on CDS, typically with every major GloFAS upgrade MAIN VARIABLES Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over the 24-hour time step. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over the 24-hour time step. River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over the 24-hour time step. RELATED VARIABLES Name Units Description Elevation m The mean height elevation above sea level for each pixel in the GloFAS domain. Accessible via the link in the Documentation tab. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Accessible via the link in the Documentation tab. RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Elevation m The mean height elevation above sea level for each pixel in the GloFAS domain. Accessible via the link in the Documentation tab. Elevation m The mean height elevation above sea level for each pixel in the GloFAS domain. Accessible via the link in the Documentation tab. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Accessible via the link in the Documentation tab. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Accessible via the link in the Documentation tab. 369 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-ocean-wave-indicators https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-ocean-wave-indicators sis-ocean-wave-indicators The dataset presents wave climate indicators based upon ocean surface wave parameters computed for a European-wide domain. This dataset provides an understanding of the European wave climate under the impact of climate change. It provides added value for various coastal sectors and studies such as port, shipping, and coastal management. The ocean surface wave field are computed using the ECMWF's Wave Model (Stand Alone WAM, SAW) forced by surface wind and accounting for ice coverage in polar latitudes. The wave climate is defined by means of wave spectral parameters such as the significant wave height and the peak wave period. In order to assess the impact of climate change on the ocean's surface wave field, the SAW model is run for three different climate scenarios: the present climate (labelled 'historical'), and two Representative Concentration Pathway (RCP) scenarios that correspond to an optimistic emission scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century often called the business-as-usual scenario (RCP8.5). The wave climate in these scenarios are simulated using wind forcing from a member of the EURO-CORDEX climate model ensemble - the HIRHAM5 regional climate model downscaled from the global climate model EC-EARTH. In addition to the three climate scenarios, the indicators are also computed using ERA5 reanlysis wind forcing. This provides recent historical wave climate indicators that may be used, for example, to look at specific events in the past. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Point data Horizontal coverage European coastline along the 20 m bathymetric contour Horizontal resolution Coastal grid points: 30 km Vertical coverage Surface Vertical resolution Single level Temporal coverage Historical: 1976 to 2005 ERA5 reanalysis: 2001 to 2017 RCP8.5: 2041 to 2100 RCP4.5: 2041 to 2100 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Point data Data type Point data Horizontal coverage European coastline along the 20 m bathymetric contour Horizontal coverage European coastline along the 20 m bathymetric contour Horizontal resolution Coastal grid points: 30 km Horizontal resolution Coastal grid points: 30 km Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage Historical: 1976 to 2005 ERA5 reanalysis: 2001 to 2017 RCP8.5: 2041 to 2100 RCP4.5: 2041 to 2100 Temporal coverage Historical: 1976 to 2005 ERA5 reanalysis: 2001 to 2017 RCP8.5: 2041 to 2100 RCP4.5: 2041 to 2100 Historical: 1976 to 2005 ERA5 reanalysis: 2001 to 2017 RCP8.5: 2041 to 2100 RCP4.5: 2041 to 2100 File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. Significant wave height m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. This parameter can be used to assess sea state and swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. Significant wave height m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. This parameter can be used to assess sea state and swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. This parameter can be used to assess sea state and swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. 370 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/pan-european-high-resolution-image-mosaic-2015-false-0 https://land.copernicus.eu/imagery-in-situ/european-image-mosaics/high-resolution/image-2015/coverage-1-20m Pan-European High Resolution Image Mosaic 2015 - False Colour, Coverage 1 (20 m), Sept. 2017 The pan-European High Resolution (HR) Image Mosaic 2015 provides up to two cloud-free HR optical coverage 1 of EEA39 countries including all islands of those countries plus French Overseas Departments (DOMs) acquired within predefined windows corresponding to the vegetation season in 2014-2015. Images are derived from the following satellite sensors: Resourcesat-1/2 SPOT-5 Sentinel-2 MSI The mosaic primarily is used as input data in the production of various Copernicus Land Monitoring Service (CLMS) datasets and services, such as land cover maps and high resolution layers on land cover characteristic and can be also useful for CLMS users for visualizations and classifications on land. The input imagery for the creation of the mosaic is provided by ESA. Due to license restrictions, HR Image Mosaic 2015 is only available as a web service (WMS), and not for data download. 371 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sea-ice-areavolume-transport-nordic-seas-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=ARCTIC_OMI_SI_Transport_NordicSeas Sea Ice Area/Volume Transport in the Nordic Seas from Reanalysis DEFINITION Net sea-ice volume and area transport through the openings Fram Strait between Spitsbergen and Greenland along 79°N, 20°W - 10°E (positive southward); northern Barents Sea between Svalbard and Franz Josef Land archipelagos along 80°N, 27°E - 60°E (positive southward); eastern Barents Sea between the Novaya Zemlya and Franz Josef Land archipelagos along 60°E, 76°N - 80°N (positive westward). For further details, see Lien et al. (2021). CONTEXT The Arctic Ocean contains a large amount of freshwater, and the freshwater export from the Arctic to the North Atlantic influence the stratification, and, the Atlantic Meridional Overturning Circulation (e.g., Aagaard et al., 1985). The Fram Strait represents the major gateway for freshwater transport from the Arctic Ocean, both as liquid freshwater and as sea ice (e.g., Vinje et al., 1998). The transport of sea ice through the Fram Strait is therefore important for the mass balance of the perennial sea-ice cover in the Arctic as it represents a large export of about 10% of the total sea ice volume every year (e.g., Rampal et al., 2011). Sea ice export through the Fram Strait has been found to explain a major part of the interannual variations in Arctic perennial sea ice volume changes (Ricker et al., 2018). The sea ice and associated freshwater transport to the Barents Sea has been suggested to be a driving mechanism for the presence of Arctic Water in the northern Barents Sea, and, hence, the presence of the Barents Sea Polar Front dividing the Barents Sea into a boreal and an Arctic part (Lind et al., 2018). In recent decades, the Arctic part of the Barents Sea has been giving way to an increasing boreal part, with large implications for the marine ecosystem and harvestable resources (e.g., Fossheim et al., 2015). CMEMS KEY FINDINGS The sea-ice transport through the Fram Strait shows a distinct seasonal cycle in both sea ice area and volume transport, with a maximum in winter. There is a slight positive trend in the volume transport over the last two and a half decades. In the Barents Sea, a strong reduction of nearly 90% in average sea-ice thickness has diminished the sea-ice import from the Polar Basin (Lien et al., 2021). In both areas, the Fram Strait and the Barents Sea, the winds governed by the regional patterns of atmospheric pressure is an important driving force of temporal variations in sea-ice transport (e.g., Aaboe et al., 2021; Lien et al., 2021). DOI (product):https://doi.org/10.48670/moi-00192 https://doi.org/10.48670/moi-00192 372 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/pan-european-high-resolution-image-mosaic-2015-false https://land.copernicus.eu/imagery-in-situ/european-image-mosaics/high-resolution/image-2015/high-resolution-image-mosaic-2015-false-colour-cov2-20m Pan-European High Resolution Image Mosaic 2015 - False Colour, Coverage 2 (20 m), Jan. 2020 The pan-European High Resolution (HR) Image Mosaic 2015 provides up to two cloud-free HR optical coverage 2 of EEA39 countries including all islands of those countries plus French Overseas Departments (DOMs) acquired within predefined windows corresponding to the vegetation season in 2014-2015. Images are derived from the following satellite sensors: Resourcesat-1/2 SPOT-5 Sentinel-2 MSI The mosaic primarily is used as input data in the production of various Copernicus Land Monitoring Service (CLMS) datasets and services, such as land cover maps and high resolution layers on land cover characteristic and can be also useful for CLMS users for visualizations and classifications on land. The input imagery for the creation of the mosaic is provided by ESA. Due to license restrictions, HR Image Mosaic 2015 is only available as a web service (WMS), and not for data download. 373 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-humidity-profiles https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-humidity-profiles satellite-humidity-profiles This dataset provides monthly and zonally averaged tropospheric humidity profiles derived from globally distributed GPS radio occultation (RO) measurements from EUMETSAT's Metop polar-orbiting satellites. Humidity plays an important role in the Earth's climate system, due to the strong greenhouse effect of water vapour but also for its role in the global energy transport, vertically in the atmosphere and horizontally between different geographical regions. It is central to the formation of clouds and precipitation, and determines the fundamental conditions for the biosphere, including distribution of rainfall and droughts. The dataset is produced by the EUMETSAT Radio Occultation Meteorology Satellite Application Facility (ROM SAF) and comprises the Climate Data Record (CDR, December 2006 to December 2016, ROM SAF product ID GRM-29-R1) and the Interim Climate Data Record (ICDR, January 2017 to present, ROM SAF product ID GRM-29-I1). Tropospheric humidity profiles are retrieved using 1D variational assimilation of observed atmospheric refractivity profiles and background model information from the ECMWF reanalysis. Compared to other satellite observation techniques, RO observations have high vertical resolution and are not affected by clouds or the type of underlying surface (such as land and sea). The RO retrievals are averaged in monthly and 5-degree latitude bands for altitudes below 12km which roughly covers the troposphere. The variability (standard deviation) within such monthly bins are also provided, as well as associated quantities such as observation counts, sampling errors and an estimate of the fraction of model information in the humidity retrievals. In addition, mean and variability are provided for humidity for the a priori ECMWF reanalysis data, sampled (i.e. collocated) similarly at the satellite observation locations. a priori Please note that the terms humidity and water vapour are used interchangeably on this page and in the documentation. DATA DESCRIPTION Data type Gridded Projection Not applicable. Regular in height atmospheric profiles are provided along a regular latitude axis. Horizontal coverage Global zonal means Horizontal resolution 5.0° in latitude Vertical coverage 0-12 km Vertical resolution 0.2 km Temporal coverage From 2006 to present Temporal resolution Monthly File format NetCDF 3 Versions ”v1.0 (this version corresponds to ROM SAF product IDs GRM-29-R1 for the CDR and GRM-29-I1 for the ICDR)” Update frequency Quarterly updates DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Not applicable. Regular in height atmospheric profiles are provided along a regular latitude axis. Projection Not applicable. Regular in height atmospheric profiles are provided along a regular latitude axis. Horizontal coverage Global zonal means Horizontal coverage Global zonal means Horizontal resolution 5.0° in latitude Horizontal resolution 5.0° in latitude Vertical coverage 0-12 km Vertical coverage 0-12 km Vertical resolution 0.2 km Vertical resolution 0.2 km Temporal coverage From 2006 to present Temporal coverage From 2006 to present Temporal resolution Monthly Temporal resolution Monthly File format NetCDF 3 File format NetCDF 3 Versions ”v1.0 (this version corresponds to ROM SAF product IDs GRM-29-R1 for the CDR and GRM-29-I1 for the ICDR)” Versions ”v1.0 (this version corresponds to ROM SAF product IDs GRM-29-R1 for the CDR and GRM-29-I1 for the ICDR)” Update frequency Quarterly updates Update frequency Quarterly updates MAIN VARIABLES Name Units Description Specific humidity g kg-1 The ratio of the mass of water vapour in air to the total mass of the mixture of air and water vapour. Values represent the monthly mean for 5-degree latitude bands and altitudes below 12 km for all longitudes. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Specific humidity g kg-1 The ratio of the mass of water vapour in air to the total mass of the mixture of air and water vapour. Values represent the monthly mean for 5-degree latitude bands and altitudes below 12 km for all longitudes. Specific humidity g kg-1 The ratio of the mass of water vapour in air to the total mass of the mixture of air and water vapour. Values represent the monthly mean for 5-degree latitude bands and altitudes below 12 km for all longitudes. RELATED VARIABLES A number of variables accounting for distribution and uncertainty of the humidity profiles are also included in the files along the main variables. They help users understand better on per datum level the possible variations of the main variables due to changes in the processing algorithms and their assumptions. Please refer to the product user guide and specification for more details. RELATED VARIABLES RELATED VARIABLES A number of variables accounting for distribution and uncertainty of the humidity profiles are also included in the files along the main variables. They help users understand better on per datum level the possible variations of the main variables due to changes in the processing algorithms and their assumptions. Please refer to the product user guide and specification for more details. A number of variables accounting for distribution and uncertainty of the humidity profiles are also included in the files along the main variables. They help users understand better on per datum level the possible variations of the main variables due to changes in the processing algorithms and their assumptions. Please refer to the product user guide and specification for more details. 374 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-significant-wave-height-extreme-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BLKSEA_OMI_SEASTATE_extreme_var_swh_mean_and_anomaly Black Sea Significant Wave Height extreme from Reanalysis DEFINITION The CMEMS BLKSEA_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (BLKSEA_MULTIYEAR_WAV_007_006) and the Analysis product (BLKSEA_ANALYSISFORECAST_WAV_007_003). Two parameters have been considered for this OMI: * Map of the 99th mean percentile: It is obtained from the Multy Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1979-2019). * Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2020. This indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (Pérez Gómez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Álvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (Álvarez- Fanjul et al., 2019). CONTEXT The sea state and its related spatio-temporal variability affect maritime activities and the physical connectivity between offshore waters and coastal ecosystems, including biodiversity of marine protected areas (González-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2015, IPCC, 2019). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions (IPCC, 2021). Significant Wave Height mean 99th percentile in the Black Sea region shows west-eastern dependence demonstrating that the highest values of the average annual 99th percentiles are in the areas where high winds and long fetch are simultaneously present. The largest values of the mean 99th percentile in the Black Sea in the southewestern Black Sea are around 3.5 m, while in the eastern part of the basin are around 2.5 m (Staneva et al., 2019a and 2019b). CMEMS KEY FINDINGS Significant Wave Height mean 99th percentile in the Black Sea region shows west-eastern dependence with largest values in the southwestern Black Sea, with values as high as 3.5 m, while the 99th percentile values in the eastern part of the basin are around 2.5 m. The Black Sea, the 99th mean percentile for 2002-2019 shows a similar pattern demonstrating that the highest values of the mean annual 99th percentile are in the western Black Sea. This pattern is consistent with the previous studies, e.g. of (Akpınar and Kömürcü, 2012; and Akpinar et al., 2016). The anomaly of the 99th percentile in 2020 is mostly negative with values down to ~-45 cm. The highest negative anomalies for 2020 are observed in the southeastern area where the multi-year mean 99th percentile is the lowest. The highest positive anomalies of the 99th percentile in 2020 are located in the southwestern Black Sea and along the eastern coast. The map of anomalies for 2020, presenting alternate bands of positive and negative values depending on latitude, is consistent with the yearly west-east displacement of the tracks of the largest storms. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00214 https://doi.org/10.48670/moi-00214 375 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/small-woody-features-2015-vector-europe-3-yearly-nov-2019 https://land.copernicus.eu/pan-european/high-resolution-layers/small-woody-features/small-woody-features-2015 Small Woody Features 2015 (vector), Europe, 3-yearly - Nov. 2019 The HRL Small Woody Features (SWF) is a new Copernicus Land Monitoring Service (CMLS) product, which provides harmonized information on linear structures such as hedgerows, as well as patches (200 m² ≤ area ≤ 5000 m²) of woody features across the EEA39 countries. Small woody landscape features are important vectors of biodiversity and provide information on fragmentation of habitats with a direct potential for restoration while also providing a link to hazard protection and green infrastructure, amongst others. The SWF layer contains woody linear, and small patchy elements, but is not differentiated into trees, hedges, bushes and scrub. The spatial pattern are limited to linear structures and isolated patches (patchy structures) on the basis of geometric characteristics. Additional Woody Features (AWF) are also included in this product. They consist of woody structures that do not fulfil the SWF geometric specifications but which are connected to valid SWFs structures. VHR imagery (DEIMOS-2, Pleiades 1A, Pleiades 1B, GeoEye-1, SPOT 6, SPOT 7, WorldView-2, WorldView-3 images from 2015) made available in the ESA Copernicus DWH are the main data source for the detection of small woody features identifiable within the given image resolution. The dataset is available for the 2015 reference year and is produced in three different formats. This metadata corresponds to the SWF vector layer, which separates the SWF class into Linear (code = 1) and Patchy (code = 2). Additional Woody Features are represented with code = 3. This is the primary product of the Small Woody Features mapping, and thus also the one with most detail. The vector data set can be downloaded in Geodatabase and Geopackage formats. 376 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/land-surface-temperature-thermal-condition-index-2021 https://land.copernicus.eu/global/access Land Surface Temperature with Thermal Condition Index 2021-present (raster 5 km), global, 10-daily - version 2 10-day Synthesis Land Surface Temperature (LST10-TCI) provides a statistical overview of the LST over each 10-day compositing for every image pixel. LST10-TCI is useful for the scientific community, namely for those dealing with meteorological and climate models. Accurate values of LST are also of special interest in a wide range of areas related to land surface processes, including meteorology, hydrology, agrometeorology, climatology and environmental studies. 377 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/land-surface-temperature-thermal-condition-index-2017 https://land.copernicus.eu/global/access Land Surface Temperature with Thermal Condition Index 2017-2021 (raster 5 km), global, 10-daily - version 1 10-day Synthesis Land Surface Temperature (LST10-TCI) provides a statistical overview of the LST over each 10-day compositing for every image pixel. LST10-TCI is useful for the scientific community, namely for those dealing with meteorological and climate models. Accurate values of LST are also of special interest in a wide range of areas related to land surface processes, including meteorology, hydrology, agrometeorology, climatology and environmental studies. 378 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/insitu-gridded-observations-europe https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe insitu-gridded-observations-europe E-OBS is a daily gridded land-only observational dataset over Europe. The blended time series from the station network of the European Climate Assessment & Dataset (ECA&D) project form the basis for the E-OBS gridded dataset. All station data are sourced directly from the European National Meteorological and Hydrological Services (NMHSs) or other data holding institutions. For a considerable number of countries the number of stations used is the complete national network and therefore much more dense than the station network that is routinely shared among NMHSs (which is the basis of other gridded datasets). The density of stations gradually increases through collaborations with NMHSs within European research contracts. Initially, in 2008, this gridded dataset was developed to provide validation for the suite of Europe-wide climate model simulations produced as part of the European Union ENSEMBLES project. While E-OBS remains an important dataset for model validation, it is also used more generally for monitoring the climate across Europe, particularly with regard to the assessment of the magnitude and frequency of daily extremes. The position of E-OBS is unique in Europe because of the relatively high spatial horizontal grid spacing, the daily resolution of the dataset, the provision of multiple variables and the length of the dataset. Finally, the station data on which E-OBS is based are available through the ECA&D webpages (where the owner of the data has given permission to do so). In these respects it contrasts with other datasets. The dataset is daily, meaning the observations cover 24 hours per time step. The exact 24-hour period can be different per region. The reason for this is that some data providers measure between midnight to midnight while others might measure from morning to morning. Since E-OBS is an observational dataset, no attempts have been made to adjust time series for this 24-hour offset. It is made sure, where known, that the largest part of the measured 24-hour period corresponds to the day attached to the time step in E-OBS (and ECA&D). DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Europe Horizontal resolution 0.1° x 0.1° and 0.25° x 0.25° Vertical coverage Near surface Vertical resolution Single level Temporal coverage January 1950 to present Temporal resolution Day File format NetCDF-4 Conventions Climate and Forecast Metadata Convention v1.4 (CF-v1.4) Versions v19.0e to v27.0e Update frequency New versions added every 6 months DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Europe Horizontal coverage Europe Horizontal resolution 0.1° x 0.1° and 0.25° x 0.25° Horizontal resolution 0.1° x 0.1° and 0.25° x 0.25° Vertical coverage Near surface Vertical coverage Near surface Vertical resolution Single level Vertical resolution Single level Temporal coverage January 1950 to present Temporal coverage January 1950 to present Temporal resolution Day Temporal resolution Day File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast Metadata Convention v1.4 (CF-v1.4) Conventions Climate and Forecast Metadata Convention v1.4 (CF-v1.4) Versions v19.0e to v27.0e Versions v19.0e to v27.0e Update frequency New versions added every 6 months Update frequency New versions added every 6 months MAIN VARIABLES Name Units Description Land surface elevation m Earth's surface height above sea level derived from the Global 30 Arc-Second Elevation Data Set (GTOPO30) developed by the United States Geological Survey. Maximum temperature °C Daily maximum air temperature measured near the surface, usually at height of 2 meters. Mean temperature °C Daily mean air temperature measured near the surface, usually at height of 2 meters. Minimum temperature °C Daily minimum air temperature measured near the surface, usually at height of 2 meters. Precipitation amount mm Total daily amount of rain, snow and hail measured as the height of the equivalent liquid water in a square meter The data sources for the precipitation are rain gauge data which do not have a uniform way of defining the 24-hour period over which precipitation measurements are made. Therefore, there is no uniform time period (for instance, 06 UTC previous day to 06 UTC today) which could be attached to the daily precipitation. Relative humidity % Daily mean relative humidity measured near the surface usually at a height of 2 meters. Relative humidity values relate to actual humidity and saturation humidity. Values are in the interval [0,100]. 0% means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. Sea level pressure hPa Daily mean air pressure at sea level. In regions where the Earth's surface is above sea level the surface pressure is used to compute the air pressure that would exist at sea level directly below given a constant air temperature from the surface to the sea level point. Surface shortwave downwelling radiation W m-2 The flux of shortwave radiation (also known as solar radiation) measured at the Earth's surface. Wind speed m s-1 Daily mean wind speed at 10 meter height. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Land surface elevation m Earth's surface height above sea level derived from the Global 30 Arc-Second Elevation Data Set (GTOPO30) developed by the United States Geological Survey. Land surface elevation m Earth's surface height above sea level derived from the Global 30 Arc-Second Elevation Data Set (GTOPO30) developed by the United States Geological Survey. Maximum temperature °C Daily maximum air temperature measured near the surface, usually at height of 2 meters. Maximum temperature °C Daily maximum air temperature measured near the surface, usually at height of 2 meters. Mean temperature °C Daily mean air temperature measured near the surface, usually at height of 2 meters. Mean temperature °C Daily mean air temperature measured near the surface, usually at height of 2 meters. Minimum temperature °C Daily minimum air temperature measured near the surface, usually at height of 2 meters. Minimum temperature °C Daily minimum air temperature measured near the surface, usually at height of 2 meters. Precipitation amount mm Total daily amount of rain, snow and hail measured as the height of the equivalent liquid water in a square meter The data sources for the precipitation are rain gauge data which do not have a uniform way of defining the 24-hour period over which precipitation measurements are made. Therefore, there is no uniform time period (for instance, 06 UTC previous day to 06 UTC today) which could be attached to the daily precipitation. Precipitation amount mm Total daily amount of rain, snow and hail measured as the height of the equivalent liquid water in a square meter The data sources for the precipitation are rain gauge data which do not have a uniform way of defining the 24-hour period over which precipitation measurements are made. Therefore, there is no uniform time period (for instance, 06 UTC previous day to 06 UTC today) which could be attached to the daily precipitation. Relative humidity % Daily mean relative humidity measured near the surface usually at a height of 2 meters. Relative humidity values relate to actual humidity and saturation humidity. Values are in the interval [0,100]. 0% means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. Relative humidity % Daily mean relative humidity measured near the surface usually at a height of 2 meters. Relative humidity values relate to actual humidity and saturation humidity. Values are in the interval [0,100]. 0% means that the air in the grid cell is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. Sea level pressure hPa Daily mean air pressure at sea level. In regions where the Earth's surface is above sea level the surface pressure is used to compute the air pressure that would exist at sea level directly below given a constant air temperature from the surface to the sea level point. Sea level pressure hPa Daily mean air pressure at sea level. In regions where the Earth's surface is above sea level the surface pressure is used to compute the air pressure that would exist at sea level directly below given a constant air temperature from the surface to the sea level point. Surface shortwave downwelling radiation W m-2 The flux of shortwave radiation (also known as solar radiation) measured at the Earth's surface. Surface shortwave downwelling radiation W m-2 The flux of shortwave radiation (also known as solar radiation) measured at the Earth's surface. Wind speed m s-1 Daily mean wind speed at 10 meter height. Wind speed m s-1 Daily mean wind speed at 10 meter height. 379 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/high-resolution-vegetation-phenology-and-productivity-ppi https://www.wekeo.eu/data?view=viewer&t=1562219742857&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMiIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6U0VBU09OQUwtVFJBSkVDVE9SSUVTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfU1RfUFBJXzEwTSIsInpJbmRleCI6NzB9XQ%3D%3D High Resolution Vegetation Phenology and Productivity: PPI Seasonal Trajectories (raster 10m) version 1 revision 1, Sep. 2021 This metadata refers to the Plant Phenology Index (PPI) Seasonal Trajectories, is one of the products of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Plant Phenology Index (PPI) is a physically based vegetation index for improved monitoring of plant phenology, that is developed from a simplified solution to the radiative transfer equation by Jin and Eklundh (2014) and that has a linear relationship with green leaf area index. The PPI Seasonal Trajectories (ST) product is derived from a TIMESAT-based function fitting of the time series of the PPI vegetation index and thus provides a filtered time series of Plant Phenology Index (PPI), with regular 10-day time step. The PPI dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for the period from 2017 until today. It is updated in the first quarter of each year. Each file has an associated quality indicator (QFLAG) that provides a confidence level. 380 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/european-seas-gridded-l4-sea-surface-heights-and-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_EUR_PHY_L4_MY_008_068 EUROPEAN SEAS GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING) Short description: Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications. This product is processed by the DUACS multimission altimeter data processing system. http://duacs.cls.fr http://duacs.cls.fr DOI (product):https://doi.org/10.48670/moi-00141 https://doi.org/10.48670/moi-00141 381 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/n2k-2012-vector-europe-6-yearly-jul-2021 https://land.copernicus.eu/local/natura/n2k-2012?tab=download N2K 2012 (vector), Europe, 6-yearly, Jul. 2021 This metadata refers to CLMS N2K 2012 product, the Copernicus Land Cover/Land Use (LC/LU) status map, with 2012 as reference year for the classification, tailored to the needs of biodiversity monitoring in selected Natura2000 sites: 4790 sites of natural and semi-natural grassland formations listed in Annex I of the Habitats Directive, including a 2 km buffer zone surrounding the sites and covering an area of 631.820 km² across Europe (EU27, the United Kingdom and Switzerland). The product includes three Emerald sites in Switzerland. LC/LU has been extracted from VHR satellite data and other available data. This metadata specifically refers to the revision of the 2012 N2K status map carried out during the production of the 2018 update. The production of N2K updates was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 382 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/seasonal-monthly-pressure-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal-monthly-pressure-levels seasonal-monthly-pressure-levels This entry covers pressure-level data aggregated on a monthly time resolution. pressure-level data monthly time resolution Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Seasonal forecasts Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). multi-system seasonal forecast service The variables available in this data set are listed in the table below. The data includes forecasts created in real-time (since 2017) and retrospective forecasts (hindcasts) initialised at equivalent intervals during the period 1993-2016. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 1° x 1° Vertical coverage From 1000 hPa to 10 hPa Temporal coverage 1993 to 2016 (hindcasts); 2017 to present (forecasts) Temporal resolution Monthly File format GRIB Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 1° x 1° Horizontal resolution 1° x 1° Vertical coverage From 1000 hPa to 10 hPa Vertical coverage From 1000 hPa to 10 hPa Temporal coverage 1993 to 2016 (hindcasts); 2017 to present (forecasts) Temporal coverage 1993 to 2016 (hindcasts); 2017 to present (forecasts) Temporal resolution Monthly Temporal resolution Monthly File format GRIB File format GRIB Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. Update frequency Real-time forecasts are released once per month on the 6th at 12UTC for ECMWF and on the 10th at 12 UTC for the other originating centres. MAIN VARIABLES Name Units Geopotential m2 s-2 Specific humidity kg kg-1 Temperature K U-component of wind m s-1 V-component of wind m s-1 MAIN VARIABLES MAIN VARIABLES Name Units Name Units Geopotential m2 s-2 Geopotential m2 s-2 Specific humidity kg kg-1 Specific humidity kg kg-1 Temperature K Temperature K U-component of wind m s-1 U-component of wind m s-1 V-component of wind m s-1 V-component of wind m s-1 383 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-mean-sea-level-trend-map-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_SL_regional_trends Global Ocean Mean Sea Level trend map from Observations Reprocessing DEFINITION The sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2021 version) altimeter gridded maps of sea level anomalies based on a stable number of altimeters (two) in the satellite constellation. The product is distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The regional sea level trends are derived from a linear fit of the altimeter sea level maps. The altimeter data have not been corrected for the effect of the Glacial Isostatic Adjustment nor the TOPEX-A instrumental drift during the period 1993-1998. The trend uncertainty is provided in a 90% confidence interval (Prandi et al., 2021). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not taken into account. CONTEXT The indicator on sea level trend is a crucial index of climate change, and individual components contribute to sea level rise, including expansion due to ocean warming and melting of glaciers and ice sheets (WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report, global mean sea level (GMSL) increased by 0.20 (0.15 to 0.25) m over the period 1901 to 2018 with a rate 25 of rise that has accelerated since the 1960s to 3.7 (3.2 to 4.2) mm yr-1 for the period 2006–2018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). At regional scale, sea level does not change homogenously, and regional sea level change is also influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). Rising sea level can strongly affect population and infrastructures in coastal areas, increase their vulnerability and risks for food security, particularly in low lying areas and island states. Adverse impacts from floods, storms and tropical cyclones with related losses and damages have increased due to sea level rise, and increase their vulnerability and increase risks for food security, particularly in low lying areas and island states (IPCC, 2019, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). CMEMS KEY FINDINGS The altimeter mean sea level trends over the (1993/01/01, 2021/08/02) period exhibit large-scale variations at rates reaching up to more than +10 mm/yr in regions such as the western tropical Pacific Ocean. In this area, trends are mainly of thermosteric origin (Legeais et al., 2018; Meyssignac et al., 2017) in response to increased easterly winds during the last two decades associated with the decreasing Interdecadal Pacific Oscillation (IPO)/Pacific Decadal Oscillation (e.g., McGregor et al., 2012; Merrifield et al., 2012; Palanisamy et al., 2015; Rietbroek et al., 2016). Prandi et al. (2021) have estimated a regional altimeter sea level error budget from which they determine a regional error variance-covariance matrix and they provide uncertainties of the regional sea level trends. Over 1993-2019, the averaged local sea level trend uncertainty is around 0.83 mm/yr with local values ranging from 0.78 to 1.22 mm/yr. DOI (product):https://doi.org/10.48670/moi-00238 https://doi.org/10.48670/moi-00238 384 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/small-woody-features-2015-raster-100-m-europe-3-yearly https://land.copernicus.eu/pan-european/high-resolution-layers/small-woody-features/small-woody-features-2015 Small Woody Features 2015 (raster 100 m), Europe, 3-yearly - Nov. 2019 The HRL Small Woody Features (SWF) is a new Copernicus Land Monitoring Service (CMLS) product, which provides harmonized information on linear structures such as hedgerows, as well as patches (200 m² ≤ area ≤ 5000 m²) of woody features across the EEA39 countries. Small woody landscape features are important vectors of biodiversity and provide information on fragmentation of habitats with a direct potential for restoration while also providing a link to hazard protection and green infrastructure, amongst others. The SWF layer contains woody linear, and small patchy elements, but is not differentiated into trees, hedges, bushes and scrub. The spatial pattern are limited to linear structures and isolated patches (patchy structures) on the basis of geometric characteristics. Additional Woody Features (AWF) are also included in this product. They consist of woody structures that do not fulfil the SWF geometric specifications but which are connected to valid SWFs structures. VHR imagery (DEIMOS-2, Pleiades 1A, Pleiades 1B, GeoEye-1, SPOT 6, SPOT 7, WorldView-2, WorldView-3 images from 2015) made available in the ESA Copernicus DWH are the main data source for the detection of small woody features identifiable within the given image resolution. The dataset is available for the 2015 reference year and is produced in three different formats. This metadata corresponds to the SWF 100m spatial resolution raster aggregate layers: SWF density (0 – 100 %), AWF density (0 – 100 %) and SWF+AWF density (0 – 100 %). The SWF 100m raster layer, consistent with the EEA 100m grid, is a 100m aggregated version of the SWF 5m raster layer. It can be used as a landscape descriptor of SWF density for large areas. 385 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-gridded-l4-sea-surface-heights-and-derived http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_L4_MY_008_047 GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (1993-ONGOING) Short description: Altimeter satellite gridded Sea Level Anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean. The SLA is estimated by Optimal Interpolation, merging the L3 along-track measurement from the different altimeter missions available. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (i.e. Absolute Dynamic Topography and geostrophic currents (absolute and anomalies)). It serves in delayed-time applications. This product is processed by the DUACS multimission altimeter data processing system. http://duacs.cls.fr http://duacs.cls.fr DOI (product) :https://doi.org/10.48670/moi-00148 https://doi.org/10.48670/moi-00148 386 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-chlorophyll-trend-map-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_trend Mediterranean Sea Chlorophyll-a trend map from Observations Reprocessing DEFINITION This product includes the Mediterranean Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3A-OLCI) (at 1 km resolution) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2021). The trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens’s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included. CONTEXT Phytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration - as a proxy for phytoplankton - respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). The Mediterranean Sea is an oligotrophic basin, where chlorophyll concentration decreases following a specific gradient from West to East (Colella et al. 2016). The highest concentrations are observed in coastal areas and at the river mouths, where the anthropogenic pressure and nutrient loads impact on the eutrophication regimes (Colella et al. 2016). The use of long-term time series of consistent, well-calibrated, climate-quality data record is crucial for detecting eutrophication. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series. CMEMS KEY FINDINGS Chlorophyll trend in the Mediterranean Sea, for the period 1997-2021, is negative over most of the basin. With respect to the previous trend estimate (1997-2020) the negative values are even more widespread over the basin. Weak positive trend areas are visible only in the southern part of the western Mediterranean basin, Rhode Gyre and in the northern coast of the Aegean Sea (the only area where the positive trend is increased with respect the previous map). On average the trend in the Mediterranean Sea is about -0.6% per year. Contrary to what shown by Salgado-Hernanz et al. (2019) in their analysis (related to 1998-2014 satellite observations), western and eastern part of the Mediterranean Sea do not show differences. In the Ligurian Sea, the trend switch to negative values, differing from the positive regime observed in the trend maps of both Colella et al. (2016) and Salgado-Hernanz et al. (2019), referred, respectively, to 1998-2009 and 1998-2014 period, respectively. The waters offshore the Po River mouth show weak negative trend values, partially differing from the markable negative regime observed in the 1998-2009 period (Colella et al., 2016), and definitely moving from the positive trend observed by Salgado-Hernanz et al. (2019). DOI (product):https://doi.org/10.48670/moi-00260 https://doi.org/10.48670/moi-00260 387 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-era5-single-levels-monthly-means https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means reanalysis-era5-single-levels-monthly-means ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. ERA5 Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. this article these guidelines Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on single levels from 1940 to present". DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Temporal coverage 1940 to present Temporal resolution Monthly File format GRIB Update frequency Monthly DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Horizontal resolution Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Temporal coverage 1940 to present Temporal coverage 1940 to present Temporal resolution Monthly Temporal resolution Monthly File format GRIB File format GRIB Update frequency Monthly Update frequency Monthly MAIN VARIABLES Name Units Description 100m u-component of wind m s-1 This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. 100m v-component of wind m s-1 This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. 10m u-component of neutral wind m s-1 This parameter is the eastward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m u-component of wind m s-1 This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). 10m v-component of neutral wind m s-1 This parameter is the northward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m v-component of wind m s-1 This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). 10m wind speed m s-1 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). The eastward and northward components of the horizontal wind at 10m are also available as parameters. 2m dewpoint temperature K This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Air density over the oceans kg m-3 This parameter is the mass of air per cubic metre over the oceans, derived from the temperature, specific humidity and pressure at the lowest model level in the atmospheric model. This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Angle of sub-gridscale orography radians This parameter is one of four parameters (the others being standard deviation, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. The angle of the sub-grid scale orography characterises the geographical orientation of the terrain in the horizontal plane (from a bird's-eye view) relative to an eastwards axis. This parameter does not vary in time. Anisotropy of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, slope and angle of sub-gridscale orography) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter is a measure of how much the shape of the terrain in the horizontal plane (from a bird's-eye view) is distorted from a circle. A value of one is a circle, less than one an ellipse, and 0 is a ridge. In the case of a ridge, wind blowing parallel to it does not exert any drag on the flow, but wind blowing perpendicular to it exerts the maximum drag. This parameter does not vary in time. Benjamin-feir index Dimensionless This parameter is used to calculate the likelihood of freak ocean waves, which are waves that are higher than twice the mean height of the highest third of waves. Large values of this parameter (in practice of the order 1) indicate increased probability of the occurrence of freak waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived from the statistics of the two-dimensional wave spectrum. More precisely, it is the square of the ratio of the integral ocean wave steepness and the relative width of the frequency spectrum of the waves. Further information on the calculation of this parameter is given in Section 10.6 of the ECMWF Wave Model documentation. Boundary layer dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Boundary layer height m This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface. The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop. The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review. Charnock Dimensionless This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind. When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by the ECMWF Wave Model. Clear-sky direct solar radiation at surface J m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Cloud base height m The height above the Earth's surface of the base of the lowest cloud layer, at the specified time. This parameter is calculated by searching from the second lowest model level upwards, to the height of the level where cloud fraction becomes greater than 1% and condensate content greater than 1.E-6 kg kg-1. Fog (i.e., cloud in the lowest model layer) is not considered when defining cloud base height. Coefficient of drag with waves Dimensionless This parameter is the resistance that ocean waves exert on the atmosphere. It is sometimes also called a "friction coefficient". It is calculated by the wave model as the ratio of the square of the friction velocity, to the square of the neutral wind speed at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. Convective available potential energy J kg-1 This is an indication of the instability (or stability) of the atmosphere and can be used to assess the potential for the development of convection, which can lead to heavy rainfall, thunderstorms and other severe weather. In the ECMWF Integrated Forecasting System (IFS), CAPE is calculated by considering parcels of air departing at different model levels below the 350 hPa level. If a parcel of air is more buoyant (warmer and/or with more moisture) than its surrounding environment, it will continue to rise (cooling as it rises) until it reaches a point where it no longer has positive buoyancy. CAPE is the potential energy represented by the total excess buoyancy. The maximum CAPE produced by the different parcels is the value retained. Large positive values of CAPE indicate that an air parcel would be much warmer than its surrounding environment and therefore, very buoyant. CAPE is related to the maximum potential vertical velocity of air within an updraft; thus, higher values indicate greater potential for severe weather. Observed values in thunderstorm environments often may exceed 1000 joules per kilogram (J kg-1), and in extreme cases may exceed 5000 J kg-1. The calculation of this parameter assumes: (i) the parcel of air does not mix with surrounding air; (ii) ascent is pseudo-adiabatic (all condensed water falls out) and (iii) other simplifications related to the mixed-phase condensational heating. Convective inhibition J kg-1 This parameter is a measure of the amount of energy required for convection to commence. If the value of this parameter is too high, then deep, moist convection is unlikely to occur even if the convective available potential energy or convective available potential energy shear are large. CIN values greater than 200 J kg-1 would be considered high. An atmospheric layer where temperature increases with height (known as a temperature inversion) would inhibit convective uplift and is a situation in which convective inhibition would be large. Convective precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Rainfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Downward UV radiation at the surface J m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Duct base height m Duct base height as diagnosed from the vertical gradient of atmospheric refractivity. Eastward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Eastward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Forecast albedo Dimensionless This parameter is a measure of the reflectivity of the Earth's surface. It is the fraction of short-wave (solar) radiation reflected by the Earth's surface, for diffuse radiation, assuming a fixed spectrum of downward short-wave radiation at the surface. The values of this parameter vary between zero and one. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Short-wave radiation from the Sun is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Forecast logarithm of surface roughness for heat Dimensionless This parameter is the natural logarithm of the roughness length for heat. The surface roughness for heat is a measure of the surface resistance to heat transfer. This parameter is used to determine the air to surface transfer of heat. For given atmospheric conditions, a higher surface roughness for heat means that it is more difficult for the air to exchange heat with the surface. A lower surface roughness for heat means that it is easier for the air to exchange heat with the surface. Over the ocean, surface roughness for heat depends on the waves. Over sea-ice, it has a constant value of 0.001 m. Over land, it is derived from the vegetation type and snow cover. Forecast surface roughness m This parameter is the aerodynamic roughness length in metres. It is a measure of the surface resistance. This parameter is used to determine the air to surface transfer of momentum. For given atmospheric conditions, a higher surface roughness causes a slower near-surface wind speed. Over ocean, surface roughness depends on the waves. Over land, surface roughness is derived from the vegetation type and snow cover. Free convective velocity over the oceans m s-1 This parameter is an estimate of the vertical velocity of updraughts generated by free convection. Free convection is fluid motion induced by buoyancy forces, which are driven by density gradients. The free convective velocity is used to estimate the impact of wind gusts on ocean wave growth. It is calculated at the height of the lowest temperature inversion (the height above the surface of the Earth where the temperature increases with height). This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Friction velocity m s-1 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is a theoretical wind speed at the Earth's surface that expresses the magnitude of stress. It is calculated by dividing the surface stress by air density and taking its square root. For turbulent flow, the friction velocity is approximately constant in the lowest few metres of the atmosphere. This parameter increases with the roughness of the surface. It is used to calculate the way wind changes with height in the lowest levels of the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location at the surface of the Earth, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The (surface) geopotential height (orography) can be calculated by dividing the (surface) geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2 ). This parameter does not vary in time. Gravity wave dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. High cloud cover Dimensionless The proportion of a grid box covered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a "standard atmosphere")). The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. High vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "high". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Ice temperature layer 1 K This parameter is the sea-ice temperature in layer 1 (0 to 7cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 1, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 2 K This parameter is the sea-ice temperature in layer 2 (7 to 28cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 2, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 3 K This parameter is the sea-ice temperature in layer 3 (28 to 100cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 3, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 4 K This parameter is the sea-ice temperature in layer 4 (100 to 150cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 4, grid point value would be contaminated with fictitious zero ice values. Instantaneous 10m wind gust m s-1 This parameter is the maximum wind gust at the specified time, at a height of ten metres above the surface of the Earth. The WMO defines a wind gust as the maximum of the wind averaged over 3 second intervals. This duration is shorter than a model time step, and so the ECMWF Integrated Forecasting System (IFS) deduces the magnitude of a gust within each time step from the time-step-averaged surface stress, surface friction, wind shear and stability. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Instantaneous eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. Instantaneous large-scale surface precipitation fraction Dimensionless This parameter is the fraction of the grid box (0-1) covered by large-scale precipitation at the specified time. Large-scale precipitation is rain and snow that falls to the Earth's surface, and is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. Precipitation can also be due to convection generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box. Instantaneous moisture flux kg m-2 s-1 This parameter is the net rate of moisture exchange between the land/ocean surface and the atmosphere, due to the processes of evaporation (including evapotranspiration) and condensation, at the specified time. By convention, downward fluxes are positive, which means that evaporation is represented by negative values and condensation by positive values. Instantaneous northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. Instantaneous surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere, at the specified time, through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. The ECMWF convention for vertical fluxes is positive downwards. K index K This parameter is a measure of the potential for a thunderstorm to develop, calculated from the temperature and dew point temperature in the lower part of the atmosphere. The calculation uses the temperature at 850, 700 and 500 hPa and dewpoint temperature at 850 and 700 hPa. Higher values of K indicate a higher potential for the development of thunderstorms. This parameter is related to the probability of occurrence of a thunderstorm: <20 K: No thunderstorm, 20-25 K: Isolated thunderstorms, 26-30 K: Widely scattered thunderstorms, 31-35 K: Scattered thunderstorms, >35 K: Numerous thunderstorms. Lake bottom temperature K This parameter is the temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake cover Dimensionless This parameter is the proportion of a grid box covered by inland water bodies (lakes, reservoirs, rivers and coastal waters). Values vary between 0: no inland water, and 1: grid box is fully covered with inland water. This parameter is specified from observations and does not vary in time. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth m This parameter is the mean depth of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is specified from in-situ measurements and indirect estimates and does not vary in time. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake ice depth m This parameter is the thickness of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter is the thickness of that ice layer. Lake ice temperature K This parameter is the temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). It is the temperature at the ice/atmosphere or ice/snow interface. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer depth m This parameter is the thickness of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake mix-layer temperature K This parameter is the temperature of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake shape factor Dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) i.e., it describes the shape of the vertical temperature profile. It is used to calculate the lake bottom temperature and other lake-related parameters. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake total layer temperature K This parameter is the mean temperature of the total water column in inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. This parameter is the mean temperature over the two layers. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Land-sea mask Dimensionless This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless. In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water. In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water. This parameter does not vary in time. Large scale rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Rainfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation fraction s This parameter is the accumulation of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Large-scale snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Leaf area index, high vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "high". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Leaf area index, low vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "low". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Low cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a "standard atmosphere")). Assumptions are made about the degree of overlap/randomness between clouds in different model levels. This parameter has values from 0 to 1. Low vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "low". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Magnitude of turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the magnitude of the accumulated stress on the Earth's surface, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Maximum individual wave height m This parameter is an estimate of the height of the expected highest individual wave within a 20 minute time window. It can be used as a guide to the likelihood of extreme or freak waves. The interactions between waves are non-linear and occasionally concentrate wave energy giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered as a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Mean boundary layer dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean convective precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean direction of total swell degrees This parameter is the mean direction of waves associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of wind waves degrees The mean direction of waves generated by local winds. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea wave spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean eastward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean evaporation rate kg m-2 s-1 This parameter is the amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Mean gravity wave dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean large-scale precipitation fraction Dimensionless This parameter is the mean of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean large-scale precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean magnitude of turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the magnitude of the mean stress on the Earth's surface, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean northward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean period of total swell s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea associated with swell, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. Mean period of wind waves s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea generated by local winds, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea spectrum. Mean potential evaporation rate kg m-2 s-1 This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean sea level pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of mean sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at mean sea level. It is calculated over all surfaces - land, sea and inland water. Maps of mean sea level pressure are used to identify the locations of low and high pressure weather systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds. The units of this parameter are pascals (Pa). Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Mean snow evaporation rate kg m-2 s-1 This parameter is the average rate of snow evaporation from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snow evaporation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Mean snowfall rate kg m-2 s-1 This parameter is the rate of snowfall at the Earth's surface. It is the sum of large-scale and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean snowmelt rate kg m-2 s-1 This parameter is the rate of snow melt in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the melting would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean square slope of waves Dimensionless This parameter can be related analytically to the average slope of combined wind-sea and swell waves. It can also be expressed as a function of wind speed under some statistical assumptions. The higher the slope, the steeper the waves. This parameter indicates the roughness of the sea/ocean surface which affects the interaction between ocean and atmosphere. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. Mean sub-surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface direct short-wave radiation flux W m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux, clear sky W m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward UV radiation flux W m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux, clear sky W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface latent heat flux W m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux, clear sky W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top downward short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux W m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux, clear sky W m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean top net short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux, clear sky W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean total precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface. It is the sum of the rates due to large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean vertical gradient of refractivity inside trapping layer m-1 Mean vertical gradient of atmospheric refractivity inside the trapping layer. Mean vertically integrated moisture divergence kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean wave direction degree true This parameter is the mean direction of ocean/sea surface waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use this type of wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of first swell partition degrees This parameter is the mean direction of waves in the first swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of second swell partition degrees This parameter is the mean direction of waves in the second swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of third swell partition degrees This parameter is the mean direction of waves in the third swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave period s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use such wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. Mean wave period based on first moment s This parameter is the reciprocal of the mean frequency of the wave components that represent the sea state. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for swell s This parameter is the reciprocal of the mean frequency of the wave components associated with swell. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for wind waves s This parameter is the reciprocal of the mean frequency of the wave components generated by local winds. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with wind waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for swell s This parameter is equivalent to the zero-crossing mean wave period for swell. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for wind waves s This parameter is equivalent to the zero-crossing mean wave period for waves generated by local winds. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period of first swell partition s This parameter is the mean period of waves in the first swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of second swell partition s This parameter is the mean period of waves in the second swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of third swell partition s This parameter is the mean period of waves in the third swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third swell partition might be from one system at one location and a different system at the neighbouring location). Mean zero-crossing wave period s This parameter represents the mean length of time between occasions where the sea/ocean surface crosses mean sea level. In combination with wave height information, it could be used to assess the length of time that a coastal structure might be under water, for example. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). In the ECMWF Integrated Forecasting System (IFS) this parameter is calculated from the characteristics of the two-dimensional wave spectrum. Medium cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a "standard atmosphere")). The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. Minimum vertical gradient of refractivity inside trapping layer m-1 Minimum vertical gradient of atmospheric refractivity inside the trapping layer. Model bathymetry m This parameter is the depth of water from the surface to the bottom of the ocean. It is used by the ocean wave model to specify the propagation properties of the different waves that could be present. Note that the ocean wave model grid is too coarse to resolve some small islands and mountains on the bottom of the ocean, but they can have an impact on surface ocean waves. The ocean wave model has been modified to reduce the wave energy flowing around or over features at spatial scales smaller than the grid box. Near IR albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Normalized energy flux into ocean Dimensionless This parameter is the normalised vertical flux of turbulent kinetic energy from ocean waves into the ocean. The energy flux is calculated from an estimation of the loss of wave energy due to white capping waves. A white capping wave is one that appears white at its crest as it breaks, due to air being mixed into the water. When waves break in this way, there is a transfer of energy from the waves to the ocean. Such a flux is defined to be negative. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into waves Dimensionless This parameter is the normalised vertical flux of energy from wind into the ocean waves. A positive flux implies a flux into the waves. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized stress into ocean Dimensionless This parameter is the normalised surface stress, or momentum flux, from the air into the ocean due to turbulence at the air-sea interface and breaking waves. It does not include the flux used to generate waves. The ECMWF convention for vertical fluxes is positive downwards. The stress has units of Newtons per metre squared, and this is normalised by being divided by the product of air density and the square of the friction velocity. Northward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Northward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Ocean surface stress equivalent 10m neutral wind direction degrees This parameter is the direction from which the "neutral wind" blows, in degrees clockwise from true north, at a height of ten metres above the surface of the Earth. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind direction used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind speed m s-1 This parameter is the horizontal speed of the "neutral wind", at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind speed used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Period corresponding to maximum individual wave height s This parameter is the period of the expected highest individual wave within a 20-minute time window. It can be used as a guide to the characteristics of extreme or freak waves. Wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. Occasionally waves of different periods reinforce and interact non-linearly giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered to be a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Potential evaporation m This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Precipitation type Dimensionless This parameter describes the type of precipitation at the surface, at the specified time. A precipitation type is assigned wherever there is a non-zero value of precipitation. In the ECMWF Integrated Forecasting System (IFS) there are only two predicted precipitation variables: rain and snow. Precipitation type is derived from these two predicted variables in combination with atmospheric conditions, such as temperature. Values of precipitation type defined in the IFS: 0: No precipitation, 1: Rain, 3: Freezing rain (i.e. supercooled raindrops which freeze on contact with the ground and other surfaces), 5: Snow, 6: Wet snow (i.e. snow particles which are starting to melt), 7: Mixture of rain and snow, 8: Ice pellets. These precipitation types are consistent with WMO Code Table 4.201. Other types in this WMO table are not defined in the IFS. The monthly mean procedure applied to such integers, will yield non-integer values. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Sea surface temperature K This parameter (SST) is the temperature of sea water near the surface. In ERA5, this parameter is a foundation SST, which means there are no variations due to the daily cycle of the sun (diurnal variations). SST, in ERA5, is given by two external providers. Before September 2007, SST from the HadISST2 dataset is used and from September 2007 onwards, the OSTIA dataset is used. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Sea-ice cover Dimensionless This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land-sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover. In ERA5, sea-ice cover is given by two external providers. Before 1979 the HadISST2 dataset is used. From 1979 to August 2007 the OSI SAF (409a) dataset is used and from September 2007 the OSI SAF oper dataset is used. Sea ice is frozen sea water which floats on the surface of the ocean. Sea ice does not include ice which forms on land such as glaciers, icebergs and ice-sheets. It also excludes ice shelves which are anchored on land, but protrude out over the surface of the ocean. These phenomena are not modelled by the IFS. Long-term monitoring of sea ice is important for understanding climate change. Sea ice also affects shipping routes through the polar regions. Significant height of combined wind waves and swell m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. This parameter can be used to assess sea state and swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of total swell m This parameter represents the average height of the highest third of surface ocean/sea waves associated with swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of total swell only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional total swell spectrum. The total swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of wind waves m This parameter represents the average height of the highest third of surface ocean/sea waves generated by the local wind. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wind-sea wave spectrum. The wind-sea wave spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are still under the influence of the local wind. This parameter can be used to assess wind-sea waves. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of first swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the first swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of second swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the second swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of third swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the third swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Skin reservoir content m of water equivalent This parameter is the amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of "skin reservoir content" a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the "skin reservoir" by evaporation. Skin temperature K This parameter is the temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Slope of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, angle and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the slope of the sub-grid valleys, hills and mountains. A flat surface has a value of 0, and a 45 degree slope has a value of 0.5. This parameter does not vary in time. Snow albedo Dimensionless This parameter is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter changes with snow age and also depends on vegetation height. It has a range of values between 0 and 1. For low vegetation, it ranges between 0.52 for old snow and 0.88 for fresh snow. For high vegetation with snow underneath, it depends on vegetation type and has values between 0.27 and 0.38. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean snow albedo, grid point value would be contaminated with fictitious zero snow values. Snow density kg m-3 This parameter is the mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean snow density, grid point value would be contaminated with fictitious zero snow values. Snow depth m of water equivalent This parameter is the amount of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from snow from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the evaporated snow (from the snow-covered area of a grid box) were liquid and were spread evenly over the whole grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Snowmelt m of water equivalent This parameter is the accumulated amount of water that has melted from snow in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the melted snow (from the snow-covered area of a grid box) were spread evenly over the whole grid box. For example, if half the grid box were covered in snow with a water equivalent depth of 0.02m, this parameter would have a value of 0.01m. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Soil temperature level 1 K This parameter is the temperature of the soil at level 1 (in the middle of layer 1). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K This parameter is the temperature of the soil at level 2 (in the middle of layer 2). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 3 K This parameter is the temperature of the soil at level 3 (in the middle of layer 3). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 4 K This parameter is the temperature of the soil at level 4 (in the middle of layer 4). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil type Dimensionless This parameter is the texture (or classification) of soil used by the land surface scheme of the ECMWF Integrated Forecasting System (IFS) to predict the water holding capacity of soil in soil moisture and runoff calculations. It is derived from the root zone data (30-100 cm below the surface) of the FAO/UNESCO Digital Soil Map of the World, DSMW (FAO, 2003), which exists at a resolution of 5' X 5' (about 10 km). The seven soil types are: 1: Coarse, 2: Medium, 3: Medium fine, 4: Fine, 5: Very fine, 6: Organic, 7: Tropical organic. A value of 0 indicates a non-land point. This parameter does not vary in time. Standard deviation of filtered subgrid orography m Climatological parameter (scales between approximately 3 and 22 km are included). This parameter does not vary in time. Standard deviation of orography Dimensionless This parameter is one of four parameters (the others being angle of sub-gridscale orography, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the standard deviation of the height of the sub-grid valleys, hills and mountains within a grid box. This parameter does not vary in time. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface latent heat flux J m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation, clear sky J m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation, clear sky J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of land, sea and inland water. It is a measure of the weight of all the air in a column vertically above a point on the Earth's surface. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure weather systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa). Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface sensible heat flux J m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downward, clear sky J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downward, clear sky J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. TOA incident solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Temperature of snow layer K This parameter gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean temperature of snow layer, grid point value would be contaminated with fictitious zero snow values. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Top net solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation, clear sky J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation J m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation, clear sky J m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). Note that OLR is typically shown in units of watts per square metre (W m-2 ). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. Total cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights. Cloud fractions vary from 0 to 1. Total column cloud ice water kg m-2 This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud liquid water kg m-2 This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can also be referred to as total ozone, or vertically integrated ozone. The values are dominated by ozone within the stratosphere. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. In the IFS, the units for total ozone are kilograms per square metre, but before 12/06/2001 dobson units were used. Dobson units (DU) are still used extensively for total column ozone. 1 DU = 2.1415E-5 kg m-2 Total column rain water kg m-2 This parameter is the total amount of water in droplets of raindrop size (which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column snow water kg m-2 This parameter is the total amount of water in the form of snow (aggregated ice crystals which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column supercooled liquid water kg m-2 This parameter is the total amount of supercooled water in a column extending from the surface of the Earth to the top of the atmosphere. Supercooled water is water that exists in liquid form below 0oC. It is common in cold clouds and is important in the formation of precipitation. Also, supercooled water in clouds extending to the surface (i.e., fog) can cause icing/riming of various structures. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column water kg m-2 This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for. Total column water vapour kg m-2 This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Total precipitation m This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Total sky direct solar radiation at surface J m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Total totals index K This parameter gives an indication of the probability of occurrence of a thunderstorm and its severity by using the vertical gradient of temperature and humidity. The values of this index indicate the following: <44 K: Thunderstorms not likely, 44-50 K: Thunderstorms likely, 51-52 K: Isolated severe thunderstorms, 53-56 K: Widely scattered severe thunderstorms, 56-60 K: Scattered severe thunderstorms more likely. The total totals index is the temperature difference between 850 hPa (near surface) and 500 hPa (mid-troposphere) (lapse rate) plus a measure of the moisture content between 850 hPa and 500 hPa. The probability of deep convection tends to increase with increasing lapse rate and atmospheric moisture content. There are a number of limitations to this index. Also, the interpretation of the index value varies with season and location. Trapping layer base height m Trapping layer base height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer top height m Trapping layer top height as diagnosed from the vertical gradient of atmospheric refractivity. Type of high vegetation Dimensionless This parameter indicates the 6 types of high vegetation recognised by the ECMWF Integrated Forecasting System: 3 = Evergreen needleleaf trees, 4 = Deciduous needleleaf trees, 5 = Deciduous broadleaf trees, 6 = Evergreen broadleaf trees, 18 = Mixed forest/woodland, 19 = Interrupted forest. A value of 0 indicates a point without high vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of low vegetation Dimensionless This parameter indicates the 10 types of low vegetation recognised by the ECMWF Integrated Forecasting System: 1 = Crops, Mixed farming, 2 = Grass, 7 = Tall grass, 9 = Tundra, 10 = Irrigated crops, 11 = Semidesert, 13 = Bogs and marshes, 16 = Evergreen shrubs, 17 = Deciduous shrubs, 20 = Water and land mixtures. A value of 0 indicates a point without low vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. U-component stokes drift m s-1 This parameter is the eastward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. UV visible albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. This parameter varies between 0 and 1. UV visible albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. V-component stokes drift m s-1 This parameter is the northward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. Vertical integral of divergence of cloud frozen water flux kg m-2 s-1 The vertical integral of the cloud frozen water flux is the horizontal rate of flow of cloud frozen water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud frozen water spreading outward from a point, per square metre. This parameter is positive for cloud frozen water that is spreading out, or diverging, and negative for the opposite, for cloud frozen water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud frozen water. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of divergence of cloud liquid water flux kg m-2 s-1 The vertical integral of the cloud liquid water flux is the horizontal rate of flow of cloud liquid water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud liquid water spreading outward from a point, per square metre. This parameter is positive for cloud liquid water that is spreading out, or diverging, and negative for the opposite, for cloud liquid water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud liquid water. Vertical integral of divergence of geopotential flux W m-2 The vertical integral of the geopotential flux is the horizontal rate of flow of geopotential, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of geopotential spreading outward from a point, per square metre. This parameter is positive for geopotential that is spreading out, or diverging, and negative for the opposite, for geopotential that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of geopotential. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of kinetic energy flux W m-2 The vertical integral of the kinetic energy flux is the horizontal rate of flow of kinetic energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of kinetic energy spreading outward from a point, per square metre. This parameter is positive for kinetic energy that is spreading out, or diverging, and negative for the opposite, for kinetic energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of kinetic energy. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of mass flux kg m-2 s-1 The vertical integral of the mass flux is the horizontal rate of flow of mass, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of mass spreading outward from a point, per square metre. This parameter is positive for mass that is spreading out, or diverging, and negative for the opposite, for mass that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of mass. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of divergence of moisture flux kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Vertical integral of divergence of ozone flux kg m-2 s-1 The vertical integral of the ozone flux is the horizontal rate of flow of ozone, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of ozone spreading outward from a point, per square metre. This parameter is positive for ozone that is spreading out, or diverging, and negative for the opposite, for ozone that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of ozone. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of divergence of thermal energy flux W m-2 The vertical integral of the thermal energy flux is the horizontal rate of flow of thermal energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of thermal energy spreading outward from a point, per square metre. This parameter is positive for thermal energy that is spreading out, or diverging, and negative for the opposite, for thermal energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of thermal energy. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the flow of thermal energy through the climate system and to investigate the atmospheric energy budget. Vertical integral of divergence of total energy flux W m-2 The vertical integral of the total energy flux is the horizontal rate of flow of total energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of total energy spreading outward from a point, per square metre. This parameter is positive for total energy that is spreading out, or diverging, and negative for the opposite, for total energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of total energy. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of eastward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the eastward direction, per meter across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of eastward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from west to east. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of eastward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of energy conversion W m-2 This parameter is one contribution to the amount of energy being converted between kinetic energy, and internal plus potential energy, for a column of air extending from the surface of the Earth to the top of the atmosphere. Negative values indicate a conversion to kinetic energy from potential plus internal energy. This parameter can be used to study the atmospheric energy budget. The circulation of the atmosphere can also be considered in terms of energy conversions. Vertical integral of kinetic energy J m-2 This parameter is the vertical integral of kinetic energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of mass of atmosphere kg m-2 This parameter is the total mass of air for a column extending from the surface of the Earth to the top of the atmosphere, per square metre. This parameter is calculated by dividing surface pressure by the Earth's gravitational acceleration, g (=9.80665 m s-2 ), and has units of kilograms per square metre. This parameter can be used to study the atmospheric mass budget. Vertical integral of mass tendency kg m-2 s-1 This parameter is the rate of change of the mass of a column of air extending from the Earth's surface to the top of the atmosphere. An increasing mass of the column indicates rising surface pressure. In contrast, a decrease indicates a falling surface pressure. The mass of the column is calculated by dividing pressure at the Earth's surface by the gravitational acceleration, g (=9.80665 m s-2 ). This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of northward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from south to north. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of northward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of potential and internal energy J m-2 This parameter is the mass weighted vertical integral of potential and internal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential, internal and latent energy J m-2 This parameter is the mass weighted vertical integral of potential, internal and latent energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The latent energy refers to the energy associated with the water vapour in the atmosphere and is equal to the energy required to convert liquid water into water vapour. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of temperature K kg m-2 This parameter is the mass-weighted vertical integral of temperature for a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can be used to study the atmospheric energy budget. Vertical integral of thermal energy J m-2 This parameter is the mass-weighted vertical integral of thermal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Thermal energy is calculated from the product of temperature and the specific heat capacity of air at constant pressure. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of total energy J m-2 This parameter is the vertical integral of total energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertically integrated moisture divergence kg m-2 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm. Volumetric soil water layer 1 m3 m-3 This parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 This parameter is the volume of water in soil layer 2 (7 - 28cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 3 m3 m-3 This parameter is the volume of water in soil layer 3 (28 - 100cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 4 m3 m-3 This parameter is the volume of water in soil layer 4 (100 - 289cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Wave spectral directional width Dimensionless This parameter indicates whether waves (generated by local winds and associated with swell) are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for swell Dimensionless This parameter indicates whether waves associated with swell are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for wind waves Dimensionless This parameter indicates whether waves generated by the local wind are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral kurtosis Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It shows how much of the probability density function of the sea surface elevation exists in the tails of the distribution. So, a positive kurtosis (typical range 0.0 to 0.06) means more frequent occurrences of very extreme values (either above or below the mean), relative to a normal distribution. Wave spectral peakedness Dimensionless This parameter is a statistical measure used to forecast extreme or freak waves. It is a measure of the relative width of the ocean/sea wave frequency spectrum (i.e., whether the ocean/sea wave field is made up of a narrow or broad range of frequencies). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). When the wave field is more focussed around a narrow range of frequencies, the probability of freak/extreme waves increases. This parameter is Goda's peakedness factor and is used to calculate the Benjamin-Feir Index (BFI). The BFI is in turn used to estimate the probability and nature of extreme/freak waves. Wave spectral skewness Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It is a measure of the asymmetry of the probability density function of the sea surface elevation. So, a positive/negative skewness (typical range -0.2 to 0.12) means more frequent occurrences of extreme values above/below the mean, relative to a normal distribution. Zero degree level m The height above the Earth's surface where the temperature passes from positive to negative values, corresponding to the top of a warm layer, at the specified time. This parameter can be used to help forecast snow. If more than one warm layer is encountered, then the zero degree level corresponds to the top of the second atmospheric layer. This parameter is set to zero when the temperature in the whole atmosphere is below 0℃. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 100m u-component of wind m s-1 This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. 100m u-component of wind m s-1 This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. 100m v-component of wind m s-1 This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. 100m v-component of wind m s-1 This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. 10m u-component of neutral wind m s-1 This parameter is the eastward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m u-component of neutral wind m s-1 This parameter is the eastward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m u-component of wind m s-1 This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). 10m u-component of wind m s-1 This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). 10m v-component of neutral wind m s-1 This parameter is the northward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m v-component of neutral wind m s-1 This parameter is the northward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m v-component of wind m s-1 This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). 10m v-component of wind m s-1 This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). 10m wind speed m s-1 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). The eastward and northward components of the horizontal wind at 10m are also available as parameters. 10m wind speed m s-1 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). The eastward and northward components of the horizontal wind at 10m are also available as parameters. 2m dewpoint temperature K This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m dewpoint temperature K This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Air density over the oceans kg m-3 This parameter is the mass of air per cubic metre over the oceans, derived from the temperature, specific humidity and pressure at the lowest model level in the atmospheric model. This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Air density over the oceans kg m-3 This parameter is the mass of air per cubic metre over the oceans, derived from the temperature, specific humidity and pressure at the lowest model level in the atmospheric model. This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Angle of sub-gridscale orography radians This parameter is one of four parameters (the others being standard deviation, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. The angle of the sub-grid scale orography characterises the geographical orientation of the terrain in the horizontal plane (from a bird's-eye view) relative to an eastwards axis. This parameter does not vary in time. Angle of sub-gridscale orography radians This parameter is one of four parameters (the others being standard deviation, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. The angle of the sub-grid scale orography characterises the geographical orientation of the terrain in the horizontal plane (from a bird's-eye view) relative to an eastwards axis. This parameter does not vary in time. Anisotropy of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, slope and angle of sub-gridscale orography) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter is a measure of how much the shape of the terrain in the horizontal plane (from a bird's-eye view) is distorted from a circle. A value of one is a circle, less than one an ellipse, and 0 is a ridge. In the case of a ridge, wind blowing parallel to it does not exert any drag on the flow, but wind blowing perpendicular to it exerts the maximum drag. This parameter does not vary in time. Anisotropy of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, slope and angle of sub-gridscale orography) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter is a measure of how much the shape of the terrain in the horizontal plane (from a bird's-eye view) is distorted from a circle. A value of one is a circle, less than one an ellipse, and 0 is a ridge. In the case of a ridge, wind blowing parallel to it does not exert any drag on the flow, but wind blowing perpendicular to it exerts the maximum drag. This parameter does not vary in time. Benjamin-feir index Dimensionless This parameter is used to calculate the likelihood of freak ocean waves, which are waves that are higher than twice the mean height of the highest third of waves. Large values of this parameter (in practice of the order 1) indicate increased probability of the occurrence of freak waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived from the statistics of the two-dimensional wave spectrum. More precisely, it is the square of the ratio of the integral ocean wave steepness and the relative width of the frequency spectrum of the waves. Further information on the calculation of this parameter is given in Section 10.6 of the ECMWF Wave Model documentation. Benjamin-feir index Dimensionless This parameter is used to calculate the likelihood of freak ocean waves, which are waves that are higher than twice the mean height of the highest third of waves. Large values of this parameter (in practice of the order 1) indicate increased probability of the occurrence of freak waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived from the statistics of the two-dimensional wave spectrum. More precisely, it is the square of the ratio of the integral ocean wave steepness and the relative width of the frequency spectrum of the waves. Further information on the calculation of this parameter is given in Section 10.6 of the ECMWF Wave Model documentation. Boundary layer dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Boundary layer dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Boundary layer height m This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface. The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop. The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review. Boundary layer height m This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface. The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop. The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review. Charnock Dimensionless This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind. When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by the ECMWF Wave Model. Charnock Dimensionless This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind. When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by the ECMWF Wave Model. Clear-sky direct solar radiation at surface J m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Clear-sky direct solar radiation at surface J m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Cloud base height m The height above the Earth's surface of the base of the lowest cloud layer, at the specified time. This parameter is calculated by searching from the second lowest model level upwards, to the height of the level where cloud fraction becomes greater than 1% and condensate content greater than 1.E-6 kg kg-1. Fog (i.e., cloud in the lowest model layer) is not considered when defining cloud base height. Cloud base height m The height above the Earth's surface of the base of the lowest cloud layer, at the specified time. This parameter is calculated by searching from the second lowest model level upwards, to the height of the level where cloud fraction becomes greater than 1% and condensate content greater than 1.E-6 kg kg-1. Fog (i.e., cloud in the lowest model layer) is not considered when defining cloud base height. Coefficient of drag with waves Dimensionless This parameter is the resistance that ocean waves exert on the atmosphere. It is sometimes also called a "friction coefficient". It is calculated by the wave model as the ratio of the square of the friction velocity, to the square of the neutral wind speed at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. Coefficient of drag with waves Dimensionless This parameter is the resistance that ocean waves exert on the atmosphere. It is sometimes also called a "friction coefficient". It is calculated by the wave model as the ratio of the square of the friction velocity, to the square of the neutral wind speed at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. Convective available potential energy J kg-1 This is an indication of the instability (or stability) of the atmosphere and can be used to assess the potential for the development of convection, which can lead to heavy rainfall, thunderstorms and other severe weather. In the ECMWF Integrated Forecasting System (IFS), CAPE is calculated by considering parcels of air departing at different model levels below the 350 hPa level. If a parcel of air is more buoyant (warmer and/or with more moisture) than its surrounding environment, it will continue to rise (cooling as it rises) until it reaches a point where it no longer has positive buoyancy. CAPE is the potential energy represented by the total excess buoyancy. The maximum CAPE produced by the different parcels is the value retained. Large positive values of CAPE indicate that an air parcel would be much warmer than its surrounding environment and therefore, very buoyant. CAPE is related to the maximum potential vertical velocity of air within an updraft; thus, higher values indicate greater potential for severe weather. Observed values in thunderstorm environments often may exceed 1000 joules per kilogram (J kg-1), and in extreme cases may exceed 5000 J kg-1. The calculation of this parameter assumes: (i) the parcel of air does not mix with surrounding air; (ii) ascent is pseudo-adiabatic (all condensed water falls out) and (iii) other simplifications related to the mixed-phase condensational heating. Convective available potential energy J kg-1 This is an indication of the instability (or stability) of the atmosphere and can be used to assess the potential for the development of convection, which can lead to heavy rainfall, thunderstorms and other severe weather. In the ECMWF Integrated Forecasting System (IFS), CAPE is calculated by considering parcels of air departing at different model levels below the 350 hPa level. If a parcel of air is more buoyant (warmer and/or with more moisture) than its surrounding environment, it will continue to rise (cooling as it rises) until it reaches a point where it no longer has positive buoyancy. CAPE is the potential energy represented by the total excess buoyancy. The maximum CAPE produced by the different parcels is the value retained. Large positive values of CAPE indicate that an air parcel would be much warmer than its surrounding environment and therefore, very buoyant. CAPE is related to the maximum potential vertical velocity of air within an updraft; thus, higher values indicate greater potential for severe weather. Observed values in thunderstorm environments often may exceed 1000 joules per kilogram (J kg-1), and in extreme cases may exceed 5000 J kg-1. The calculation of this parameter assumes: (i) the parcel of air does not mix with surrounding air; (ii) ascent is pseudo-adiabatic (all condensed water falls out) and (iii) other simplifications related to the mixed-phase condensational heating. Convective inhibition J kg-1 This parameter is a measure of the amount of energy required for convection to commence. If the value of this parameter is too high, then deep, moist convection is unlikely to occur even if the convective available potential energy or convective available potential energy shear are large. CIN values greater than 200 J kg-1 would be considered high. An atmospheric layer where temperature increases with height (known as a temperature inversion) would inhibit convective uplift and is a situation in which convective inhibition would be large. Convective inhibition J kg-1 This parameter is a measure of the amount of energy required for convection to commence. If the value of this parameter is too high, then deep, moist convection is unlikely to occur even if the convective available potential energy or convective available potential energy shear are large. CIN values greater than 200 J kg-1 would be considered high. An atmospheric layer where temperature increases with height (known as a temperature inversion) would inhibit convective uplift and is a situation in which convective inhibition would be large. Convective precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Rainfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Rainfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Downward UV radiation at the surface J m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Downward UV radiation at the surface J m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Duct base height m Duct base height as diagnosed from the vertical gradient of atmospheric refractivity. Duct base height m Duct base height as diagnosed from the vertical gradient of atmospheric refractivity. Eastward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Eastward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Eastward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Eastward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Forecast albedo Dimensionless This parameter is a measure of the reflectivity of the Earth's surface. It is the fraction of short-wave (solar) radiation reflected by the Earth's surface, for diffuse radiation, assuming a fixed spectrum of downward short-wave radiation at the surface. The values of this parameter vary between zero and one. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Short-wave radiation from the Sun is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Forecast albedo Dimensionless This parameter is a measure of the reflectivity of the Earth's surface. It is the fraction of short-wave (solar) radiation reflected by the Earth's surface, for diffuse radiation, assuming a fixed spectrum of downward short-wave radiation at the surface. The values of this parameter vary between zero and one. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Short-wave radiation from the Sun is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Forecast logarithm of surface roughness for heat Dimensionless This parameter is the natural logarithm of the roughness length for heat. The surface roughness for heat is a measure of the surface resistance to heat transfer. This parameter is used to determine the air to surface transfer of heat. For given atmospheric conditions, a higher surface roughness for heat means that it is more difficult for the air to exchange heat with the surface. A lower surface roughness for heat means that it is easier for the air to exchange heat with the surface. Over the ocean, surface roughness for heat depends on the waves. Over sea-ice, it has a constant value of 0.001 m. Over land, it is derived from the vegetation type and snow cover. Forecast logarithm of surface roughness for heat Dimensionless This parameter is the natural logarithm of the roughness length for heat. The surface roughness for heat is a measure of the surface resistance to heat transfer. This parameter is used to determine the air to surface transfer of heat. For given atmospheric conditions, a higher surface roughness for heat means that it is more difficult for the air to exchange heat with the surface. A lower surface roughness for heat means that it is easier for the air to exchange heat with the surface. Over the ocean, surface roughness for heat depends on the waves. Over sea-ice, it has a constant value of 0.001 m. Over land, it is derived from the vegetation type and snow cover. Forecast surface roughness m This parameter is the aerodynamic roughness length in metres. It is a measure of the surface resistance. This parameter is used to determine the air to surface transfer of momentum. For given atmospheric conditions, a higher surface roughness causes a slower near-surface wind speed. Over ocean, surface roughness depends on the waves. Over land, surface roughness is derived from the vegetation type and snow cover. Forecast surface roughness m This parameter is the aerodynamic roughness length in metres. It is a measure of the surface resistance. This parameter is used to determine the air to surface transfer of momentum. For given atmospheric conditions, a higher surface roughness causes a slower near-surface wind speed. Over ocean, surface roughness depends on the waves. Over land, surface roughness is derived from the vegetation type and snow cover. Free convective velocity over the oceans m s-1 This parameter is an estimate of the vertical velocity of updraughts generated by free convection. Free convection is fluid motion induced by buoyancy forces, which are driven by density gradients. The free convective velocity is used to estimate the impact of wind gusts on ocean wave growth. It is calculated at the height of the lowest temperature inversion (the height above the surface of the Earth where the temperature increases with height). This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Free convective velocity over the oceans m s-1 This parameter is an estimate of the vertical velocity of updraughts generated by free convection. Free convection is fluid motion induced by buoyancy forces, which are driven by density gradients. The free convective velocity is used to estimate the impact of wind gusts on ocean wave growth. It is calculated at the height of the lowest temperature inversion (the height above the surface of the Earth where the temperature increases with height). This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Friction velocity m s-1 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is a theoretical wind speed at the Earth's surface that expresses the magnitude of stress. It is calculated by dividing the surface stress by air density and taking its square root. For turbulent flow, the friction velocity is approximately constant in the lowest few metres of the atmosphere. This parameter increases with the roughness of the surface. It is used to calculate the way wind changes with height in the lowest levels of the atmosphere. Friction velocity m s-1 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is a theoretical wind speed at the Earth's surface that expresses the magnitude of stress. It is calculated by dividing the surface stress by air density and taking its square root. For turbulent flow, the friction velocity is approximately constant in the lowest few metres of the atmosphere. This parameter increases with the roughness of the surface. It is used to calculate the way wind changes with height in the lowest levels of the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location at the surface of the Earth, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The (surface) geopotential height (orography) can be calculated by dividing the (surface) geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2 ). This parameter does not vary in time. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location at the surface of the Earth, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The (surface) geopotential height (orography) can be calculated by dividing the (surface) geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2 ). This parameter does not vary in time. Gravity wave dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Gravity wave dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. High cloud cover Dimensionless The proportion of a grid box covered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a "standard atmosphere")). The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. High cloud cover Dimensionless The proportion of a grid box covered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a "standard atmosphere")). The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. High vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "high". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. High vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "high". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Ice temperature layer 1 K This parameter is the sea-ice temperature in layer 1 (0 to 7cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 1, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 1 K This parameter is the sea-ice temperature in layer 1 (0 to 7cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 1, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 2 K This parameter is the sea-ice temperature in layer 2 (7 to 28cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 2, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 2 K This parameter is the sea-ice temperature in layer 2 (7 to 28cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 2, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 3 K This parameter is the sea-ice temperature in layer 3 (28 to 100cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 3, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 3 K This parameter is the sea-ice temperature in layer 3 (28 to 100cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 3, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 4 K This parameter is the sea-ice temperature in layer 4 (100 to 150cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 4, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 4 K This parameter is the sea-ice temperature in layer 4 (100 to 150cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 4, grid point value would be contaminated with fictitious zero ice values. Instantaneous 10m wind gust m s-1 This parameter is the maximum wind gust at the specified time, at a height of ten metres above the surface of the Earth. The WMO defines a wind gust as the maximum of the wind averaged over 3 second intervals. This duration is shorter than a model time step, and so the ECMWF Integrated Forecasting System (IFS) deduces the magnitude of a gust within each time step from the time-step-averaged surface stress, surface friction, wind shear and stability. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Instantaneous 10m wind gust m s-1 This parameter is the maximum wind gust at the specified time, at a height of ten metres above the surface of the Earth. The WMO defines a wind gust as the maximum of the wind averaged over 3 second intervals. This duration is shorter than a model time step, and so the ECMWF Integrated Forecasting System (IFS) deduces the magnitude of a gust within each time step from the time-step-averaged surface stress, surface friction, wind shear and stability. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Instantaneous eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. Instantaneous eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. Instantaneous large-scale surface precipitation fraction Dimensionless This parameter is the fraction of the grid box (0-1) covered by large-scale precipitation at the specified time. Large-scale precipitation is rain and snow that falls to the Earth's surface, and is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. Precipitation can also be due to convection generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box. Instantaneous large-scale surface precipitation fraction Dimensionless This parameter is the fraction of the grid box (0-1) covered by large-scale precipitation at the specified time. Large-scale precipitation is rain and snow that falls to the Earth's surface, and is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. Precipitation can also be due to convection generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box. Instantaneous moisture flux kg m-2 s-1 This parameter is the net rate of moisture exchange between the land/ocean surface and the atmosphere, due to the processes of evaporation (including evapotranspiration) and condensation, at the specified time. By convention, downward fluxes are positive, which means that evaporation is represented by negative values and condensation by positive values. Instantaneous moisture flux kg m-2 s-1 This parameter is the net rate of moisture exchange between the land/ocean surface and the atmosphere, due to the processes of evaporation (including evapotranspiration) and condensation, at the specified time. By convention, downward fluxes are positive, which means that evaporation is represented by negative values and condensation by positive values. Instantaneous northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. Instantaneous northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. Instantaneous surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere, at the specified time, through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. The ECMWF convention for vertical fluxes is positive downwards. Instantaneous surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere, at the specified time, through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. The ECMWF convention for vertical fluxes is positive downwards. K index K This parameter is a measure of the potential for a thunderstorm to develop, calculated from the temperature and dew point temperature in the lower part of the atmosphere. The calculation uses the temperature at 850, 700 and 500 hPa and dewpoint temperature at 850 and 700 hPa. Higher values of K indicate a higher potential for the development of thunderstorms. This parameter is related to the probability of occurrence of a thunderstorm: <20 K: No thunderstorm, 20-25 K: Isolated thunderstorms, 26-30 K: Widely scattered thunderstorms, 31-35 K: Scattered thunderstorms, >35 K: Numerous thunderstorms. K index K This parameter is a measure of the potential for a thunderstorm to develop, calculated from the temperature and dew point temperature in the lower part of the atmosphere. The calculation uses the temperature at 850, 700 and 500 hPa and dewpoint temperature at 850 and 700 hPa. Higher values of K indicate a higher potential for the development of thunderstorms. This parameter is related to the probability of occurrence of a thunderstorm: <20 K: No thunderstorm, 20-25 K: Isolated thunderstorms, 26-30 K: Widely scattered thunderstorms, 31-35 K: Scattered thunderstorms, >35 K: Numerous thunderstorms. Lake bottom temperature K This parameter is the temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake bottom temperature K This parameter is the temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake cover Dimensionless This parameter is the proportion of a grid box covered by inland water bodies (lakes, reservoirs, rivers and coastal waters). Values vary between 0: no inland water, and 1: grid box is fully covered with inland water. This parameter is specified from observations and does not vary in time. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake cover Dimensionless This parameter is the proportion of a grid box covered by inland water bodies (lakes, reservoirs, rivers and coastal waters). Values vary between 0: no inland water, and 1: grid box is fully covered with inland water. This parameter is specified from observations and does not vary in time. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth m This parameter is the mean depth of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is specified from in-situ measurements and indirect estimates and does not vary in time. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth m This parameter is the mean depth of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is specified from in-situ measurements and indirect estimates and does not vary in time. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake ice depth m This parameter is the thickness of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter is the thickness of that ice layer. Lake ice depth m This parameter is the thickness of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter is the thickness of that ice layer. Lake ice temperature K This parameter is the temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). It is the temperature at the ice/atmosphere or ice/snow interface. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake ice temperature K This parameter is the temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). It is the temperature at the ice/atmosphere or ice/snow interface. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer depth m This parameter is the thickness of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake mix-layer depth m This parameter is the thickness of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake mix-layer temperature K This parameter is the temperature of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer temperature K This parameter is the temperature of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake shape factor Dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) i.e., it describes the shape of the vertical temperature profile. It is used to calculate the lake bottom temperature and other lake-related parameters. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake shape factor Dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) i.e., it describes the shape of the vertical temperature profile. It is used to calculate the lake bottom temperature and other lake-related parameters. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake total layer temperature K This parameter is the mean temperature of the total water column in inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. This parameter is the mean temperature over the two layers. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake total layer temperature K This parameter is the mean temperature of the total water column in inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. This parameter is the mean temperature over the two layers. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Land-sea mask Dimensionless This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless. In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water. In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water. This parameter does not vary in time. Land-sea mask Dimensionless This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless. In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water. In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water. This parameter does not vary in time. Large scale rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Rainfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Rainfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation fraction s This parameter is the accumulation of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Large-scale precipitation fraction s This parameter is the accumulation of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Large-scale snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Leaf area index, high vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "high". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Leaf area index, high vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "high". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Leaf area index, low vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "low". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Leaf area index, low vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "low". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Low cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a "standard atmosphere")). Assumptions are made about the degree of overlap/randomness between clouds in different model levels. This parameter has values from 0 to 1. Low cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a "standard atmosphere")). Assumptions are made about the degree of overlap/randomness between clouds in different model levels. This parameter has values from 0 to 1. Low vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "low". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Low vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "low". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Magnitude of turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the magnitude of the accumulated stress on the Earth's surface, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Magnitude of turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the magnitude of the accumulated stress on the Earth's surface, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Maximum individual wave height m This parameter is an estimate of the height of the expected highest individual wave within a 20 minute time window. It can be used as a guide to the likelihood of extreme or freak waves. The interactions between waves are non-linear and occasionally concentrate wave energy giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered as a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Maximum individual wave height m This parameter is an estimate of the height of the expected highest individual wave within a 20 minute time window. It can be used as a guide to the likelihood of extreme or freak waves. The interactions between waves are non-linear and occasionally concentrate wave energy giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered as a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Mean boundary layer dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean boundary layer dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean convective precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean direction of total swell degrees This parameter is the mean direction of waves associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of total swell degrees This parameter is the mean direction of waves associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of wind waves degrees The mean direction of waves generated by local winds. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea wave spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of wind waves degrees The mean direction of waves generated by local winds. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea wave spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean eastward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean eastward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean evaporation rate kg m-2 s-1 This parameter is the amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Mean evaporation rate kg m-2 s-1 This parameter is the amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Mean gravity wave dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean gravity wave dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean large-scale precipitation fraction Dimensionless This parameter is the mean of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean large-scale precipitation fraction Dimensionless This parameter is the mean of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean large-scale precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean magnitude of turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the magnitude of the mean stress on the Earth's surface, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean magnitude of turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the magnitude of the mean stress on the Earth's surface, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean northward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean northward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean period of total swell s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea associated with swell, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. Mean period of total swell s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea associated with swell, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. Mean period of wind waves s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea generated by local winds, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea spectrum. Mean period of wind waves s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea generated by local winds, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea spectrum. Mean potential evaporation rate kg m-2 s-1 This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean potential evaporation rate kg m-2 s-1 This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean sea level pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of mean sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at mean sea level. It is calculated over all surfaces - land, sea and inland water. Maps of mean sea level pressure are used to identify the locations of low and high pressure weather systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds. The units of this parameter are pascals (Pa). Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Mean sea level pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of mean sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at mean sea level. It is calculated over all surfaces - land, sea and inland water. Maps of mean sea level pressure are used to identify the locations of low and high pressure weather systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds. The units of this parameter are pascals (Pa). Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Mean snow evaporation rate kg m-2 s-1 This parameter is the average rate of snow evaporation from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snow evaporation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Mean snow evaporation rate kg m-2 s-1 This parameter is the average rate of snow evaporation from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snow evaporation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Mean snowfall rate kg m-2 s-1 This parameter is the rate of snowfall at the Earth's surface. It is the sum of large-scale and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean snowfall rate kg m-2 s-1 This parameter is the rate of snowfall at the Earth's surface. It is the sum of large-scale and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean snowmelt rate kg m-2 s-1 This parameter is the rate of snow melt in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the melting would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean snowmelt rate kg m-2 s-1 This parameter is the rate of snow melt in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the melting would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean square slope of waves Dimensionless This parameter can be related analytically to the average slope of combined wind-sea and swell waves. It can also be expressed as a function of wind speed under some statistical assumptions. The higher the slope, the steeper the waves. This parameter indicates the roughness of the sea/ocean surface which affects the interaction between ocean and atmosphere. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. Mean square slope of waves Dimensionless This parameter can be related analytically to the average slope of combined wind-sea and swell waves. It can also be expressed as a function of wind speed under some statistical assumptions. The higher the slope, the steeper the waves. This parameter indicates the roughness of the sea/ocean surface which affects the interaction between ocean and atmosphere. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. Mean sub-surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean sub-surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface direct short-wave radiation flux W m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux W m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux, clear sky W m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux, clear sky W m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward UV radiation flux W m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward UV radiation flux W m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux, clear sky W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux, clear sky W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface latent heat flux W m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface latent heat flux W m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux, clear sky W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux, clear sky W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top downward short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top downward short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux W m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux W m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux, clear sky W m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean top net long-wave radiation flux, clear sky W m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean top net short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux, clear sky W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux, clear sky W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean total precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface. It is the sum of the rates due to large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean total precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface. It is the sum of the rates due to large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean vertical gradient of refractivity inside trapping layer m-1 Mean vertical gradient of atmospheric refractivity inside the trapping layer. Mean vertical gradient of refractivity inside trapping layer m-1 Mean vertical gradient of atmospheric refractivity inside the trapping layer. Mean vertically integrated moisture divergence kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean vertically integrated moisture divergence kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean wave direction degree true This parameter is the mean direction of ocean/sea surface waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use this type of wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction degree true This parameter is the mean direction of ocean/sea surface waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use this type of wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of first swell partition degrees This parameter is the mean direction of waves in the first swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of first swell partition degrees This parameter is the mean direction of waves in the first swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of second swell partition degrees This parameter is the mean direction of waves in the second swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of second swell partition degrees This parameter is the mean direction of waves in the second swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of third swell partition degrees This parameter is the mean direction of waves in the third swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of third swell partition degrees This parameter is the mean direction of waves in the third swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave period s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use such wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. Mean wave period s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use such wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. Mean wave period based on first moment s This parameter is the reciprocal of the mean frequency of the wave components that represent the sea state. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment s This parameter is the reciprocal of the mean frequency of the wave components that represent the sea state. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for swell s This parameter is the reciprocal of the mean frequency of the wave components associated with swell. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for swell s This parameter is the reciprocal of the mean frequency of the wave components associated with swell. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for wind waves s This parameter is the reciprocal of the mean frequency of the wave components generated by local winds. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with wind waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for wind waves s This parameter is the reciprocal of the mean frequency of the wave components generated by local winds. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with wind waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for swell s This parameter is equivalent to the zero-crossing mean wave period for swell. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for swell s This parameter is equivalent to the zero-crossing mean wave period for swell. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for wind waves s This parameter is equivalent to the zero-crossing mean wave period for waves generated by local winds. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for wind waves s This parameter is equivalent to the zero-crossing mean wave period for waves generated by local winds. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period of first swell partition s This parameter is the mean period of waves in the first swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of first swell partition s This parameter is the mean period of waves in the first swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of second swell partition s This parameter is the mean period of waves in the second swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of second swell partition s This parameter is the mean period of waves in the second swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of third swell partition s This parameter is the mean period of waves in the third swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of third swell partition s This parameter is the mean period of waves in the third swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third swell partition might be from one system at one location and a different system at the neighbouring location). Mean zero-crossing wave period s This parameter represents the mean length of time between occasions where the sea/ocean surface crosses mean sea level. In combination with wave height information, it could be used to assess the length of time that a coastal structure might be under water, for example. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). In the ECMWF Integrated Forecasting System (IFS) this parameter is calculated from the characteristics of the two-dimensional wave spectrum. Mean zero-crossing wave period s This parameter represents the mean length of time between occasions where the sea/ocean surface crosses mean sea level. In combination with wave height information, it could be used to assess the length of time that a coastal structure might be under water, for example. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). In the ECMWF Integrated Forecasting System (IFS) this parameter is calculated from the characteristics of the two-dimensional wave spectrum. Medium cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a "standard atmosphere")). The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. Medium cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a "standard atmosphere")). The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. Minimum vertical gradient of refractivity inside trapping layer m-1 Minimum vertical gradient of atmospheric refractivity inside the trapping layer. Minimum vertical gradient of refractivity inside trapping layer m-1 Minimum vertical gradient of atmospheric refractivity inside the trapping layer. Model bathymetry m This parameter is the depth of water from the surface to the bottom of the ocean. It is used by the ocean wave model to specify the propagation properties of the different waves that could be present. Note that the ocean wave model grid is too coarse to resolve some small islands and mountains on the bottom of the ocean, but they can have an impact on surface ocean waves. The ocean wave model has been modified to reduce the wave energy flowing around or over features at spatial scales smaller than the grid box. Model bathymetry m This parameter is the depth of water from the surface to the bottom of the ocean. It is used by the ocean wave model to specify the propagation properties of the different waves that could be present. Note that the ocean wave model grid is too coarse to resolve some small islands and mountains on the bottom of the ocean, but they can have an impact on surface ocean waves. The ocean wave model has been modified to reduce the wave energy flowing around or over features at spatial scales smaller than the grid box. Near IR albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Normalized energy flux into ocean Dimensionless This parameter is the normalised vertical flux of turbulent kinetic energy from ocean waves into the ocean. The energy flux is calculated from an estimation of the loss of wave energy due to white capping waves. A white capping wave is one that appears white at its crest as it breaks, due to air being mixed into the water. When waves break in this way, there is a transfer of energy from the waves to the ocean. Such a flux is defined to be negative. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into ocean Dimensionless This parameter is the normalised vertical flux of turbulent kinetic energy from ocean waves into the ocean. The energy flux is calculated from an estimation of the loss of wave energy due to white capping waves. A white capping wave is one that appears white at its crest as it breaks, due to air being mixed into the water. When waves break in this way, there is a transfer of energy from the waves to the ocean. Such a flux is defined to be negative. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into waves Dimensionless This parameter is the normalised vertical flux of energy from wind into the ocean waves. A positive flux implies a flux into the waves. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into waves Dimensionless This parameter is the normalised vertical flux of energy from wind into the ocean waves. A positive flux implies a flux into the waves. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized stress into ocean Dimensionless This parameter is the normalised surface stress, or momentum flux, from the air into the ocean due to turbulence at the air-sea interface and breaking waves. It does not include the flux used to generate waves. The ECMWF convention for vertical fluxes is positive downwards. The stress has units of Newtons per metre squared, and this is normalised by being divided by the product of air density and the square of the friction velocity. Normalized stress into ocean Dimensionless This parameter is the normalised surface stress, or momentum flux, from the air into the ocean due to turbulence at the air-sea interface and breaking waves. It does not include the flux used to generate waves. The ECMWF convention for vertical fluxes is positive downwards. The stress has units of Newtons per metre squared, and this is normalised by being divided by the product of air density and the square of the friction velocity. Northward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Northward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Northward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Northward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Ocean surface stress equivalent 10m neutral wind direction degrees This parameter is the direction from which the "neutral wind" blows, in degrees clockwise from true north, at a height of ten metres above the surface of the Earth. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind direction used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind direction degrees This parameter is the direction from which the "neutral wind" blows, in degrees clockwise from true north, at a height of ten metres above the surface of the Earth. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind direction used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind speed m s-1 This parameter is the horizontal speed of the "neutral wind", at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind speed used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind speed m s-1 This parameter is the horizontal speed of the "neutral wind", at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind speed used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Period corresponding to maximum individual wave height s This parameter is the period of the expected highest individual wave within a 20-minute time window. It can be used as a guide to the characteristics of extreme or freak waves. Wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. Occasionally waves of different periods reinforce and interact non-linearly giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered to be a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Period corresponding to maximum individual wave height s This parameter is the period of the expected highest individual wave within a 20-minute time window. It can be used as a guide to the characteristics of extreme or freak waves. Wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. Occasionally waves of different periods reinforce and interact non-linearly giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered to be a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Potential evaporation m This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Potential evaporation m This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Precipitation type Dimensionless This parameter describes the type of precipitation at the surface, at the specified time. A precipitation type is assigned wherever there is a non-zero value of precipitation. In the ECMWF Integrated Forecasting System (IFS) there are only two predicted precipitation variables: rain and snow. Precipitation type is derived from these two predicted variables in combination with atmospheric conditions, such as temperature. Values of precipitation type defined in the IFS: 0: No precipitation, 1: Rain, 3: Freezing rain (i.e. supercooled raindrops which freeze on contact with the ground and other surfaces), 5: Snow, 6: Wet snow (i.e. snow particles which are starting to melt), 7: Mixture of rain and snow, 8: Ice pellets. These precipitation types are consistent with WMO Code Table 4.201. Other types in this WMO table are not defined in the IFS. The monthly mean procedure applied to such integers, will yield non-integer values. Precipitation type Dimensionless This parameter describes the type of precipitation at the surface, at the specified time. A precipitation type is assigned wherever there is a non-zero value of precipitation. In the ECMWF Integrated Forecasting System (IFS) there are only two predicted precipitation variables: rain and snow. Precipitation type is derived from these two predicted variables in combination with atmospheric conditions, such as temperature. Values of precipitation type defined in the IFS: 0: No precipitation, 1: Rain, 3: Freezing rain (i.e. supercooled raindrops which freeze on contact with the ground and other surfaces), 5: Snow, 6: Wet snow (i.e. snow particles which are starting to melt), 7: Mixture of rain and snow, 8: Ice pellets. These precipitation types are consistent with WMO Code Table 4.201. Other types in this WMO table are not defined in the IFS. The monthly mean procedure applied to such integers, will yield non-integer values. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Sea surface temperature K This parameter (SST) is the temperature of sea water near the surface. In ERA5, this parameter is a foundation SST, which means there are no variations due to the daily cycle of the sun (diurnal variations). SST, in ERA5, is given by two external providers. Before September 2007, SST from the HadISST2 dataset is used and from September 2007 onwards, the OSTIA dataset is used. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Sea surface temperature K This parameter (SST) is the temperature of sea water near the surface. In ERA5, this parameter is a foundation SST, which means there are no variations due to the daily cycle of the sun (diurnal variations). SST, in ERA5, is given by two external providers. Before September 2007, SST from the HadISST2 dataset is used and from September 2007 onwards, the OSTIA dataset is used. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Sea-ice cover Dimensionless This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land-sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover. In ERA5, sea-ice cover is given by two external providers. Before 1979 the HadISST2 dataset is used. From 1979 to August 2007 the OSI SAF (409a) dataset is used and from September 2007 the OSI SAF oper dataset is used. Sea ice is frozen sea water which floats on the surface of the ocean. Sea ice does not include ice which forms on land such as glaciers, icebergs and ice-sheets. It also excludes ice shelves which are anchored on land, but protrude out over the surface of the ocean. These phenomena are not modelled by the IFS. Long-term monitoring of sea ice is important for understanding climate change. Sea ice also affects shipping routes through the polar regions. Sea-ice cover Dimensionless This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land-sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover. In ERA5, sea-ice cover is given by two external providers. Before 1979 the HadISST2 dataset is used. From 1979 to August 2007 the OSI SAF (409a) dataset is used and from September 2007 the OSI SAF oper dataset is used. Sea ice is frozen sea water which floats on the surface of the ocean. Sea ice does not include ice which forms on land such as glaciers, icebergs and ice-sheets. It also excludes ice shelves which are anchored on land, but protrude out over the surface of the ocean. These phenomena are not modelled by the IFS. Long-term monitoring of sea ice is important for understanding climate change. Sea ice also affects shipping routes through the polar regions. Significant height of combined wind waves and swell m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. This parameter can be used to assess sea state and swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of combined wind waves and swell m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. This parameter can be used to assess sea state and swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of total swell m This parameter represents the average height of the highest third of surface ocean/sea waves associated with swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of total swell only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional total swell spectrum. The total swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of total swell m This parameter represents the average height of the highest third of surface ocean/sea waves associated with swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of total swell only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional total swell spectrum. The total swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of wind waves m This parameter represents the average height of the highest third of surface ocean/sea waves generated by the local wind. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wind-sea wave spectrum. The wind-sea wave spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are still under the influence of the local wind. This parameter can be used to assess wind-sea waves. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of wind waves m This parameter represents the average height of the highest third of surface ocean/sea waves generated by the local wind. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wind-sea wave spectrum. The wind-sea wave spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are still under the influence of the local wind. This parameter can be used to assess wind-sea waves. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of first swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the first swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of first swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the first swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of second swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the second swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of second swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the second swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of third swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the third swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of third swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the third swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Skin reservoir content m of water equivalent This parameter is the amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of "skin reservoir content" a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the "skin reservoir" by evaporation. Skin reservoir content m of water equivalent This parameter is the amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of "skin reservoir content" a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the "skin reservoir" by evaporation. Skin temperature K This parameter is the temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Skin temperature K This parameter is the temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Slope of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, angle and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the slope of the sub-grid valleys, hills and mountains. A flat surface has a value of 0, and a 45 degree slope has a value of 0.5. This parameter does not vary in time. Slope of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, angle and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the slope of the sub-grid valleys, hills and mountains. A flat surface has a value of 0, and a 45 degree slope has a value of 0.5. This parameter does not vary in time. Snow albedo Dimensionless This parameter is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter changes with snow age and also depends on vegetation height. It has a range of values between 0 and 1. For low vegetation, it ranges between 0.52 for old snow and 0.88 for fresh snow. For high vegetation with snow underneath, it depends on vegetation type and has values between 0.27 and 0.38. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean snow albedo, grid point value would be contaminated with fictitious zero snow values. Snow albedo Dimensionless This parameter is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter changes with snow age and also depends on vegetation height. It has a range of values between 0 and 1. For low vegetation, it ranges between 0.52 for old snow and 0.88 for fresh snow. For high vegetation with snow underneath, it depends on vegetation type and has values between 0.27 and 0.38. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean snow albedo, grid point value would be contaminated with fictitious zero snow values. Snow density kg m-3 This parameter is the mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean snow density, grid point value would be contaminated with fictitious zero snow values. Snow density kg m-3 This parameter is the mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean snow density, grid point value would be contaminated with fictitious zero snow values. Snow depth m of water equivalent This parameter is the amount of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow depth m of water equivalent This parameter is the amount of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from snow from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the evaporated snow (from the snow-covered area of a grid box) were liquid and were spread evenly over the whole grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Snow evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from snow from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the evaporated snow (from the snow-covered area of a grid box) were liquid and were spread evenly over the whole grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Snowmelt m of water equivalent This parameter is the accumulated amount of water that has melted from snow in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the melted snow (from the snow-covered area of a grid box) were spread evenly over the whole grid box. For example, if half the grid box were covered in snow with a water equivalent depth of 0.02m, this parameter would have a value of 0.01m. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Snowmelt m of water equivalent This parameter is the accumulated amount of water that has melted from snow in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the melted snow (from the snow-covered area of a grid box) were spread evenly over the whole grid box. For example, if half the grid box were covered in snow with a water equivalent depth of 0.02m, this parameter would have a value of 0.01m. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Soil temperature level 1 K This parameter is the temperature of the soil at level 1 (in the middle of layer 1). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 1 K This parameter is the temperature of the soil at level 1 (in the middle of layer 1). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K This parameter is the temperature of the soil at level 2 (in the middle of layer 2). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K This parameter is the temperature of the soil at level 2 (in the middle of layer 2). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 3 K This parameter is the temperature of the soil at level 3 (in the middle of layer 3). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 3 K This parameter is the temperature of the soil at level 3 (in the middle of layer 3). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 4 K This parameter is the temperature of the soil at level 4 (in the middle of layer 4). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 4 K This parameter is the temperature of the soil at level 4 (in the middle of layer 4). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil type Dimensionless This parameter is the texture (or classification) of soil used by the land surface scheme of the ECMWF Integrated Forecasting System (IFS) to predict the water holding capacity of soil in soil moisture and runoff calculations. It is derived from the root zone data (30-100 cm below the surface) of the FAO/UNESCO Digital Soil Map of the World, DSMW (FAO, 2003), which exists at a resolution of 5' X 5' (about 10 km). The seven soil types are: 1: Coarse, 2: Medium, 3: Medium fine, 4: Fine, 5: Very fine, 6: Organic, 7: Tropical organic. A value of 0 indicates a non-land point. This parameter does not vary in time. Soil type Dimensionless This parameter is the texture (or classification) of soil used by the land surface scheme of the ECMWF Integrated Forecasting System (IFS) to predict the water holding capacity of soil in soil moisture and runoff calculations. It is derived from the root zone data (30-100 cm below the surface) of the FAO/UNESCO Digital Soil Map of the World, DSMW (FAO, 2003), which exists at a resolution of 5' X 5' (about 10 km). The seven soil types are: 1: Coarse, 2: Medium, 3: Medium fine, 4: Fine, 5: Very fine, 6: Organic, 7: Tropical organic. A value of 0 indicates a non-land point. This parameter does not vary in time. Standard deviation of filtered subgrid orography m Climatological parameter (scales between approximately 3 and 22 km are included). This parameter does not vary in time. Standard deviation of filtered subgrid orography m Climatological parameter (scales between approximately 3 and 22 km are included). This parameter does not vary in time. Standard deviation of orography Dimensionless This parameter is one of four parameters (the others being angle of sub-gridscale orography, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the standard deviation of the height of the sub-grid valleys, hills and mountains within a grid box. This parameter does not vary in time. Standard deviation of orography Dimensionless This parameter is one of four parameters (the others being angle of sub-gridscale orography, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the standard deviation of the height of the sub-grid valleys, hills and mountains within a grid box. This parameter does not vary in time. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface latent heat flux J m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface latent heat flux J m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation, clear sky J m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation, clear sky J m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation, clear sky J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation, clear sky J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of land, sea and inland water. It is a measure of the weight of all the air in a column vertically above a point on the Earth's surface. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure weather systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa). Surface pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of land, sea and inland water. It is a measure of the weight of all the air in a column vertically above a point on the Earth's surface. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure weather systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa). Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface sensible heat flux J m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface sensible heat flux J m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downward, clear sky J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downward, clear sky J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downward, clear sky J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downward, clear sky J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. TOA incident solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. TOA incident solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Temperature of snow layer K This parameter gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean temperature of snow layer, grid point value would be contaminated with fictitious zero snow values. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Temperature of snow layer K This parameter gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean temperature of snow layer, grid point value would be contaminated with fictitious zero snow values. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Top net solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation, clear sky J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation, clear sky J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation J m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation J m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation, clear sky J m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). Note that OLR is typically shown in units of watts per square metre (W m-2 ). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. Top net thermal radiation, clear sky J m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). Note that OLR is typically shown in units of watts per square metre (W m-2 ). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. Total cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights. Cloud fractions vary from 0 to 1. Total cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights. Cloud fractions vary from 0 to 1. Total column cloud ice water kg m-2 This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud ice water kg m-2 This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud liquid water kg m-2 This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud liquid water kg m-2 This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can also be referred to as total ozone, or vertically integrated ozone. The values are dominated by ozone within the stratosphere. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. In the IFS, the units for total ozone are kilograms per square metre, but before 12/06/2001 dobson units were used. Dobson units (DU) are still used extensively for total column ozone. 1 DU = 2.1415E-5 kg m-2 Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can also be referred to as total ozone, or vertically integrated ozone. The values are dominated by ozone within the stratosphere. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. In the IFS, the units for total ozone are kilograms per square metre, but before 12/06/2001 dobson units were used. Dobson units (DU) are still used extensively for total column ozone. 1 DU = 2.1415E-5 kg m-2 Total column rain water kg m-2 This parameter is the total amount of water in droplets of raindrop size (which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column rain water kg m-2 This parameter is the total amount of water in droplets of raindrop size (which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column snow water kg m-2 This parameter is the total amount of water in the form of snow (aggregated ice crystals which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column snow water kg m-2 This parameter is the total amount of water in the form of snow (aggregated ice crystals which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column supercooled liquid water kg m-2 This parameter is the total amount of supercooled water in a column extending from the surface of the Earth to the top of the atmosphere. Supercooled water is water that exists in liquid form below 0oC. It is common in cold clouds and is important in the formation of precipitation. Also, supercooled water in clouds extending to the surface (i.e., fog) can cause icing/riming of various structures. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column supercooled liquid water kg m-2 This parameter is the total amount of supercooled water in a column extending from the surface of the Earth to the top of the atmosphere. Supercooled water is water that exists in liquid form below 0oC. It is common in cold clouds and is important in the formation of precipitation. Also, supercooled water in clouds extending to the surface (i.e., fog) can cause icing/riming of various structures. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column water kg m-2 This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for. Total column water kg m-2 This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for. Total column water vapour kg m-2 This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Total column water vapour kg m-2 This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Total precipitation m This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Total precipitation m This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Total sky direct solar radiation at surface J m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Total sky direct solar radiation at surface J m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Total totals index K This parameter gives an indication of the probability of occurrence of a thunderstorm and its severity by using the vertical gradient of temperature and humidity. The values of this index indicate the following: <44 K: Thunderstorms not likely, 44-50 K: Thunderstorms likely, 51-52 K: Isolated severe thunderstorms, 53-56 K: Widely scattered severe thunderstorms, 56-60 K: Scattered severe thunderstorms more likely. The total totals index is the temperature difference between 850 hPa (near surface) and 500 hPa (mid-troposphere) (lapse rate) plus a measure of the moisture content between 850 hPa and 500 hPa. The probability of deep convection tends to increase with increasing lapse rate and atmospheric moisture content. There are a number of limitations to this index. Also, the interpretation of the index value varies with season and location. Total totals index K This parameter gives an indication of the probability of occurrence of a thunderstorm and its severity by using the vertical gradient of temperature and humidity. The values of this index indicate the following: <44 K: Thunderstorms not likely, 44-50 K: Thunderstorms likely, 51-52 K: Isolated severe thunderstorms, 53-56 K: Widely scattered severe thunderstorms, 56-60 K: Scattered severe thunderstorms more likely. The total totals index is the temperature difference between 850 hPa (near surface) and 500 hPa (mid-troposphere) (lapse rate) plus a measure of the moisture content between 850 hPa and 500 hPa. The probability of deep convection tends to increase with increasing lapse rate and atmospheric moisture content. There are a number of limitations to this index. Also, the interpretation of the index value varies with season and location. Trapping layer base height m Trapping layer base height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer base height m Trapping layer base height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer top height m Trapping layer top height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer top height m Trapping layer top height as diagnosed from the vertical gradient of atmospheric refractivity. Type of high vegetation Dimensionless This parameter indicates the 6 types of high vegetation recognised by the ECMWF Integrated Forecasting System: 3 = Evergreen needleleaf trees, 4 = Deciduous needleleaf trees, 5 = Deciduous broadleaf trees, 6 = Evergreen broadleaf trees, 18 = Mixed forest/woodland, 19 = Interrupted forest. A value of 0 indicates a point without high vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of high vegetation Dimensionless This parameter indicates the 6 types of high vegetation recognised by the ECMWF Integrated Forecasting System: 3 = Evergreen needleleaf trees, 4 = Deciduous needleleaf trees, 5 = Deciduous broadleaf trees, 6 = Evergreen broadleaf trees, 18 = Mixed forest/woodland, 19 = Interrupted forest. A value of 0 indicates a point without high vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of low vegetation Dimensionless This parameter indicates the 10 types of low vegetation recognised by the ECMWF Integrated Forecasting System: 1 = Crops, Mixed farming, 2 = Grass, 7 = Tall grass, 9 = Tundra, 10 = Irrigated crops, 11 = Semidesert, 13 = Bogs and marshes, 16 = Evergreen shrubs, 17 = Deciduous shrubs, 20 = Water and land mixtures. A value of 0 indicates a point without low vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of low vegetation Dimensionless This parameter indicates the 10 types of low vegetation recognised by the ECMWF Integrated Forecasting System: 1 = Crops, Mixed farming, 2 = Grass, 7 = Tall grass, 9 = Tundra, 10 = Irrigated crops, 11 = Semidesert, 13 = Bogs and marshes, 16 = Evergreen shrubs, 17 = Deciduous shrubs, 20 = Water and land mixtures. A value of 0 indicates a point without low vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. U-component stokes drift m s-1 This parameter is the eastward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. U-component stokes drift m s-1 This parameter is the eastward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. UV visible albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. This parameter varies between 0 and 1. UV visible albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. This parameter varies between 0 and 1. UV visible albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. UV visible albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. V-component stokes drift m s-1 This parameter is the northward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. V-component stokes drift m s-1 This parameter is the northward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. Vertical integral of divergence of cloud frozen water flux kg m-2 s-1 The vertical integral of the cloud frozen water flux is the horizontal rate of flow of cloud frozen water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud frozen water spreading outward from a point, per square metre. This parameter is positive for cloud frozen water that is spreading out, or diverging, and negative for the opposite, for cloud frozen water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud frozen water. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of divergence of cloud frozen water flux kg m-2 s-1 The vertical integral of the cloud frozen water flux is the horizontal rate of flow of cloud frozen water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud frozen water spreading outward from a point, per square metre. This parameter is positive for cloud frozen water that is spreading out, or diverging, and negative for the opposite, for cloud frozen water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud frozen water. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of divergence of cloud liquid water flux kg m-2 s-1 The vertical integral of the cloud liquid water flux is the horizontal rate of flow of cloud liquid water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud liquid water spreading outward from a point, per square metre. This parameter is positive for cloud liquid water that is spreading out, or diverging, and negative for the opposite, for cloud liquid water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud liquid water. Vertical integral of divergence of cloud liquid water flux kg m-2 s-1 The vertical integral of the cloud liquid water flux is the horizontal rate of flow of cloud liquid water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud liquid water spreading outward from a point, per square metre. This parameter is positive for cloud liquid water that is spreading out, or diverging, and negative for the opposite, for cloud liquid water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud liquid water. Vertical integral of divergence of geopotential flux W m-2 The vertical integral of the geopotential flux is the horizontal rate of flow of geopotential, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of geopotential spreading outward from a point, per square metre. This parameter is positive for geopotential that is spreading out, or diverging, and negative for the opposite, for geopotential that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of geopotential. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of geopotential flux W m-2 The vertical integral of the geopotential flux is the horizontal rate of flow of geopotential, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of geopotential spreading outward from a point, per square metre. This parameter is positive for geopotential that is spreading out, or diverging, and negative for the opposite, for geopotential that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of geopotential. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of kinetic energy flux W m-2 The vertical integral of the kinetic energy flux is the horizontal rate of flow of kinetic energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of kinetic energy spreading outward from a point, per square metre. This parameter is positive for kinetic energy that is spreading out, or diverging, and negative for the opposite, for kinetic energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of kinetic energy. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of kinetic energy flux W m-2 The vertical integral of the kinetic energy flux is the horizontal rate of flow of kinetic energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of kinetic energy spreading outward from a point, per square metre. This parameter is positive for kinetic energy that is spreading out, or diverging, and negative for the opposite, for kinetic energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of kinetic energy. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of mass flux kg m-2 s-1 The vertical integral of the mass flux is the horizontal rate of flow of mass, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of mass spreading outward from a point, per square metre. This parameter is positive for mass that is spreading out, or diverging, and negative for the opposite, for mass that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of mass. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of divergence of mass flux kg m-2 s-1 The vertical integral of the mass flux is the horizontal rate of flow of mass, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of mass spreading outward from a point, per square metre. This parameter is positive for mass that is spreading out, or diverging, and negative for the opposite, for mass that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of mass. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of divergence of moisture flux kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Vertical integral of divergence of moisture flux kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Vertical integral of divergence of ozone flux kg m-2 s-1 The vertical integral of the ozone flux is the horizontal rate of flow of ozone, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of ozone spreading outward from a point, per square metre. This parameter is positive for ozone that is spreading out, or diverging, and negative for the opposite, for ozone that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of ozone. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of divergence of ozone flux kg m-2 s-1 The vertical integral of the ozone flux is the horizontal rate of flow of ozone, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of ozone spreading outward from a point, per square metre. This parameter is positive for ozone that is spreading out, or diverging, and negative for the opposite, for ozone that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of ozone. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of divergence of thermal energy flux W m-2 The vertical integral of the thermal energy flux is the horizontal rate of flow of thermal energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of thermal energy spreading outward from a point, per square metre. This parameter is positive for thermal energy that is spreading out, or diverging, and negative for the opposite, for thermal energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of thermal energy. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the flow of thermal energy through the climate system and to investigate the atmospheric energy budget. Vertical integral of divergence of thermal energy flux W m-2 The vertical integral of the thermal energy flux is the horizontal rate of flow of thermal energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of thermal energy spreading outward from a point, per square metre. This parameter is positive for thermal energy that is spreading out, or diverging, and negative for the opposite, for thermal energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of thermal energy. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the flow of thermal energy through the climate system and to investigate the atmospheric energy budget. Vertical integral of divergence of total energy flux W m-2 The vertical integral of the total energy flux is the horizontal rate of flow of total energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of total energy spreading outward from a point, per square metre. This parameter is positive for total energy that is spreading out, or diverging, and negative for the opposite, for total energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of total energy. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of total energy flux W m-2 The vertical integral of the total energy flux is the horizontal rate of flow of total energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of total energy spreading outward from a point, per square metre. This parameter is positive for total energy that is spreading out, or diverging, and negative for the opposite, for total energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of total energy. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of eastward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of eastward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the eastward direction, per meter across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the eastward direction, per meter across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of eastward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of eastward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from west to east. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of eastward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from west to east. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of eastward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of energy conversion W m-2 This parameter is one contribution to the amount of energy being converted between kinetic energy, and internal plus potential energy, for a column of air extending from the surface of the Earth to the top of the atmosphere. Negative values indicate a conversion to kinetic energy from potential plus internal energy. This parameter can be used to study the atmospheric energy budget. The circulation of the atmosphere can also be considered in terms of energy conversions. Vertical integral of energy conversion W m-2 This parameter is one contribution to the amount of energy being converted between kinetic energy, and internal plus potential energy, for a column of air extending from the surface of the Earth to the top of the atmosphere. Negative values indicate a conversion to kinetic energy from potential plus internal energy. This parameter can be used to study the atmospheric energy budget. The circulation of the atmosphere can also be considered in terms of energy conversions. Vertical integral of kinetic energy J m-2 This parameter is the vertical integral of kinetic energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of kinetic energy J m-2 This parameter is the vertical integral of kinetic energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of mass of atmosphere kg m-2 This parameter is the total mass of air for a column extending from the surface of the Earth to the top of the atmosphere, per square metre. This parameter is calculated by dividing surface pressure by the Earth's gravitational acceleration, g (=9.80665 m s-2 ), and has units of kilograms per square metre. This parameter can be used to study the atmospheric mass budget. Vertical integral of mass of atmosphere kg m-2 This parameter is the total mass of air for a column extending from the surface of the Earth to the top of the atmosphere, per square metre. This parameter is calculated by dividing surface pressure by the Earth's gravitational acceleration, g (=9.80665 m s-2 ), and has units of kilograms per square metre. This parameter can be used to study the atmospheric mass budget. Vertical integral of mass tendency kg m-2 s-1 This parameter is the rate of change of the mass of a column of air extending from the Earth's surface to the top of the atmosphere. An increasing mass of the column indicates rising surface pressure. In contrast, a decrease indicates a falling surface pressure. The mass of the column is calculated by dividing pressure at the Earth's surface by the gravitational acceleration, g (=9.80665 m s-2 ). This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of mass tendency kg m-2 s-1 This parameter is the rate of change of the mass of a column of air extending from the Earth's surface to the top of the atmosphere. An increasing mass of the column indicates rising surface pressure. In contrast, a decrease indicates a falling surface pressure. The mass of the column is calculated by dividing pressure at the Earth's surface by the gravitational acceleration, g (=9.80665 m s-2 ). This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of northward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of northward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from south to north. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of northward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from south to north. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of northward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of potential and internal energy J m-2 This parameter is the mass weighted vertical integral of potential and internal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential and internal energy J m-2 This parameter is the mass weighted vertical integral of potential and internal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential, internal and latent energy J m-2 This parameter is the mass weighted vertical integral of potential, internal and latent energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The latent energy refers to the energy associated with the water vapour in the atmosphere and is equal to the energy required to convert liquid water into water vapour. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential, internal and latent energy J m-2 This parameter is the mass weighted vertical integral of potential, internal and latent energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The latent energy refers to the energy associated with the water vapour in the atmosphere and is equal to the energy required to convert liquid water into water vapour. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of temperature K kg m-2 This parameter is the mass-weighted vertical integral of temperature for a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can be used to study the atmospheric energy budget. Vertical integral of temperature K kg m-2 This parameter is the mass-weighted vertical integral of temperature for a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can be used to study the atmospheric energy budget. Vertical integral of thermal energy J m-2 This parameter is the mass-weighted vertical integral of thermal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Thermal energy is calculated from the product of temperature and the specific heat capacity of air at constant pressure. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of thermal energy J m-2 This parameter is the mass-weighted vertical integral of thermal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Thermal energy is calculated from the product of temperature and the specific heat capacity of air at constant pressure. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of total energy J m-2 This parameter is the vertical integral of total energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of total energy J m-2 This parameter is the vertical integral of total energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertically integrated moisture divergence kg m-2 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm. Vertically integrated moisture divergence kg m-2 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm. Volumetric soil water layer 1 m3 m-3 This parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 m3 m-3 This parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 This parameter is the volume of water in soil layer 2 (7 - 28cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 This parameter is the volume of water in soil layer 2 (7 - 28cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 3 m3 m-3 This parameter is the volume of water in soil layer 3 (28 - 100cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 3 m3 m-3 This parameter is the volume of water in soil layer 3 (28 - 100cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 4 m3 m-3 This parameter is the volume of water in soil layer 4 (100 - 289cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 4 m3 m-3 This parameter is the volume of water in soil layer 4 (100 - 289cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Wave spectral directional width Dimensionless This parameter indicates whether waves (generated by local winds and associated with swell) are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width Dimensionless This parameter indicates whether waves (generated by local winds and associated with swell) are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for swell Dimensionless This parameter indicates whether waves associated with swell are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for swell Dimensionless This parameter indicates whether waves associated with swell are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for wind waves Dimensionless This parameter indicates whether waves generated by the local wind are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for wind waves Dimensionless This parameter indicates whether waves generated by the local wind are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral kurtosis Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It shows how much of the probability density function of the sea surface elevation exists in the tails of the distribution. So, a positive kurtosis (typical range 0.0 to 0.06) means more frequent occurrences of very extreme values (either above or below the mean), relative to a normal distribution. Wave spectral kurtosis Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It shows how much of the probability density function of the sea surface elevation exists in the tails of the distribution. So, a positive kurtosis (typical range 0.0 to 0.06) means more frequent occurrences of very extreme values (either above or below the mean), relative to a normal distribution. Wave spectral peakedness Dimensionless This parameter is a statistical measure used to forecast extreme or freak waves. It is a measure of the relative width of the ocean/sea wave frequency spectrum (i.e., whether the ocean/sea wave field is made up of a narrow or broad range of frequencies). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). When the wave field is more focussed around a narrow range of frequencies, the probability of freak/extreme waves increases. This parameter is Goda's peakedness factor and is used to calculate the Benjamin-Feir Index (BFI). The BFI is in turn used to estimate the probability and nature of extreme/freak waves. Wave spectral peakedness Dimensionless This parameter is a statistical measure used to forecast extreme or freak waves. It is a measure of the relative width of the ocean/sea wave frequency spectrum (i.e., whether the ocean/sea wave field is made up of a narrow or broad range of frequencies). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). When the wave field is more focussed around a narrow range of frequencies, the probability of freak/extreme waves increases. This parameter is Goda's peakedness factor and is used to calculate the Benjamin-Feir Index (BFI). The BFI is in turn used to estimate the probability and nature of extreme/freak waves. Wave spectral skewness Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It is a measure of the asymmetry of the probability density function of the sea surface elevation. So, a positive/negative skewness (typical range -0.2 to 0.12) means more frequent occurrences of extreme values above/below the mean, relative to a normal distribution. Wave spectral skewness Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It is a measure of the asymmetry of the probability density function of the sea surface elevation. So, a positive/negative skewness (typical range -0.2 to 0.12) means more frequent occurrences of extreme values above/below the mean, relative to a normal distribution. Zero degree level m The height above the Earth's surface where the temperature passes from positive to negative values, corresponding to the top of a warm layer, at the specified time. This parameter can be used to help forecast snow. If more than one warm layer is encountered, then the zero degree level corresponds to the top of the second atmospheric layer. This parameter is set to zero when the temperature in the whole atmosphere is below 0℃. Zero degree level m The height above the Earth's surface where the temperature passes from positive to negative values, corresponding to the top of a warm layer, at the specified time. This parameter can be used to help forecast snow. If more than one warm layer is encountered, then the zero degree level corresponds to the top of the second atmospheric layer. This parameter is set to zero when the temperature in the whole atmosphere is below 0℃. 388 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-west-atlantic-shelf-mean-sea-level-time-series-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=NORTHWESTSHELF_OMI_SL_area_averaged_anomalies North West Atlantic Shelf Mean Sea Level time series and trend from Observations Reprocessing DEFINITION The ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version) altimeter gridded maps of sea level anomalies based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The mean sea level evolution estimated in the North-West Shelf region is derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed (least scare fit of sinusoidal function) and the time series is low-pass filtered (175 days cut-off). The curve is corrected for the regional mean effect of the Glacial Isostatic Adjustment using the ICE5G-VM2 GIA model (Peltier, 2004). During 1993-1998, the Global men sea level (hereafter GMSL) has been known to be affected by a TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018; Legeais et al., 2020). This drift led to overestimate the trend of the GMSL during the first 6 years of the altimetry record (about 0.04 mm/y at global scale over the whole altimeter period). A correction of the drift is proposed for the Global mean sea level (Legeais et al., 2020). Whereas this TOPEX-A instrumental drift should also affect the regional mean sea level (hereafter RMSL) trend estimation, this empirical correction is currently not applied to the altimeter sea level dataset and resulting estimated for RMSL. Indeed, the pertinence of the global correction applied at regional scale has not been demonstrated yet and there is no clear consensus achieved on the way to proceed at regional scale. Additionally, the estimate of such a correction at regional scale is not obvious, especially in areas where few accurate independent measurements (e.g. in situ)- necessary for this estimation - are available. The trend uncertainty is provided in a 90% confidence interval (Prandi et al., 2021). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not taken into account. CONTEXT The indicator on area averaged sea level is a crucial index of climate change, and individual components contribute to sea level rise, including expansion due to ocean warming and melting of glaciers and ice sheets (WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report, global mean sea level (GMSL) increased by 0.20 (0.15 to 0.25) m over the period 1901 to 2018 with a rate 25 of rise that has accelerated since the 1960s to 3.7 (3.2 to 4.2) mm yr-1 for the period 2006–2018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). At regional scale, sea level does not change homogenously, and RMSL rise can also be influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). Rising sea level can strongly affect population and infrastructures in coastal areas, increase their vulnerability and risks for food security, particularly in low lying areas and island states. Adverse impacts from floods, storms and tropical cyclones with related losses and damages have increased due to sea level rise, and increase their vulnerability and increase risks for food security, particularly in low lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). In this region, the RMSL trend is modulated decadal variations. As observed over the global ocean, the main actors of the long-term RMSL trend are associated with anthropogenic global/regional warming. Decadal variability is mainly linked to the Strengthening or weakening of the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). The latest is driven by the North Atlantic Oscillation (NAO) (e.g. Delworth and Zeng, 2016). Along the European coast, the NAO also influences the along-slope winds dynamic which in return significantly contributes to the local sea level variability observed (Chafik et al., 2019). CMEMS KEY FINDINGS Over the [1993/01/01, 2021/08/02] period, the basin-wide RMSL in the NWS area rises at a rate of 3.0  0.83 mm/year. DOI (product):https://doi.org/10.48670/moi-00271 https://doi.org/10.48670/moi-00271 389 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-meridional-overturning-circulation-index http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_CIRCULATION_MOC_MEDSEA_area_averaged_mean Mediterranean Meridional Overturning Circulation Index from Reanalysis DEFINITION Time mean meridional Eulerian streamfunctions are computed using the velocity field estimate provided by the Copernicus Marine Mediterranean Sea reanalysis over the last 34 years (1987–2020). The Eulerian meridional streamfunction is evaluated by integrating meridional velocity daily data first in a vertical direction, then in a meridional direction, and finally averaging over the reanalysis period (Pinardi et al. 2019). The Mediterranean overturning indices are derived for the eastern and western Mediterranean Sea by computing the annual streamfunction in the two areas separated by the Strait of Sicily around 36.5°N, and then considering the associated maxima. In each case a geographical constraint focused the computation on the main region of interest. For the western index, we focused on deep-water formation regions, thus excluding both the effect of shallow physical processes and the Gibraltar net inflow. For the eastern index, we investigate the Levantine and Cretan areas corresponding to the strongest meridional overturning cell locations, thus only a zonal constraint is defined. Time series of annual mean values is provided for the Mediterranean Sea using the Mediterranean 1/24o eddy resolving reanalysis (Escudier et al., 2020, 2021). More details can be found in the Copernicus Marine Ocean State Report issue 4 (OSR4, von Schuckmann et al., 2020) Section 2.4 (Lyubartsev et al., 2020). CONTEXT The western and eastern Mediterranean clockwise meridional overturning circulation is connected to deep-water formation processes. The Mediterranean Sea 1/24o eddy resolving reanalysis (Escudier et al., 2020, 2021) is used to show the interannual variability of the Meridional Overturning Index. Details on the product are delivered in the PUM and QUID of this OMI. The Mediterranean Meridional Overturning Index is defined here as the maxima of the clockwise cells in the eastern and western Mediterranean Sea and is associated with deep and intermediate water mass formation processes that occur in specific areas of the basin: Gulf of Lion, Southern Adriatic Sea, Cretan Sea and Rhodes Gyre (Pinardi et al., 2015). As in the global ocean, the overturning circulation of the western and eastern Mediterranean are paramount to determine the stratification of the basins (Cessi, 2019). In turn, the stratification and deep water formation mediate the exchange of oxygen and other tracers between the surface and the deep ocean (e.g., Johnson et al., 2009; Yoon et al., 2018). In this sense, the overturning indices are potential gauges of the ecosystem health of the Mediterranean Sea, and in particular they could instruct early warning indices for the Mediterranean Sea to support the Sustainable Development Goal (SDG) 13 Target 13.3. CMEMS KEY FINDINGS The western and eastern Mediterranean overturning indices (WMOI and EMOI) are synthetic indices of changes in the thermohaline properties of the Mediterranean basin related to changes in the main drivers of the basin scale circulation. The western sub-basin clockwise overturning circulation is associated with the deep-water formation area of the Gulf of Lion, while the eastern clockwise meridional overturning circulation is composed of multiple cells associated with different intermediate and deep-water sources in the Levantine, Aegean, and Adriatic Seas. On average, the EMOI shows higher values than the WMOI indicating a more vigorous overturning circulation in eastern Mediterranean. The difference is mostly related to the occurrence of the eastern Mediterranean transient (EMT) climatic event, and linked to a peak of the EMOI in 1992. In 1999, the difference between the two indices started to decrease because EMT water masses reached the Sicily Strait flowing into the western Mediterranean Sea (Schroeder et al., 2016). The western peak in 2006 is discussed to be linked to anomalous deep-water formation during the Western Mediterranean Transition (Smith, 2008; Schroeder et al., 2016). Thus, the WMOI and EMOI indices are a useful tool for long-term climate monitoring of overturning changes in the Mediterranean Sea. DOI (product):https://doi.org/10.48670/mds-00317 https://doi.org/10.48670/mds-00317 390 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-ocean-colour-plankton-reflectance-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_BLK_BGC_L3_MY_009_153 Black Sea Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations Short description: For the Black Sea Ocean Satellite Observations, the Italian National Research Council (CNR – Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets: * ''plankton'' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm * ''reflectance'' with the spectral Remote Sensing Reflectance (RRS) * ''transparency'' with the diffuse attenuation coefficient of light at 490 nm (KD490) * ''optics'' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates) Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A for the ""multi"" products, and OLCI-S3A & S3B for the ""olci"" products Temporal resolution: daily Spatial resolution: 1 km for ""multi"" and 300 meters for ""olci"" To find this product in the catalogue, use the search keyword ""OCEANCOLOUR_BLK_BGC_L3_MY"". DOI (product) :https://doi.org/10.48670/moi-00303 https://doi.org/10.48670/moi-00303 391 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-sea-ice-concentration https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-ice-concentration satellite-sea-ice-concentration This dataset provides daily gridded data of sea ice concentration for both hemispheres derived from satellite passive microwave brightness temperatures. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its presence or its retreat has a strong impact on air-sea interactions, the Earth’s energy budget as well as marine ecosystems. It is recognised by the Global Climate Observing System as an Essential Climate Variable. Sea ice concentration is defined as the fraction of a pixel or grid cell in a satellite image or other gridded product that is covered with sea ice. It is one of the parameters commonly used to characterise sea ice. Other sea ice parameters include sea ice thickness, sea ice edge, and sea ice type, also available in the Climate Data Store. The dataset consists of two products: The Global Sea Ice Concentration Climate Data Record produced by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF). This is a coarse-resolution product based on measurements from the following sensors: Scanning Multichannel Microwave Radiometer (SMMR; 1979–1987), Special Sensor Microwave/Imager (SSM/I; 1987–2006), and Special Sensor Microwave Imager/Sounder (SSMIS; 2005 onward). This product spans the period from 1979 to present and is updated daily. In the following, it is referred to as the SSMIS product. The Global Sea Ice Concentration Climate Data Record produced by the European Space Agency Climate Change Initiative Phase 2 project (ESA CCI). This is a medium-resolution product based on measurements from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensor (2002–2011) and its successor, AMSR2 (2012–2017). This product spans the 2002–2017 period and is not updated. In the following, it is referred to as the AMSR product. The Global Sea Ice Concentration Climate Data Record produced by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF). This is a coarse-resolution product based on measurements from the following sensors: Scanning Multichannel Microwave Radiometer (SMMR; 1979–1987), Special Sensor Microwave/Imager (SSM/I; 1987–2006), and Special Sensor Microwave Imager/Sounder (SSMIS; 2005 onward). This product spans the period from 1979 to present and is updated daily. In the following, it is referred to as the SSMIS product. The Global Sea Ice Concentration Climate Data Record produced by the European Space Agency Climate Change Initiative Phase 2 project (ESA CCI). This is a medium-resolution product based on measurements from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensor (2002–2011) and its successor, AMSR2 (2012–2017). This product spans the 2002–2017 period and is not updated. In the following, it is referred to as the AMSR product. Both products are provided on the same polar projection with a grid resolution of 25 km. However, the AMSR product has a true spatial resolution (as resolved by the sensor) of about 15–25 km versus 30–60 km for the SSMIS product. Therefore, the AMSR product provides a much more detailed view of the sea ice cover than the SSMIS product, especially along the marginal ice zone, the transitional zone between open water and the dense sea ice pack. On the other hand, the clear strength of the SSMIS product is its more than 40-year long and consistent record with daily updates. Although originating from different projects, the two products share the same algorithm baseline, which is both a continuation of the EUMETSAT OSI SAF approach and a series of innovations contributed mostly by ESA CCI activities. For both products, the underlying algorithm makes use of a combination of the same three temperature channels near 19 GHz and 37 GHz. The data also share a common data format so that interested users can revert some of the filtering steps and access the raw output of the SIC algorithms. Both are level-4 products in the sense that gaps are filled by temporal and spatial interpolation. However, gap filling is not applied to fill in days when no input satellite data are available. Further details about each product can be found below as well as in the Documentation section. 392 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-physics-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTICSEA_ANALYSISFORECAST_PHY_003_006 Baltic Sea Physics Analysis and Forecast Short description: This Baltic Sea physical model product provides forecasts for the physical conditions in the Baltic Sea. The Baltic forecast is updated twice daily providing a new six days forecast. Four datasets are provided: One with hourly instantaneous values, one with daily mean values and one with monthly mean values, all containing these parameters: sea level variations, ice concentration and thickness at the surface, and temperature, salinity and horizontal and vertical velocities for the 3D field. Additionally a dataset with 15 minutes (instantaneous) surface values are provided for the sea level variation and the surface horizontal currents. The product is produced by a Baltic Sea set up of the NEMOv4.0 ocean model. This product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and up to 56 vertical depth levels. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The ocean model is forced with Stokes drift data from the Baltic Wave forecast product (BALTICSEA_ANALYSISFORECAST_WAV_003_010). Satellite SST and in-situ T and S profiles are assimilated into the model's analysis field. DOI (product) :https://doi.org/10.48670/moi-00010 https://doi.org/10.48670/moi-00010 393 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/european-north-west-shelfiberia-biscay-irish-seas-high-2 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_ATL_PHY_L3S_MY_010_038 European North West Shelf/Iberia Biscay Irish Seas – High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations Short description: For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05° resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. DOI (product) :https://doi.org/10.48670/moi-00311 https://doi.org/10.48670/moi-00311 394 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-fire-emissions-gfas https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-fire-emissions-gfas cams-global-fire-emissions-gfas Emissions of atmospheric pollutants from biomass burning and vegetation fires are key drivers of the evolution of atmospheric composition, with a high degree of spatial and temporal variability, and an accurate representation of them in models is essential. The CAMS Global Fire Assimilation System (GFAS) utilises satellite observations of fire radiative power (FRP) to provide near-real-time information on the location, relative intensity and estimated emissions from biomass burning and vegetation fires. Emissions are estimated by (i) conversion of FRP observations to the dry matter (DM) consumed by the fire, and (ii) application of emission factors to DM for different biomes, based on field and laboratory studies in the scientific literature, to estimate the emissions. Emissions estimates for 40 pyrogenic species are available from GFAS, including aerosols, reactive gases and greenhouse gases, on a regular grid with a spatial resolution of 0.1 degrees longitude by 0.1 degrees latitude. This version of GFAS (v1.2) provides daily averaged data based on a combination of FRP observations from two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, one on the NASA EOS-Terra satellite and the other on the NASA EOS-Aqua satellite from 1 January 2003 to present. GFAS also provides daily estimates of smoke plume injection heights derived from FRP observations and meteorological information from the operational weather forecasts from ECMWF. GFAS data have been used to provide surface boundary conditions for the CAMS global atmospheric composition and European regional air quality forecasts, and the wider atmospheric chemistry modelling community. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 0.1°x0.1° Vertical coverage Surface Temporal coverage 2003 to present Temporal resolution Daily averages File format GRIB (optional conversion to netCDF) Update frequency Once per day DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.1°x0.1° Horizontal resolution 0.1°x0.1° Vertical coverage Surface Vertical coverage Surface Temporal coverage 2003 to present Temporal coverage 2003 to present Temporal resolution Daily averages Temporal resolution Daily averages File format GRIB (optional conversion to netCDF) File format GRIB (optional conversion to netCDF) Update frequency Once per day Update frequency Once per day MAIN VARIABLES Name Units Altitude of plume bottom m Altitude of plume top m Injection height (from IS4FIRES) m Mean altitude of maximum injection m Wildfire combustion rate kg m-2 s-1 Wildfire flux of acetaldehyde (C2H4O) kg m-2 s-1 Wildfire flux of acetone (C3H6O) kg m-2 s-1 Wildfire flux of ammonia (NH3) kg m-2 s-1 Wildfire flux of benzene (C6H6) kg m-2 s-1 Wildfire flux of black carbon kg m-2 s-1 Wildfire flux of butanes (C4H10) kg m-2 s-1 Wildfire flux of butenes (C4H8) kg m-2 s-1 Wildfire flux of carbon dioxide (CO2) kg m-2 s-1 Wildfire flux of carbon monoxide (CO) kg m-2 s-1 Wildfire flux of dimethyl sulfide (DMS) (C2H6S) kg m-2 s-1 Wildfire flux of ethane (C2H6) kg m-2 s-1 Wildfire flux of ethanol (C2H5OH) kg m-2 s-1 Wildfire flux of ethene (C2H4) kg m-2 s-1 Wildfire flux of formaldehyde (CH2O) kg m-2 s-1 Wildfire flux of heptane (C7H16) kg m-2 s-1 Wildfire flux of hexanes (C6H14) kg m-2 s-1 Wildfire flux of hexene (C6H12) kg m-2 s-1 Wildfire flux of higher alkanes (CnH2n+2, c>=4) kg m-2 s-1 Wildfire flux of higher alkenes (CnH2n, c>=4) kg m-2 s-1 Wildfire flux of hydrogen (H) kg m-2 s-1 Wildfire flux of isoprene (C5H8) kg m-2 s-1 Wildfire flux of methane (CH4) kg m-2 s-1 Wildfire flux of methanol (CH3OH) kg m-2 s-1 Wildfire flux of nitrogen oxides (NOx) kg m-2 s-1 Wildfire flux of nitrous oxide (N20) kg m-2 s-1 Wildfire flux of non-methane hydrocarbons kg m-2 s-1 Wildfire flux of octene (C8H16) kg m-2 s-1 Wildfire flux of organic carbon kg m-2 s-1 Wildfire flux of particulate matter d < 2.5 µm (PM2.5) kg m-2 s-1 Wildfire flux of pentanes (C5H12) kg m-2 s-1 Wildfire flux of pentenes (C5H10) kg m-2 s-1 Wildfire flux of propane (C3H8) kg m-2 s-1 Wildfire flux of propene (C3H6) kg m-2 s-1 Wildfire flux of sulphur dioxide (SO2) kg m-2 s-1 Wildfire flux of terpenes ((C5H8)n) kg m-2 s-1 Wildfire flux of toluene (C7H8) kg m-2 s-1 Wildfire flux of toluene_lump (C7H8+ C6H6 + C8H10) kg m-2 s-1 Wildfire flux of total carbon in aerosols kg m-2 s-1 Wildfire flux of total particulate matter kg m-2 s-1 Wildfire flux of xylene (C8H10) kg m-2 s-1 Wildfire fraction of area observed dimensionless Wildfire overall flux of burnt carbon kg m-2 s-1 Wildfire radiative power W m-2 MAIN VARIABLES MAIN VARIABLES Name Units Name Units Altitude of plume bottom m Altitude of plume bottom m Altitude of plume top m Altitude of plume top m Injection height (from IS4FIRES) m Injection height (from IS4FIRES) m Mean altitude of maximum injection m Mean altitude of maximum injection m Wildfire combustion rate kg m-2 s-1 Wildfire combustion rate kg m-2 s-1 Wildfire flux of acetaldehyde (C2H4O) kg m-2 s-1 Wildfire flux of acetaldehyde (C2H4O) kg m-2 s-1 Wildfire flux of acetone (C3H6O) kg m-2 s-1 Wildfire flux of acetone (C3H6O) kg m-2 s-1 Wildfire flux of ammonia (NH3) kg m-2 s-1 Wildfire flux of ammonia (NH3) kg m-2 s-1 Wildfire flux of benzene (C6H6) kg m-2 s-1 Wildfire flux of benzene (C6H6) kg m-2 s-1 Wildfire flux of black carbon kg m-2 s-1 Wildfire flux of black carbon kg m-2 s-1 Wildfire flux of butanes (C4H10) kg m-2 s-1 Wildfire flux of butanes (C4H10) kg m-2 s-1 Wildfire flux of butenes (C4H8) kg m-2 s-1 Wildfire flux of butenes (C4H8) kg m-2 s-1 Wildfire flux of carbon dioxide (CO2) kg m-2 s-1 Wildfire flux of carbon dioxide (CO2) kg m-2 s-1 Wildfire flux of carbon monoxide (CO) kg m-2 s-1 Wildfire flux of carbon monoxide (CO) kg m-2 s-1 Wildfire flux of dimethyl sulfide (DMS) (C2H6S) kg m-2 s-1 Wildfire flux of dimethyl sulfide (DMS) (C2H6S) kg m-2 s-1 Wildfire flux of ethane (C2H6) kg m-2 s-1 Wildfire flux of ethane (C2H6) kg m-2 s-1 Wildfire flux of ethanol (C2H5OH) kg m-2 s-1 Wildfire flux of ethanol (C2H5OH) kg m-2 s-1 Wildfire flux of ethene (C2H4) kg m-2 s-1 Wildfire flux of ethene (C2H4) kg m-2 s-1 Wildfire flux of formaldehyde (CH2O) kg m-2 s-1 Wildfire flux of formaldehyde (CH2O) kg m-2 s-1 Wildfire flux of heptane (C7H16) kg m-2 s-1 Wildfire flux of heptane (C7H16) kg m-2 s-1 Wildfire flux of hexanes (C6H14) kg m-2 s-1 Wildfire flux of hexanes (C6H14) kg m-2 s-1 Wildfire flux of hexene (C6H12) kg m-2 s-1 Wildfire flux of hexene (C6H12) kg m-2 s-1 Wildfire flux of higher alkanes (CnH2n+2, c>=4) kg m-2 s-1 Wildfire flux of higher alkanes (CnH2n+2, c>=4) kg m-2 s-1 Wildfire flux of higher alkenes (CnH2n, c>=4) kg m-2 s-1 Wildfire flux of higher alkenes (CnH2n, c>=4) kg m-2 s-1 Wildfire flux of hydrogen (H) kg m-2 s-1 Wildfire flux of hydrogen (H) kg m-2 s-1 Wildfire flux of isoprene (C5H8) kg m-2 s-1 Wildfire flux of isoprene (C5H8) kg m-2 s-1 Wildfire flux of methane (CH4) kg m-2 s-1 Wildfire flux of methane (CH4) kg m-2 s-1 Wildfire flux of methanol (CH3OH) kg m-2 s-1 Wildfire flux of methanol (CH3OH) kg m-2 s-1 Wildfire flux of nitrogen oxides (NOx) kg m-2 s-1 Wildfire flux of nitrogen oxides (NOx) kg m-2 s-1 Wildfire flux of nitrous oxide (N20) kg m-2 s-1 Wildfire flux of nitrous oxide (N20) kg m-2 s-1 Wildfire flux of non-methane hydrocarbons kg m-2 s-1 Wildfire flux of non-methane hydrocarbons kg m-2 s-1 Wildfire flux of octene (C8H16) kg m-2 s-1 Wildfire flux of octene (C8H16) kg m-2 s-1 Wildfire flux of organic carbon kg m-2 s-1 Wildfire flux of organic carbon kg m-2 s-1 Wildfire flux of particulate matter d < 2.5 µm (PM2.5) kg m-2 s-1 Wildfire flux of particulate matter d < 2.5 µm (PM2.5) kg m-2 s-1 Wildfire flux of pentanes (C5H12) kg m-2 s-1 Wildfire flux of pentanes (C5H12) kg m-2 s-1 Wildfire flux of pentenes (C5H10) kg m-2 s-1 Wildfire flux of pentenes (C5H10) kg m-2 s-1 Wildfire flux of propane (C3H8) kg m-2 s-1 Wildfire flux of propane (C3H8) kg m-2 s-1 Wildfire flux of propene (C3H6) kg m-2 s-1 Wildfire flux of propene (C3H6) kg m-2 s-1 Wildfire flux of sulphur dioxide (SO2) kg m-2 s-1 Wildfire flux of sulphur dioxide (SO2) kg m-2 s-1 Wildfire flux of terpenes ((C5H8)n) kg m-2 s-1 Wildfire flux of terpenes ((C5H8)n) kg m-2 s-1 Wildfire flux of toluene (C7H8) kg m-2 s-1 Wildfire flux of toluene (C7H8) kg m-2 s-1 Wildfire flux of toluene_lump (C7H8+ C6H6 + C8H10) kg m-2 s-1 Wildfire flux of toluene_lump (C7H8+ C6H6 + C8H10) kg m-2 s-1 Wildfire flux of total carbon in aerosols kg m-2 s-1 Wildfire flux of total carbon in aerosols kg m-2 s-1 Wildfire flux of total particulate matter kg m-2 s-1 Wildfire flux of total particulate matter kg m-2 s-1 Wildfire flux of xylene (C8H10) kg m-2 s-1 Wildfire flux of xylene (C8H10) kg m-2 s-1 Wildfire fraction of area observed dimensionless Wildfire fraction of area observed dimensionless Wildfire overall flux of burnt carbon kg m-2 s-1 Wildfire overall flux of burnt carbon kg m-2 s-1 Wildfire radiative power W m-2 Wildfire radiative power W m-2 395 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-era5-single-levels-monthly-means-preliminary https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means-preliminary-back-extension reanalysis-era5-single-levels-monthly-means-preliminary-back-extension This entry is a preliminary version of the ERA5 reanalysis back extension from 1950 to 1978. It has now been superseded by the ERA5 Climate Data Store entries from 1940 onwards and will be deprecated in due course. Therefore, users are advised to use the latter, final release, instead. Although in many other respects the quality of this dataset is quite satisfactory (Bell et al., 2021), this preliminary data does suffer from tropical cyclones that are sometimes unrealistically intense. This is in contrast with the ERA5 product from 1959 onwards. For more details see the articles, ERA5 back extension 1950-1978 (Preliminary version): tropical cyclones are too intense and Changes in the ERA5 back extension compared to its preliminary version. (Bell et al., 2021) ERA5 back extension 1950-1978 (Preliminary version): tropical cyclones are too intense Changes in the ERA5 back extension compared to its preliminary version ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Currently, data is available from 1940, with superseded Climate Data Store entries for 1950-1978 (preliminary back extension, this page) and from 1940 onwards (final release plus timely updates). ERA5 replaces the ERA-Interim reanalysis. ERA5 Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. this article these guidelines Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly averaged data on single levels from 1950 to 1978 (preliminary version)". DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Temporal coverage 1950 to 1978 Temporal resolution Monthly File format GRIB Update frequency Monthly DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Horizontal resolution Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Reanalysis: 0.25° x 0.25° (atmosphere), 0.5° x 0.5° (ocean waves) Mean, spread and members: 0.5° x 0.5° (atmosphere), 1° x 1° (ocean waves) Temporal coverage 1950 to 1978 Temporal coverage 1950 to 1978 Temporal resolution Monthly Temporal resolution Monthly File format GRIB File format GRIB Update frequency Monthly Update frequency Monthly MAIN VARIABLES Name Units Description 100m u-component of wind m s-1 This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. 100m v-component of wind m s-1 This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. 10m u-component of neutral wind m s-1 This parameter is the eastward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m u-component of wind m s-1 This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). 10m v-component of neutral wind m s-1 This parameter is the northward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m v-component of wind m s-1 This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). 10m wind speed m s-1 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). The eastward and northward components of the horizontal wind at 10m are also available as parameters. 2m dewpoint temperature K This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Air density over the oceans kg m-3 This parameter is the mass of air per cubic metre over the oceans, derived from the temperature, specific humidity and pressure at the lowest model level in the atmospheric model. This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Angle of sub-gridscale orography radians This parameter is one of four parameters (the others being standard deviation, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. The angle of the sub-grid scale orography characterises the geographical orientation of the terrain in the horizontal plane (from a bird's-eye view) relative to an eastwards axis. This parameter does not vary in time. Anisotropy of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, slope and angle of sub-gridscale orography) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter is a measure of how much the shape of the terrain in the horizontal plane (from a bird's-eye view) is distorted from a circle. A value of one is a circle, less than one an ellipse, and 0 is a ridge. In the case of a ridge, wind blowing parallel to it does not exert any drag on the flow, but wind blowing perpendicular to it exerts the maximum drag. This parameter does not vary in time. Benjamin-feir index Dimensionless This parameter is used to calculate the likelihood of freak ocean waves, which are waves that are higher than twice the mean height of the highest third of waves. Large values of this parameter (in practice of the order 1) indicate increased probability of the occurrence of freak waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived from the statistics of the two-dimensional wave spectrum. More precisely, it is the square of the ratio of the integral ocean wave steepness and the relative width of the frequency spectrum of the waves. Further information on the calculation of this parameter is given in Section 10.6 of the ECMWF Wave Model documentation. Boundary layer dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Boundary layer height m This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface. The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop. The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review. Charnock Dimensionless This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind. When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by the ECMWF Wave Model. Clear-sky direct solar radiation at surface J m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Cloud base height m The height above the Earth's surface of the base of the lowest cloud layer, at the specified time. This parameter is calculated by searching from the second lowest model level upwards, to the height of the level where cloud fraction becomes greater than 1% and condensate content greater than 1.E-6 kg kg-1. Fog (i.e., cloud in the lowest model layer) is not considered when defining cloud base height. Coefficient of drag with waves Dimensionless This parameter is the resistance that ocean waves exert on the atmosphere. It is sometimes also called a "friction coefficient". It is calculated by the wave model as the ratio of the square of the friction velocity, to the square of the neutral wind speed at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. Convective available potential energy J kg-1 This is an indication of the instability (or stability) of the atmosphere and can be used to assess the potential for the development of convection, which can lead to heavy rainfall, thunderstorms and other severe weather. In the ECMWF Integrated Forecasting System (IFS), CAPE is calculated by considering parcels of air departing at different model levels below the 350 hPa level. If a parcel of air is more buoyant (warmer and/or with more moisture) than its surrounding environment, it will continue to rise (cooling as it rises) until it reaches a point where it no longer has positive buoyancy. CAPE is the potential energy represented by the total excess buoyancy. The maximum CAPE produced by the different parcels is the value retained. Large positive values of CAPE indicate that an air parcel would be much warmer than its surrounding environment and therefore, very buoyant. CAPE is related to the maximum potential vertical velocity of air within an updraft; thus, higher values indicate greater potential for severe weather. Observed values in thunderstorm environments often may exceed 1000 joules per kilogram (J kg-1), and in extreme cases may exceed 5000 J kg-1. The calculation of this parameter assumes: (i) the parcel of air does not mix with surrounding air; (ii) ascent is pseudo-adiabatic (all condensed water falls out) and (iii) other simplifications related to the mixed-phase condensational heating. Convective inhibition J kg-1 This parameter is a measure of the amount of energy required for convection to commence. If the value of this parameter is too high, then deep, moist convection is unlikely to occur even if the convective available potential energy or convective available potential energy shear are large. CIN values greater than 200 J kg-1 would be considered high. An atmospheric layer where temperature increases with height (known as a temperature inversion) would inhibit convective uplift and is a situation in which convective inhibition would be large. Convective precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Rainfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Downward UV radiation at the surface J m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Duct base height m Duct base height as diagnosed from the vertical gradient of atmospheric refractivity. Eastward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Eastward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Forecast albedo Dimensionless This parameter is a measure of the reflectivity of the Earth's surface. It is the fraction of short-wave (solar) radiation reflected by the Earth's surface, for diffuse radiation, assuming a fixed spectrum of downward short-wave radiation at the surface. The values of this parameter vary between zero and one. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Short-wave radiation from the Sun is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Forecast logarithm of surface roughness for heat Dimensionless This parameter is the natural logarithm of the roughness length for heat. The surface roughness for heat is a measure of the surface resistance to heat transfer. This parameter is used to determine the air to surface transfer of heat. For given atmospheric conditions, a higher surface roughness for heat means that it is more difficult for the air to exchange heat with the surface. A lower surface roughness for heat means that it is easier for the air to exchange heat with the surface. Over the ocean, surface roughness for heat depends on the waves. Over sea-ice, it has a constant value of 0.001 m. Over land, it is derived from the vegetation type and snow cover. Forecast surface roughness m This parameter is the aerodynamic roughness length in metres. It is a measure of the surface resistance. This parameter is used to determine the air to surface transfer of momentum. For given atmospheric conditions, a higher surface roughness causes a slower near-surface wind speed. Over ocean, surface roughness depends on the waves. Over land, surface roughness is derived from the vegetation type and snow cover. Free convective velocity over the oceans m s-1 This parameter is an estimate of the vertical velocity of updraughts generated by free convection. Free convection is fluid motion induced by buoyancy forces, which are driven by density gradients. The free convective velocity is used to estimate the impact of wind gusts on ocean wave growth. It is calculated at the height of the lowest temperature inversion (the height above the surface of the Earth where the temperature increases with height). This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Friction velocity m s-1 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is a theoretical wind speed at the Earth's surface that expresses the magnitude of stress. It is calculated by dividing the surface stress by air density and taking its square root. For turbulent flow, the friction velocity is approximately constant in the lowest few metres of the atmosphere. This parameter increases with the roughness of the surface. It is used to calculate the way wind changes with height in the lowest levels of the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location at the surface of the Earth, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The (surface) geopotential height (orography) can be calculated by dividing the (surface) geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2 ). This parameter does not vary in time. Gravity wave dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. High cloud cover Dimensionless The proportion of a grid box covered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a "standard atmosphere")). The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. High vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "high". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Ice temperature layer 1 K This parameter is the sea-ice temperature in layer 1 (0 to 7cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 1, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 2 K This parameter is the sea-ice temperature in layer 2 (7 to 28cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 2, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 3 K This parameter is the sea-ice temperature in layer 3 (28 to 100cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 3, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 4 K This parameter is the sea-ice temperature in layer 4 (100 to 150cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 4, grid point value would be contaminated with fictitious zero ice values. Instantaneous 10m wind gust m s-1 This parameter is the maximum wind gust at the specified time, at a height of ten metres above the surface of the Earth. The WMO defines a wind gust as the maximum of the wind averaged over 3 second intervals. This duration is shorter than a model time step, and so the ECMWF Integrated Forecasting System (IFS) deduces the magnitude of a gust within each time step from the time-step-averaged surface stress, surface friction, wind shear and stability. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Instantaneous eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. Instantaneous large-scale surface precipitation fraction Dimensionless This parameter is the fraction of the grid box (0-1) covered by large-scale precipitation at the specified time. Large-scale precipitation is rain and snow that falls to the Earth's surface, and is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. Precipitation can also be due to convection generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box. Instantaneous moisture flux kg m-2 s-1 This parameter is the net rate of moisture exchange between the land/ocean surface and the atmosphere, due to the processes of evaporation (including evapotranspiration) and condensation, at the specified time. By convention, downward fluxes are positive, which means that evaporation is represented by negative values and condensation by positive values. Instantaneous northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. Instantaneous surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere, at the specified time, through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. The ECMWF convention for vertical fluxes is positive downwards. K index K This parameter is a measure of the potential for a thunderstorm to develop, calculated from the temperature and dew point temperature in the lower part of the atmosphere. The calculation uses the temperature at 850, 700 and 500 hPa and dewpoint temperature at 850 and 700 hPa. Higher values of K indicate a higher potential for the development of thunderstorms. This parameter is related to the probability of occurrence of a thunderstorm: <20 K: No thunderstorm, 20-25 K: Isolated thunderstorms, 26-30 K: Widely scattered thunderstorms, 31-35 K: Scattered thunderstorms, >35 K: Numerous thunderstorms. Lake bottom temperature K This parameter is the temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake cover Dimensionless This parameter is the proportion of a grid box covered by inland water bodies (lakes, reservoirs, rivers and coastal waters). Values vary between 0: no inland water, and 1: grid box is fully covered with inland water. This parameter is specified from observations and does not vary in time. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth m This parameter is the mean depth of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is specified from in-situ measurements and indirect estimates and does not vary in time. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake ice depth m This parameter is the thickness of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter is the thickness of that ice layer. Lake ice temperature K This parameter is the temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). It is the temperature at the ice/atmosphere or ice/snow interface. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer depth m This parameter is the thickness of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake mix-layer temperature K This parameter is the temperature of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake shape factor Dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) i.e., it describes the shape of the vertical temperature profile. It is used to calculate the lake bottom temperature and other lake-related parameters. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake total layer temperature K This parameter is the mean temperature of the total water column in inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. This parameter is the mean temperature over the two layers. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Land-sea mask Dimensionless This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless. In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water. In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water. This parameter does not vary in time. Large scale rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Rainfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation fraction s This parameter is the accumulation of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Large-scale snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Leaf area index, high vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "high". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Leaf area index, low vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "low". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Low cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a "standard atmosphere")). Assumptions are made about the degree of overlap/randomness between clouds in different model levels. This parameter has values from 0 to 1. Low vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "low". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Magnitude of turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the magnitude of the accumulated stress on the Earth's surface, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Maximum individual wave height m This parameter is an estimate of the height of the expected highest individual wave within a 20 minute time window. It can be used as a guide to the likelihood of extreme or freak waves. The interactions between waves are non-linear and occasionally concentrate wave energy giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered as a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Mean boundary layer dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean convective precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean direction of total swell degrees This parameter is the mean direction of waves associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of wind waves degrees The mean direction of waves generated by local winds. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea wave spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean eastward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean evaporation rate kg m-2 s-1 This parameter is the amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Mean gravity wave dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean large-scale precipitation fraction Dimensionless This parameter is the mean of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean large-scale precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean magnitude of turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the magnitude of the mean stress on the Earth's surface, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean northward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean period of total swell s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea associated with swell, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. Mean period of wind waves s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea generated by local winds, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea spectrum. Mean potential evaporation rate kg m-2 s-1 This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean sea level pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of mean sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at mean sea level. It is calculated over all surfaces - land, sea and inland water. Maps of mean sea level pressure are used to identify the locations of low and high pressure weather systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds. The units of this parameter are pascals (Pa). Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Mean snow evaporation rate kg m-2 s-1 This parameter is the average rate of snow evaporation from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snow evaporation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Mean snowfall rate kg m-2 s-1 This parameter is the rate of snowfall at the Earth's surface. It is the sum of large-scale and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean snowmelt rate kg m-2 s-1 This parameter is the rate of snow melt in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the melting would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean square slope of waves Dimensionless This parameter can be related analytically to the average slope of combined wind-sea and swell waves. It can also be expressed as a function of wind speed under some statistical assumptions. The higher the slope, the steeper the waves. This parameter indicates the roughness of the sea/ocean surface which affects the interaction between ocean and atmosphere. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. Mean sub-surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface direct short-wave radiation flux W m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux, clear sky W m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward UV radiation flux W m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux, clear sky W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface latent heat flux W m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux, clear sky W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top downward short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux W m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux, clear sky W m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean top net short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux, clear sky W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean total precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface. It is the sum of the rates due to large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean vertical gradient of refractivity inside trapping layer m-1 Mean vertical gradient of atmospheric refractivity inside the trapping layer. Mean vertically integrated moisture divergence kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean wave direction degree true This parameter is the mean direction of ocean/sea surface waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use this type of wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of first swell partition degrees This parameter is the mean direction of waves in the first swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of second swell partition degrees This parameter is the mean direction of waves in the second swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of third swell partition degrees This parameter is the mean direction of waves in the third swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave period s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use such wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. Mean wave period based on first moment s This parameter is the reciprocal of the mean frequency of the wave components that represent the sea state. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for swell s This parameter is the reciprocal of the mean frequency of the wave components associated with swell. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for wind waves s This parameter is the reciprocal of the mean frequency of the wave components generated by local winds. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with wind waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for swell s This parameter is equivalent to the zero-crossing mean wave period for swell. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for wind waves s This parameter is equivalent to the zero-crossing mean wave period for waves generated by local winds. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period of first swell partition s This parameter is the mean period of waves in the first swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of second swell partition s This parameter is the mean period of waves in the second swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of third swell partition s This parameter is the mean period of waves in the third swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third swell partition might be from one system at one location and a different system at the neighbouring location). Mean zero-crossing wave period s This parameter represents the mean length of time between occasions where the sea/ocean surface crosses mean sea level. In combination with wave height information, it could be used to assess the length of time that a coastal structure might be under water, for example. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). In the ECMWF Integrated Forecasting System (IFS) this parameter is calculated from the characteristics of the two-dimensional wave spectrum. Medium cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a "standard atmosphere")). The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. Minimum vertical gradient of refractivity inside trapping layer m-1 Minimum vertical gradient of atmospheric refractivity inside the trapping layer. Model bathymetry m This parameter is the depth of water from the surface to the bottom of the ocean. It is used by the ocean wave model to specify the propagation properties of the different waves that could be present. Note that the ocean wave model grid is too coarse to resolve some small islands and mountains on the bottom of the ocean, but they can have an impact on surface ocean waves. The ocean wave model has been modified to reduce the wave energy flowing around or over features at spatial scales smaller than the grid box. Near IR albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Normalized energy flux into ocean Dimensionless This parameter is the normalised vertical flux of turbulent kinetic energy from ocean waves into the ocean. The energy flux is calculated from an estimation of the loss of wave energy due to white capping waves. A white capping wave is one that appears white at its crest as it breaks, due to air being mixed into the water. When waves break in this way, there is a transfer of energy from the waves to the ocean. Such a flux is defined to be negative. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into waves Dimensionless This parameter is the normalised vertical flux of energy from wind into the ocean waves. A positive flux implies a flux into the waves. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized stress into ocean Dimensionless This parameter is the normalised surface stress, or momentum flux, from the air into the ocean due to turbulence at the air-sea interface and breaking waves. It does not include the flux used to generate waves. The ECMWF convention for vertical fluxes is positive downwards. The stress has units of Newtons per metre squared, and this is normalised by being divided by the product of air density and the square of the friction velocity. Northward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Northward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Ocean surface stress equivalent 10m neutral wind direction degrees This parameter is the direction from which the "neutral wind" blows, in degrees clockwise from true north, at a height of ten metres above the surface of the Earth. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind direction used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind speed m s-1 This parameter is the horizontal speed of the "neutral wind", at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind speed used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Period corresponding to maximum individual wave height s This parameter is the period of the expected highest individual wave within a 20-minute time window. It can be used as a guide to the characteristics of extreme or freak waves. Wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. Occasionally waves of different periods reinforce and interact non-linearly giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered to be a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Potential evaporation m This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Precipitation type Dimensionless This parameter describes the type of precipitation at the surface, at the specified time. A precipitation type is assigned wherever there is a non-zero value of precipitation. In the ECMWF Integrated Forecasting System (IFS) there are only two predicted precipitation variables: rain and snow. Precipitation type is derived from these two predicted variables in combination with atmospheric conditions, such as temperature. Values of precipitation type defined in the IFS: 0: No precipitation, 1: Rain, 3: Freezing rain (i.e. supercooled raindrops which freeze on contact with the ground and other surfaces), 5: Snow, 6: Wet snow (i.e. snow particles which are starting to melt), 7: Mixture of rain and snow, 8: Ice pellets. These precipitation types are consistent with WMO Code Table 4.201. Other types in this WMO table are not defined in the IFS. The monthly mean procedure applied to such integers, will yield non-integer values. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Sea surface temperature K This parameter (SST) is the temperature of sea water near the surface. In ERA5, this parameter is a foundation SST, which means there are no variations due to the daily cycle of the sun (diurnal variations). SST, in ERA5, is given by two external providers. Before September 2007, SST from the HadISST2 dataset is used and from September 2007 onwards, the OSTIA dataset is used. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Sea-ice cover Dimensionless This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land-sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover. In ERA5, sea-ice cover is given by two external providers. Before 1979 the HadISST2 dataset is used. From 1979 to August 2007 the OSI SAF (409a) dataset is used and from September 2007 the OSI SAF oper dataset is used. Sea ice is frozen sea water which floats on the surface of the ocean. Sea ice does not include ice which forms on land such as glaciers, icebergs and ice-sheets. It also excludes ice shelves which are anchored on land, but protrude out over the surface of the ocean. These phenomena are not modelled by the IFS. Long-term monitoring of sea ice is important for understanding climate change. Sea ice also affects shipping routes through the polar regions. Significant height of combined wind waves and swell m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. This parameter can be used to assess sea state and swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of total swell m This parameter represents the average height of the highest third of surface ocean/sea waves associated with swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of total swell only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional total swell spectrum. The total swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of wind waves m This parameter represents the average height of the highest third of surface ocean/sea waves generated by the local wind. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wind-sea wave spectrum. The wind-sea wave spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are still under the influence of the local wind. This parameter can be used to assess wind-sea waves. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of first swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the first swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of second swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the second swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of third swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the third swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Skin reservoir content m of water equivalent This parameter is the amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of "skin reservoir content" a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the "skin reservoir" by evaporation. Skin temperature K This parameter is the temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Slope of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, angle and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the slope of the sub-grid valleys, hills and mountains. A flat surface has a value of 0, and a 45 degree slope has a value of 0.5. This parameter does not vary in time. Snow albedo Dimensionless This parameter is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter changes with snow age and also depends on vegetation height. It has a range of values between 0 and 1. For low vegetation, it ranges between 0.52 for old snow and 0.88 for fresh snow. For high vegetation with snow underneath, it depends on vegetation type and has values between 0.27 and 0.38. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean snow albedo, grid point value would be contaminated with fictitious zero snow values. Snow density kg m-3 This parameter is the mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean snow density, grid point value would be contaminated with fictitious zero snow values. Snow depth m of water equivalent This parameter is the amount of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from snow from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the evaporated snow (from the snow-covered area of a grid box) were liquid and were spread evenly over the whole grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Snowmelt m of water equivalent This parameter is the accumulated amount of water that has melted from snow in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the melted snow (from the snow-covered area of a grid box) were spread evenly over the whole grid box. For example, if half the grid box were covered in snow with a water equivalent depth of 0.02m, this parameter would have a value of 0.01m. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Soil temperature level 1 K This parameter is the temperature of the soil at level 1 (in the middle of layer 1). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K This parameter is the temperature of the soil at level 2 (in the middle of layer 2). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 3 K This parameter is the temperature of the soil at level 3 (in the middle of layer 3). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 4 K This parameter is the temperature of the soil at level 4 (in the middle of layer 4). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil type Dimensionless This parameter is the texture (or classification) of soil used by the land surface scheme of the ECMWF Integrated Forecasting System (IFS) to predict the water holding capacity of soil in soil moisture and runoff calculations. It is derived from the root zone data (30-100 cm below the surface) of the FAO/UNESCO Digital Soil Map of the World, DSMW (FAO, 2003), which exists at a resolution of 5' X 5' (about 10 km). The seven soil types are: 1: Coarse, 2: Medium, 3: Medium fine, 4: Fine, 5: Very fine, 6: Organic, 7: Tropical organic. A value of 0 indicates a non-land point. This parameter does not vary in time. Standard deviation of filtered subgrid orography m Climatological parameter (scales between approximately 3 and 22 km are included). This parameter does not vary in time. Standard deviation of orography Dimensionless This parameter is one of four parameters (the others being angle of sub-gridscale orography, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the standard deviation of the height of the sub-grid valleys, hills and mountains within a grid box. This parameter does not vary in time. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface latent heat flux J m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation, clear sky J m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation, clear sky J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of land, sea and inland water. It is a measure of the weight of all the air in a column vertically above a point on the Earth's surface. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure weather systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa). Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface sensible heat flux J m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downward, clear sky J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downward, clear sky J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. TOA incident solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Temperature of snow layer K This parameter gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean temperature of snow layer, grid point value would be contaminated with fictitious zero snow values. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Top net solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation, clear sky J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation J m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation, clear sky J m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). Note that OLR is typically shown in units of watts per square metre (W m-2 ). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. Total cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights. Cloud fractions vary from 0 to 1. Total column cloud ice water kg m-2 This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud liquid water kg m-2 This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can also be referred to as total ozone, or vertically integrated ozone. The values are dominated by ozone within the stratosphere. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. In the IFS, the units for total ozone are kilograms per square metre, but before 12/06/2001 dobson units were used. Dobson units (DU) are still used extensively for total column ozone. 1 DU = 2.1415E-5 kg m-2 Total column rain water kg m-2 This parameter is the total amount of water in droplets of raindrop size (which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column snow water kg m-2 This parameter is the total amount of water in the form of snow (aggregated ice crystals which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column supercooled liquid water kg m-2 This parameter is the total amount of supercooled water in a column extending from the surface of the Earth to the top of the atmosphere. Supercooled water is water that exists in liquid form below 0oC. It is common in cold clouds and is important in the formation of precipitation. Also, supercooled water in clouds extending to the surface (i.e., fog) can cause icing/riming of various structures. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column water kg m-2 This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for. Total column water vapour kg m-2 This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Total precipitation m This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Total sky direct solar radiation at surface J m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Total totals index K This parameter gives an indication of the probability of occurrence of a thunderstorm and its severity by using the vertical gradient of temperature and humidity. The values of this index indicate the following: <44 K: Thunderstorms not likely, 44-50 K: Thunderstorms likely, 51-52 K: Isolated severe thunderstorms, 53-56 K: Widely scattered severe thunderstorms, 56-60 K: Scattered severe thunderstorms more likely. The total totals index is the temperature difference between 850 hPa (near surface) and 500 hPa (mid-troposphere) (lapse rate) plus a measure of the moisture content between 850 hPa and 500 hPa. The probability of deep convection tends to increase with increasing lapse rate and atmospheric moisture content. There are a number of limitations to this index. Also, the interpretation of the index value varies with season and location. Trapping layer base height m Trapping layer base height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer top height m Trapping layer top height as diagnosed from the vertical gradient of atmospheric refractivity. Type of high vegetation Dimensionless This parameter indicates the 6 types of high vegetation recognised by the ECMWF Integrated Forecasting System: 3 = Evergreen needleleaf trees, 4 = Deciduous needleleaf trees, 5 = Deciduous broadleaf trees, 6 = Evergreen broadleaf trees, 18 = Mixed forest/woodland, 19 = Interrupted forest. A value of 0 indicates a point without high vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of low vegetation Dimensionless This parameter indicates the 10 types of low vegetation recognised by the ECMWF Integrated Forecasting System: 1 = Crops, Mixed farming, 2 = Grass, 7 = Tall grass, 9 = Tundra, 10 = Irrigated crops, 11 = Semidesert, 13 = Bogs and marshes, 16 = Evergreen shrubs, 17 = Deciduous shrubs, 20 = Water and land mixtures. A value of 0 indicates a point without low vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. U-component stokes drift m s-1 This parameter is the eastward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. UV visible albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. This parameter varies between 0 and 1. UV visible albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. V-component stokes drift m s-1 This parameter is the northward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. Vertical integral of divergence of cloud frozen water flux kg m-2 s-1 The vertical integral of the cloud frozen water flux is the horizontal rate of flow of cloud frozen water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud frozen water spreading outward from a point, per square metre. This parameter is positive for cloud frozen water that is spreading out, or diverging, and negative for the opposite, for cloud frozen water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud frozen water. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of divergence of cloud liquid water flux kg m-2 s-1 The vertical integral of the cloud liquid water flux is the horizontal rate of flow of cloud liquid water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud liquid water spreading outward from a point, per square metre. This parameter is positive for cloud liquid water that is spreading out, or diverging, and negative for the opposite, for cloud liquid water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud liquid water. Vertical integral of divergence of geopotential flux W m-2 The vertical integral of the geopotential flux is the horizontal rate of flow of geopotential, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of geopotential spreading outward from a point, per square metre. This parameter is positive for geopotential that is spreading out, or diverging, and negative for the opposite, for geopotential that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of geopotential. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of kinetic energy flux W m-2 The vertical integral of the kinetic energy flux is the horizontal rate of flow of kinetic energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of kinetic energy spreading outward from a point, per square metre. This parameter is positive for kinetic energy that is spreading out, or diverging, and negative for the opposite, for kinetic energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of kinetic energy. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of mass flux kg m-2 s-1 The vertical integral of the mass flux is the horizontal rate of flow of mass, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of mass spreading outward from a point, per square metre. This parameter is positive for mass that is spreading out, or diverging, and negative for the opposite, for mass that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of mass. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of divergence of moisture flux kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Vertical integral of divergence of ozone flux kg m-2 s-1 The vertical integral of the ozone flux is the horizontal rate of flow of ozone, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of ozone spreading outward from a point, per square metre. This parameter is positive for ozone that is spreading out, or diverging, and negative for the opposite, for ozone that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of ozone. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of divergence of thermal energy flux W m-2 The vertical integral of the thermal energy flux is the horizontal rate of flow of thermal energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of thermal energy spreading outward from a point, per square metre. This parameter is positive for thermal energy that is spreading out, or diverging, and negative for the opposite, for thermal energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of thermal energy. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the flow of thermal energy through the climate system and to investigate the atmospheric energy budget. Vertical integral of divergence of total energy flux W m-2 The vertical integral of the total energy flux is the horizontal rate of flow of total energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of total energy spreading outward from a point, per square metre. This parameter is positive for total energy that is spreading out, or diverging, and negative for the opposite, for total energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of total energy. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of eastward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the eastward direction, per meter across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of eastward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from west to east. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of eastward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of energy conversion W m-2 This parameter is one contribution to the amount of energy being converted between kinetic energy, and internal plus potential energy, for a column of air extending from the surface of the Earth to the top of the atmosphere. Negative values indicate a conversion to kinetic energy from potential plus internal energy. This parameter can be used to study the atmospheric energy budget. The circulation of the atmosphere can also be considered in terms of energy conversions. Vertical integral of kinetic energy J m-2 This parameter is the vertical integral of kinetic energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of mass of atmosphere kg m-2 This parameter is the total mass of air for a column extending from the surface of the Earth to the top of the atmosphere, per square metre. This parameter is calculated by dividing surface pressure by the Earth's gravitational acceleration, g (=9.80665 m s-2 ), and has units of kilograms per square metre. This parameter can be used to study the atmospheric mass budget. Vertical integral of mass tendency kg m-2 s-1 This parameter is the rate of change of the mass of a column of air extending from the Earth's surface to the top of the atmosphere. An increasing mass of the column indicates rising surface pressure. In contrast, a decrease indicates a falling surface pressure. The mass of the column is calculated by dividing pressure at the Earth's surface by the gravitational acceleration, g (=9.80665 m s-2 ). This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of northward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from south to north. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of northward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of potential and internal energy J m-2 This parameter is the mass weighted vertical integral of potential and internal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential, internal and latent energy J m-2 This parameter is the mass weighted vertical integral of potential, internal and latent energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The latent energy refers to the energy associated with the water vapour in the atmosphere and is equal to the energy required to convert liquid water into water vapour. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of temperature K kg m-2 This parameter is the mass-weighted vertical integral of temperature for a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can be used to study the atmospheric energy budget. Vertical integral of thermal energy J m-2 This parameter is the mass-weighted vertical integral of thermal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Thermal energy is calculated from the product of temperature and the specific heat capacity of air at constant pressure. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of total energy J m-2 This parameter is the vertical integral of total energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertically integrated moisture divergence kg m-2 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm. Volumetric soil water layer 1 m3 m-3 This parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 This parameter is the volume of water in soil layer 2 (7 - 28cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 3 m3 m-3 This parameter is the volume of water in soil layer 3 (28 - 100cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 4 m3 m-3 This parameter is the volume of water in soil layer 4 (100 - 289cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Wave spectral directional width Dimensionless This parameter indicates whether waves (generated by local winds and associated with swell) are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for swell Dimensionless This parameter indicates whether waves associated with swell are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for wind waves Dimensionless This parameter indicates whether waves generated by the local wind are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral kurtosis Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It shows how much of the probability density function of the sea surface elevation exists in the tails of the distribution. So, a positive kurtosis (typical range 0.0 to 0.06) means more frequent occurrences of very extreme values (either above or below the mean), relative to a normal distribution. Wave spectral peakedness Dimensionless This parameter is a statistical measure used to forecast extreme or freak waves. It is a measure of the relative width of the ocean/sea wave frequency spectrum (i.e., whether the ocean/sea wave field is made up of a narrow or broad range of frequencies). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). When the wave field is more focussed around a narrow range of frequencies, the probability of freak/extreme waves increases. This parameter is Goda's peakedness factor and is used to calculate the Benjamin-Feir Index (BFI). The BFI is in turn used to estimate the probability and nature of extreme/freak waves. Wave spectral skewness Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It is a measure of the asymmetry of the probability density function of the sea surface elevation. So, a positive/negative skewness (typical range -0.2 to 0.12) means more frequent occurrences of extreme values above/below the mean, relative to a normal distribution. Zero degree level m The height above the Earth's surface where the temperature passes from positive to negative values, corresponding to the top of a warm layer, at the specified time. This parameter can be used to help forecast snow. If more than one warm layer is encountered, then the zero degree level corresponds to the top of the second atmospheric layer. This parameter is set to zero when the temperature in the whole atmosphere is below 0℃. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description 100m u-component of wind m s-1 This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. 100m u-component of wind m s-1 This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. 100m v-component of wind m s-1 This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. 100m v-component of wind m s-1 This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. 10m u-component of neutral wind m s-1 This parameter is the eastward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m u-component of neutral wind m s-1 This parameter is the eastward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m u-component of wind m s-1 This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). 10m u-component of wind m s-1 This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). 10m v-component of neutral wind m s-1 This parameter is the northward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m v-component of neutral wind m s-1 This parameter is the northward component of the "neutral wind", at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is slower than the actual wind in stable conditions, and faster in unstable conditions. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on land surface properties or the sea state. 10m v-component of wind m s-1 This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). 10m v-component of wind m s-1 This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). 10m wind speed m s-1 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). The eastward and northward components of the horizontal wind at 10m are also available as parameters. 10m wind speed m s-1 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System (IFS). The eastward and northward components of the horizontal wind at 10m are also available as parameters. 2m dewpoint temperature K This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m dewpoint temperature K This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. 2m temperature K This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Air density over the oceans kg m-3 This parameter is the mass of air per cubic metre over the oceans, derived from the temperature, specific humidity and pressure at the lowest model level in the atmospheric model. This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Air density over the oceans kg m-3 This parameter is the mass of air per cubic metre over the oceans, derived from the temperature, specific humidity and pressure at the lowest model level in the atmospheric model. This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Angle of sub-gridscale orography radians This parameter is one of four parameters (the others being standard deviation, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. The angle of the sub-grid scale orography characterises the geographical orientation of the terrain in the horizontal plane (from a bird's-eye view) relative to an eastwards axis. This parameter does not vary in time. Angle of sub-gridscale orography radians This parameter is one of four parameters (the others being standard deviation, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. The angle of the sub-grid scale orography characterises the geographical orientation of the terrain in the horizontal plane (from a bird's-eye view) relative to an eastwards axis. This parameter does not vary in time. Anisotropy of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, slope and angle of sub-gridscale orography) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter is a measure of how much the shape of the terrain in the horizontal plane (from a bird's-eye view) is distorted from a circle. A value of one is a circle, less than one an ellipse, and 0 is a ridge. In the case of a ridge, wind blowing parallel to it does not exert any drag on the flow, but wind blowing perpendicular to it exerts the maximum drag. This parameter does not vary in time. Anisotropy of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, slope and angle of sub-gridscale orography) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter is a measure of how much the shape of the terrain in the horizontal plane (from a bird's-eye view) is distorted from a circle. A value of one is a circle, less than one an ellipse, and 0 is a ridge. In the case of a ridge, wind blowing parallel to it does not exert any drag on the flow, but wind blowing perpendicular to it exerts the maximum drag. This parameter does not vary in time. Benjamin-feir index Dimensionless This parameter is used to calculate the likelihood of freak ocean waves, which are waves that are higher than twice the mean height of the highest third of waves. Large values of this parameter (in practice of the order 1) indicate increased probability of the occurrence of freak waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived from the statistics of the two-dimensional wave spectrum. More precisely, it is the square of the ratio of the integral ocean wave steepness and the relative width of the frequency spectrum of the waves. Further information on the calculation of this parameter is given in Section 10.6 of the ECMWF Wave Model documentation. Benjamin-feir index Dimensionless This parameter is used to calculate the likelihood of freak ocean waves, which are waves that are higher than twice the mean height of the highest third of waves. Large values of this parameter (in practice of the order 1) indicate increased probability of the occurrence of freak waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived from the statistics of the two-dimensional wave spectrum. More precisely, it is the square of the ratio of the integral ocean wave steepness and the relative width of the frequency spectrum of the waves. Further information on the calculation of this parameter is given in Section 10.6 of the ECMWF Wave Model documentation. Boundary layer dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Boundary layer dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Boundary layer height m This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface. The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop. The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review. Boundary layer height m This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface. The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop. The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review. Charnock Dimensionless This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind. When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by the ECMWF Wave Model. Charnock Dimensionless This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind. When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by the ECMWF Wave Model. Clear-sky direct solar radiation at surface J m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Clear-sky direct solar radiation at surface J m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Cloud base height m The height above the Earth's surface of the base of the lowest cloud layer, at the specified time. This parameter is calculated by searching from the second lowest model level upwards, to the height of the level where cloud fraction becomes greater than 1% and condensate content greater than 1.E-6 kg kg-1. Fog (i.e., cloud in the lowest model layer) is not considered when defining cloud base height. Cloud base height m The height above the Earth's surface of the base of the lowest cloud layer, at the specified time. This parameter is calculated by searching from the second lowest model level upwards, to the height of the level where cloud fraction becomes greater than 1% and condensate content greater than 1.E-6 kg kg-1. Fog (i.e., cloud in the lowest model layer) is not considered when defining cloud base height. Coefficient of drag with waves Dimensionless This parameter is the resistance that ocean waves exert on the atmosphere. It is sometimes also called a "friction coefficient". It is calculated by the wave model as the ratio of the square of the friction velocity, to the square of the neutral wind speed at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. Coefficient of drag with waves Dimensionless This parameter is the resistance that ocean waves exert on the atmosphere. It is sometimes also called a "friction coefficient". It is calculated by the wave model as the ratio of the square of the friction velocity, to the square of the neutral wind speed at a height of 10 metres above the surface of the Earth. The neutral wind is calculated from the surface stress and the corresponding roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. Convective available potential energy J kg-1 This is an indication of the instability (or stability) of the atmosphere and can be used to assess the potential for the development of convection, which can lead to heavy rainfall, thunderstorms and other severe weather. In the ECMWF Integrated Forecasting System (IFS), CAPE is calculated by considering parcels of air departing at different model levels below the 350 hPa level. If a parcel of air is more buoyant (warmer and/or with more moisture) than its surrounding environment, it will continue to rise (cooling as it rises) until it reaches a point where it no longer has positive buoyancy. CAPE is the potential energy represented by the total excess buoyancy. The maximum CAPE produced by the different parcels is the value retained. Large positive values of CAPE indicate that an air parcel would be much warmer than its surrounding environment and therefore, very buoyant. CAPE is related to the maximum potential vertical velocity of air within an updraft; thus, higher values indicate greater potential for severe weather. Observed values in thunderstorm environments often may exceed 1000 joules per kilogram (J kg-1), and in extreme cases may exceed 5000 J kg-1. The calculation of this parameter assumes: (i) the parcel of air does not mix with surrounding air; (ii) ascent is pseudo-adiabatic (all condensed water falls out) and (iii) other simplifications related to the mixed-phase condensational heating. Convective available potential energy J kg-1 This is an indication of the instability (or stability) of the atmosphere and can be used to assess the potential for the development of convection, which can lead to heavy rainfall, thunderstorms and other severe weather. In the ECMWF Integrated Forecasting System (IFS), CAPE is calculated by considering parcels of air departing at different model levels below the 350 hPa level. If a parcel of air is more buoyant (warmer and/or with more moisture) than its surrounding environment, it will continue to rise (cooling as it rises) until it reaches a point where it no longer has positive buoyancy. CAPE is the potential energy represented by the total excess buoyancy. The maximum CAPE produced by the different parcels is the value retained. Large positive values of CAPE indicate that an air parcel would be much warmer than its surrounding environment and therefore, very buoyant. CAPE is related to the maximum potential vertical velocity of air within an updraft; thus, higher values indicate greater potential for severe weather. Observed values in thunderstorm environments often may exceed 1000 joules per kilogram (J kg-1), and in extreme cases may exceed 5000 J kg-1. The calculation of this parameter assumes: (i) the parcel of air does not mix with surrounding air; (ii) ascent is pseudo-adiabatic (all condensed water falls out) and (iii) other simplifications related to the mixed-phase condensational heating. Convective inhibition J kg-1 This parameter is a measure of the amount of energy required for convection to commence. If the value of this parameter is too high, then deep, moist convection is unlikely to occur even if the convective available potential energy or convective available potential energy shear are large. CIN values greater than 200 J kg-1 would be considered high. An atmospheric layer where temperature increases with height (known as a temperature inversion) would inhibit convective uplift and is a situation in which convective inhibition would be large. Convective inhibition J kg-1 This parameter is a measure of the amount of energy required for convection to commence. If the value of this parameter is too high, then deep, moist convection is unlikely to occur even if the convective available potential energy or convective available potential energy shear are large. CIN values greater than 200 J kg-1 would be considered high. An atmospheric layer where temperature increases with height (known as a temperature inversion) would inhibit convective uplift and is a situation in which convective inhibition would be large. Convective precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Rainfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Rainfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Convective snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Downward UV radiation at the surface J m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Downward UV radiation at the surface J m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Duct base height m Duct base height as diagnosed from the vertical gradient of atmospheric refractivity. Duct base height m Duct base height as diagnosed from the vertical gradient of atmospheric refractivity. Eastward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Eastward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Eastward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Eastward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Forecast albedo Dimensionless This parameter is a measure of the reflectivity of the Earth's surface. It is the fraction of short-wave (solar) radiation reflected by the Earth's surface, for diffuse radiation, assuming a fixed spectrum of downward short-wave radiation at the surface. The values of this parameter vary between zero and one. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Short-wave radiation from the Sun is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Forecast albedo Dimensionless This parameter is a measure of the reflectivity of the Earth's surface. It is the fraction of short-wave (solar) radiation reflected by the Earth's surface, for diffuse radiation, assuming a fixed spectrum of downward short-wave radiation at the surface. The values of this parameter vary between zero and one. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Short-wave radiation from the Sun is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%). Forecast logarithm of surface roughness for heat Dimensionless This parameter is the natural logarithm of the roughness length for heat. The surface roughness for heat is a measure of the surface resistance to heat transfer. This parameter is used to determine the air to surface transfer of heat. For given atmospheric conditions, a higher surface roughness for heat means that it is more difficult for the air to exchange heat with the surface. A lower surface roughness for heat means that it is easier for the air to exchange heat with the surface. Over the ocean, surface roughness for heat depends on the waves. Over sea-ice, it has a constant value of 0.001 m. Over land, it is derived from the vegetation type and snow cover. Forecast logarithm of surface roughness for heat Dimensionless This parameter is the natural logarithm of the roughness length for heat. The surface roughness for heat is a measure of the surface resistance to heat transfer. This parameter is used to determine the air to surface transfer of heat. For given atmospheric conditions, a higher surface roughness for heat means that it is more difficult for the air to exchange heat with the surface. A lower surface roughness for heat means that it is easier for the air to exchange heat with the surface. Over the ocean, surface roughness for heat depends on the waves. Over sea-ice, it has a constant value of 0.001 m. Over land, it is derived from the vegetation type and snow cover. Forecast surface roughness m This parameter is the aerodynamic roughness length in metres. It is a measure of the surface resistance. This parameter is used to determine the air to surface transfer of momentum. For given atmospheric conditions, a higher surface roughness causes a slower near-surface wind speed. Over ocean, surface roughness depends on the waves. Over land, surface roughness is derived from the vegetation type and snow cover. Forecast surface roughness m This parameter is the aerodynamic roughness length in metres. It is a measure of the surface resistance. This parameter is used to determine the air to surface transfer of momentum. For given atmospheric conditions, a higher surface roughness causes a slower near-surface wind speed. Over ocean, surface roughness depends on the waves. Over land, surface roughness is derived from the vegetation type and snow cover. Free convective velocity over the oceans m s-1 This parameter is an estimate of the vertical velocity of updraughts generated by free convection. Free convection is fluid motion induced by buoyancy forces, which are driven by density gradients. The free convective velocity is used to estimate the impact of wind gusts on ocean wave growth. It is calculated at the height of the lowest temperature inversion (the height above the surface of the Earth where the temperature increases with height). This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Free convective velocity over the oceans m s-1 This parameter is an estimate of the vertical velocity of updraughts generated by free convection. Free convection is fluid motion induced by buoyancy forces, which are driven by density gradients. The free convective velocity is used to estimate the impact of wind gusts on ocean wave growth. It is calculated at the height of the lowest temperature inversion (the height above the surface of the Earth where the temperature increases with height). This parameter is one of the parameters used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model horizontal grid onto the horizontal grid used by the ocean wave model. Friction velocity m s-1 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is a theoretical wind speed at the Earth's surface that expresses the magnitude of stress. It is calculated by dividing the surface stress by air density and taking its square root. For turbulent flow, the friction velocity is approximately constant in the lowest few metres of the atmosphere. This parameter increases with the roughness of the surface. It is used to calculate the way wind changes with height in the lowest levels of the atmosphere. Friction velocity m s-1 Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is a theoretical wind speed at the Earth's surface that expresses the magnitude of stress. It is calculated by dividing the surface stress by air density and taking its square root. For turbulent flow, the friction velocity is approximately constant in the lowest few metres of the atmosphere. This parameter increases with the roughness of the surface. It is used to calculate the way wind changes with height in the lowest levels of the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location at the surface of the Earth, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The (surface) geopotential height (orography) can be calculated by dividing the (surface) geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2 ). This parameter does not vary in time. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location at the surface of the Earth, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The (surface) geopotential height (orography) can be calculated by dividing the (surface) geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2 ). This parameter does not vary in time. Gravity wave dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Gravity wave dissipation J m-2 This parameter is the accumulated conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. High cloud cover Dimensionless The proportion of a grid box covered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a "standard atmosphere")). The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. High cloud cover Dimensionless The proportion of a grid box covered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a "standard atmosphere")). The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. High vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "high". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. High vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "high". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Ice temperature layer 1 K This parameter is the sea-ice temperature in layer 1 (0 to 7cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 1, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 1 K This parameter is the sea-ice temperature in layer 1 (0 to 7cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 1, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 2 K This parameter is the sea-ice temperature in layer 2 (7 to 28cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 2, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 2 K This parameter is the sea-ice temperature in layer 2 (7 to 28cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 2, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 3 K This parameter is the sea-ice temperature in layer 3 (28 to 100cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 3, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 3 K This parameter is the sea-ice temperature in layer 3 (28 to 100cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 3, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 4 K This parameter is the sea-ice temperature in layer 4 (100 to 150cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 4, grid point value would be contaminated with fictitious zero ice values. Ice temperature layer 4 K This parameter is the sea-ice temperature in layer 4 (100 to 150cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer sea-ice slab: Layer 1: 0-7cm, Layer 2: 7-28cm, Layer 3: 28-100cm, Layer 4: 100-150cm. The temperature of the sea-ice in each layer changes as heat is transferred between the sea-ice layers and the atmosphere above and ocean below. This parameter is defined over the whole globe, even where there is no ocean or sea ice. Regions without sea ice can be masked out by only considering grid points where the monthly mean sea-ice cover does not have a missing value and is greater than 0.0. Grid points with relatively low values of monthly mean sea-ice cover might include periods during the month when the sea-ice cover is 0.0, in which case the corresponding monthly mean ice temperature layer 4, grid point value would be contaminated with fictitious zero ice values. Instantaneous 10m wind gust m s-1 This parameter is the maximum wind gust at the specified time, at a height of ten metres above the surface of the Earth. The WMO defines a wind gust as the maximum of the wind averaged over 3 second intervals. This duration is shorter than a model time step, and so the ECMWF Integrated Forecasting System (IFS) deduces the magnitude of a gust within each time step from the time-step-averaged surface stress, surface friction, wind shear and stability. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Instantaneous 10m wind gust m s-1 This parameter is the maximum wind gust at the specified time, at a height of ten metres above the surface of the Earth. The WMO defines a wind gust as the maximum of the wind averaged over 3 second intervals. This duration is shorter than a model time step, and so the ECMWF Integrated Forecasting System (IFS) deduces the magnitude of a gust within each time step from the time-step-averaged surface stress, surface friction, wind shear and stability. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Instantaneous eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. Instantaneous eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. Instantaneous large-scale surface precipitation fraction Dimensionless This parameter is the fraction of the grid box (0-1) covered by large-scale precipitation at the specified time. Large-scale precipitation is rain and snow that falls to the Earth's surface, and is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. Precipitation can also be due to convection generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box. Instantaneous large-scale surface precipitation fraction Dimensionless This parameter is the fraction of the grid box (0-1) covered by large-scale precipitation at the specified time. Large-scale precipitation is rain and snow that falls to the Earth's surface, and is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. Precipitation can also be due to convection generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box. Instantaneous moisture flux kg m-2 s-1 This parameter is the net rate of moisture exchange between the land/ocean surface and the atmosphere, due to the processes of evaporation (including evapotranspiration) and condensation, at the specified time. By convention, downward fluxes are positive, which means that evaporation is represented by negative values and condensation by positive values. Instantaneous moisture flux kg m-2 s-1 This parameter is the net rate of moisture exchange between the land/ocean surface and the atmosphere, due to the processes of evaporation (including evapotranspiration) and condensation, at the specified time. By convention, downward fluxes are positive, which means that evaporation is represented by negative values and condensation by positive values. Instantaneous northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. Instantaneous northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the surface stress at the specified time, in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. Instantaneous surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere, at the specified time, through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. The ECMWF convention for vertical fluxes is positive downwards. Instantaneous surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere, at the specified time, through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. The ECMWF convention for vertical fluxes is positive downwards. K index K This parameter is a measure of the potential for a thunderstorm to develop, calculated from the temperature and dew point temperature in the lower part of the atmosphere. The calculation uses the temperature at 850, 700 and 500 hPa and dewpoint temperature at 850 and 700 hPa. Higher values of K indicate a higher potential for the development of thunderstorms. This parameter is related to the probability of occurrence of a thunderstorm: <20 K: No thunderstorm, 20-25 K: Isolated thunderstorms, 26-30 K: Widely scattered thunderstorms, 31-35 K: Scattered thunderstorms, >35 K: Numerous thunderstorms. K index K This parameter is a measure of the potential for a thunderstorm to develop, calculated from the temperature and dew point temperature in the lower part of the atmosphere. The calculation uses the temperature at 850, 700 and 500 hPa and dewpoint temperature at 850 and 700 hPa. Higher values of K indicate a higher potential for the development of thunderstorms. This parameter is related to the probability of occurrence of a thunderstorm: <20 K: No thunderstorm, 20-25 K: Isolated thunderstorms, 26-30 K: Widely scattered thunderstorms, 31-35 K: Scattered thunderstorms, >35 K: Numerous thunderstorms. Lake bottom temperature K This parameter is the temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake bottom temperature K This parameter is the temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake cover Dimensionless This parameter is the proportion of a grid box covered by inland water bodies (lakes, reservoirs, rivers and coastal waters). Values vary between 0: no inland water, and 1: grid box is fully covered with inland water. This parameter is specified from observations and does not vary in time. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake cover Dimensionless This parameter is the proportion of a grid box covered by inland water bodies (lakes, reservoirs, rivers and coastal waters). Values vary between 0: no inland water, and 1: grid box is fully covered with inland water. This parameter is specified from observations and does not vary in time. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth m This parameter is the mean depth of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is specified from in-situ measurements and indirect estimates and does not vary in time. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth m This parameter is the mean depth of inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is specified from in-situ measurements and indirect estimates and does not vary in time. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake ice depth m This parameter is the thickness of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter is the thickness of that ice layer. Lake ice depth m This parameter is the thickness of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter is the thickness of that ice layer. Lake ice temperature K This parameter is the temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). It is the temperature at the ice/atmosphere or ice/snow interface. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake ice temperature K This parameter is the temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers and coastal waters). It is the temperature at the ice/atmosphere or ice/snow interface. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer depth m This parameter is the thickness of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake mix-layer depth m This parameter is the thickness of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake mix-layer temperature K This parameter is the temperature of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake mix-layer temperature K This parameter is the temperature of the uppermost layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) that is well mixed and has a near constant temperature with depth (i.e., a uniform distribution of temperature with depth). Mixing can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the water. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake shape factor Dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) i.e., it describes the shape of the vertical temperature profile. It is used to calculate the lake bottom temperature and other lake-related parameters. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake shape factor Dimensionless This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs, rivers and coastal waters) i.e., it describes the shape of the vertical temperature profile. It is used to calculate the lake bottom temperature and other lake-related parameters. This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. Lake total layer temperature K This parameter is the mean temperature of the total water column in inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. This parameter is the mean temperature over the two layers. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Lake total layer temperature K This parameter is the mean temperature of the total water column in inland water bodies (lakes, reservoirs, rivers and coastal waters). This parameter is defined over the whole globe, even where there is no inland water. Regions without inland water can be masked out by only considering grid points where the lake cover is greater than 0.0. In May 2015, a lake model was implemented in the ECMWF Integrated Forecasting System (IFS) to represent the water temperature and lake ice of all the world's major inland water bodies. Lake depth and area fraction (cover) are kept constant in time. Inland water bodies are represented with two layers in the vertical, the mixed layer above and the thermocline below, where temperature changes with depth. This parameter is the mean temperature over the two layers. The upper boundary of the thermocline is located at the mixed layer bottom, and the lower boundary of the thermocline at the lake bottom. A single ice layer is used to represent the formation and melting of ice on inland water bodies. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Land-sea mask Dimensionless This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless. In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water. In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water. This parameter does not vary in time. Land-sea mask Dimensionless This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless. In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water. In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water. This parameter does not vary in time. Large scale rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Rainfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale rain rate kg m-2 s-1 This parameter is the rate of rainfall (rainfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Rainfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the rainfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large scale snowfall rate water equivalent kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity), at the Earth's surface and at the specified time, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation m This parameter is the accumulated precipitation that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale precipitation fraction s This parameter is the accumulation of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Large-scale precipitation fraction s This parameter is the accumulation of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Large-scale snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Large-scale snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Leaf area index, high vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "high". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Leaf area index, high vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "high". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "High vegetation" consists of evergreen trees, deciduous trees, mixed forest/woodland, and interrupted forest. Leaf area index, low vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "low". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Leaf area index, low vegetation m2 m-2 This parameter is the surface area of one side of all the leaves found over an area of land for vegetation classified as "low". This parameter has a value of 0 over bare ground or where there are no leaves. It can be calculated daily from satellite data. It is important for forecasting, for example, how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Low cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a "standard atmosphere")). Assumptions are made about the degree of overlap/randomness between clouds in different model levels. This parameter has values from 0 to 1. Low cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a "standard atmosphere")). Assumptions are made about the degree of overlap/randomness between clouds in different model levels. This parameter has values from 0 to 1. Low vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "low". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Low vegetation cover Dimensionless This parameter is the fraction of the grid box that is covered with vegetation that is classified as "low". The values vary between 0 and 1 but do not vary in time. This is one of the parameters in the model that describes land surface vegetation. "Low vegetation" consists of crops and mixed farming, irrigated crops, short grass, tall grass, tundra, semidesert, bogs and marshes, evergreen shrubs, deciduous shrubs, and water and land mixtures. Magnitude of turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the magnitude of the accumulated stress on the Earth's surface, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Magnitude of turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the magnitude of the accumulated stress on the Earth's surface, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Maximum individual wave height m This parameter is an estimate of the height of the expected highest individual wave within a 20 minute time window. It can be used as a guide to the likelihood of extreme or freak waves. The interactions between waves are non-linear and occasionally concentrate wave energy giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered as a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Maximum individual wave height m This parameter is an estimate of the height of the expected highest individual wave within a 20 minute time window. It can be used as a guide to the likelihood of extreme or freak waves. The interactions between waves are non-linear and occasionally concentrate wave energy giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered as a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Mean boundary layer dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean boundary layer dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The dissipation associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean convective precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Precipitation can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean convective snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the convection scheme in the ECMWF Integrated Forecasting System (IFS). The convection scheme represents convection at spatial scales smaller than the grid box. Snowfall can also be generated by the cloud scheme in the IFS, which represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm thick (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean direction of total swell degrees This parameter is the mean direction of waves associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of total swell degrees This parameter is the mean direction of waves associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of wind waves degrees The mean direction of waves generated by local winds. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea wave spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean direction of wind waves degrees The mean direction of waves generated by local winds. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea wave spectrum. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean eastward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean eastward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean eastward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in an eastward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in an eastward (westward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean evaporation rate kg m-2 s-1 This parameter is the amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Mean evaporation rate kg m-2 s-1 This parameter is the amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation. Mean gravity wave dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean gravity wave dissipation W m-2 This parameter is the mean rate of conversion of kinetic energy in the mean flow into heat, over the whole atmospheric column, per unit area, that is due to the effects of stress associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The dissipation associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean large-scale precipitation fraction Dimensionless This parameter is the mean of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean large-scale precipitation fraction Dimensionless This parameter is the mean of the fraction of the grid box (0-1) that is covered by large-scale precipitation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean large-scale precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Precipitation can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean large-scale snowfall rate kg m-2 s-1 This parameter is the rate of snowfall (snowfall intensity) at the Earth's surface, which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Snowfall can also be generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. Since 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean magnitude of turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the magnitude of the mean stress on the Earth's surface, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean magnitude of turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the magnitude of the mean stress on the Earth's surface, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean northward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean northward gravity wave surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean northward turbulent surface stress N m-2 Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the mean surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean period of total swell s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea associated with swell, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. Mean period of total swell s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea associated with swell, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. It is the mean over all frequencies and directions of the total swell spectrum. Mean period of wind waves s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea generated by local winds, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea spectrum. Mean period of wind waves s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea generated by local winds, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. It is the mean over all frequencies and directions of the total wind-sea spectrum. Mean potential evaporation rate kg m-2 s-1 This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean potential evaporation rate kg m-2 s-1 This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean sea level pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of mean sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at mean sea level. It is calculated over all surfaces - land, sea and inland water. Maps of mean sea level pressure are used to identify the locations of low and high pressure weather systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds. The units of this parameter are pascals (Pa). Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Mean sea level pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of mean sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at mean sea level. It is calculated over all surfaces - land, sea and inland water. Maps of mean sea level pressure are used to identify the locations of low and high pressure weather systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds. The units of this parameter are pascals (Pa). Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa). Mean snow evaporation rate kg m-2 s-1 This parameter is the average rate of snow evaporation from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snow evaporation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Mean snow evaporation rate kg m-2 s-1 This parameter is the average rate of snow evaporation from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snow evaporation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Mean snowfall rate kg m-2 s-1 This parameter is the rate of snowfall at the Earth's surface. It is the sum of large-scale and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean snowfall rate kg m-2 s-1 This parameter is the rate of snowfall at the Earth's surface. It is the sum of large-scale and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the snowfall would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean snowmelt rate kg m-2 s-1 This parameter is the rate of snow melt in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the melting would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean snowmelt rate kg m-2 s-1 This parameter is the rate of snow melt in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the melting would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean square slope of waves Dimensionless This parameter can be related analytically to the average slope of combined wind-sea and swell waves. It can also be expressed as a function of wind speed under some statistical assumptions. The higher the slope, the steeper the waves. This parameter indicates the roughness of the sea/ocean surface which affects the interaction between ocean and atmosphere. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. Mean square slope of waves Dimensionless This parameter can be related analytically to the average slope of combined wind-sea and swell waves. It can also be expressed as a function of wind speed under some statistical assumptions. The higher the slope, the steeper the waves. This parameter indicates the roughness of the sea/ocean surface which affects the interaction between ocean and atmosphere. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. Mean sub-surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean sub-surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface direct short-wave radiation flux W m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux W m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux, clear sky W m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface direct short-wave radiation flux, clear sky W m-2 This parameter is the amount of direct radiation from the Sun (also known as solar or shortwave radiation) reaching the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward UV radiation flux W m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward UV radiation flux W m-2 This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane. UV radiation is part of the electromagnetic spectrum emitted by the Sun that has wavelengths shorter than visible light. In the ECMWF Integrated Forecasting system (IFS) it is defined as radiation with a wavelength of 0.20-0.44 µm (microns, 1 millionth of a metre). Small amounts of UV are essential for living organisms, but overexposure may result in cell damage; in humans this includes acute and chronic health effects on the skin, eyes and immune system. UV radiation is absorbed by the ozone layer, but some reaches the surface. The depletion of the ozone layer is causing concern over an increase in the damaging effects of UV. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux, clear sky W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward long-wave radiation flux, clear sky W m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface downward short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface latent heat flux W m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface latent heat flux W m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux, clear sky W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net long-wave radiation flux, clear sky W m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux W m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface net short-wave radiation flux, clear sky W m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface runoff rate kg m-2 s-1 Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the runoff would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Mean surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean surface sensible heat flux W m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top downward short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top downward short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux W m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux W m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net long-wave radiation flux, clear sky W m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean top net long-wave radiation flux, clear sky W m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. Mean top net short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux, clear sky W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean top net short-wave radiation flux, clear sky W m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. The ECMWF convention for vertical fluxes is positive downwards. Mean total precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface. It is the sum of the rates due to large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean total precipitation rate kg m-2 s-1 This parameter is the rate of precipitation at the Earth's surface. It is the sum of the rates due to large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. It is the rate the precipitation would have if it were spread evenly over the grid box. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Mean vertical gradient of refractivity inside trapping layer m-1 Mean vertical gradient of atmospheric refractivity inside the trapping layer. Mean vertical gradient of refractivity inside trapping layer m-1 Mean vertical gradient of atmospheric refractivity inside the trapping layer. Mean vertically integrated moisture divergence kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean vertically integrated moisture divergence kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is a mean over a particular time period (the processing period) which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the processing period is the complete, whole month. For the monthly averaged reanalysis by hour of day, the processing period is 1 hour for every day of the month and for the monthly averaged ensemble members by hour of day, the processing period is 3 hours for every day of the month. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Mean wave direction degree true This parameter is the mean direction of ocean/sea surface waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use this type of wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction degree true This parameter is the mean direction of ocean/sea surface waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use this type of wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of first swell partition degrees This parameter is the mean direction of waves in the first swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of first swell partition degrees This parameter is the mean direction of waves in the first swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of second swell partition degrees This parameter is the mean direction of waves in the second swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of second swell partition degrees This parameter is the mean direction of waves in the second swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of third swell partition degrees This parameter is the mean direction of waves in the third swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave direction of third swell partition degrees This parameter is the mean direction of waves in the third swell partition. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). The units are degrees true, which means the direction relative to the geographic location of the north pole. It is the direction that waves are coming from, so 0 degrees means "coming from the north" and 90 degrees means "coming from the east". Mean wave period s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use such wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. Mean wave period s This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. This parameter can be used to assess sea state and swell. For example, engineers use such wave information when designing structures in the open ocean, such as oil platforms, or in coastal applications. Mean wave period based on first moment s This parameter is the reciprocal of the mean frequency of the wave components that represent the sea state. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment s This parameter is the reciprocal of the mean frequency of the wave components that represent the sea state. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for swell s This parameter is the reciprocal of the mean frequency of the wave components associated with swell. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for swell s This parameter is the reciprocal of the mean frequency of the wave components associated with swell. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for wind waves s This parameter is the reciprocal of the mean frequency of the wave components generated by local winds. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with wind waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on first moment for wind waves s This parameter is the reciprocal of the mean frequency of the wave components generated by local winds. All wave components have been averaged proportionally to their respective amplitude. This parameter can be used to estimate the magnitude of Stokes drift transport in deep water associated with wind waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for swell s This parameter is equivalent to the zero-crossing mean wave period for swell. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for swell s This parameter is equivalent to the zero-crossing mean wave period for swell. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for wind waves s This parameter is equivalent to the zero-crossing mean wave period for waves generated by local winds. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period based on second moment for wind waves s This parameter is equivalent to the zero-crossing mean wave period for waves generated by local winds. The zero-crossing mean wave period represents the mean length of time between occasions where the sea/ocean surface crosses a defined zeroth level (such as mean sea level). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. Moments are statistical quantities derived from the two-dimensional wave spectrum. Mean wave period of first swell partition s This parameter is the mean period of waves in the first swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of first swell partition s This parameter is the mean period of waves in the first swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of second swell partition s This parameter is the mean period of waves in the second swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of second swell partition s This parameter is the mean period of waves in the second swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of third swell partition s This parameter is the mean period of waves in the third swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third swell partition might be from one system at one location and a different system at the neighbouring location). Mean wave period of third swell partition s This parameter is the mean period of waves in the third swell partition. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third swell partition might be from one system at one location and a different system at the neighbouring location). Mean zero-crossing wave period s This parameter represents the mean length of time between occasions where the sea/ocean surface crosses mean sea level. In combination with wave height information, it could be used to assess the length of time that a coastal structure might be under water, for example. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). In the ECMWF Integrated Forecasting System (IFS) this parameter is calculated from the characteristics of the two-dimensional wave spectrum. Mean zero-crossing wave period s This parameter represents the mean length of time between occasions where the sea/ocean surface crosses mean sea level. In combination with wave height information, it could be used to assess the length of time that a coastal structure might be under water, for example. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). In the ECMWF Integrated Forecasting System (IFS) this parameter is calculated from the characteristics of the two-dimensional wave spectrum. Medium cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a "standard atmosphere")). The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. Medium cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a "standard atmosphere")). The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels. Cloud fractions vary from 0 to 1. Minimum vertical gradient of refractivity inside trapping layer m-1 Minimum vertical gradient of atmospheric refractivity inside the trapping layer. Minimum vertical gradient of refractivity inside trapping layer m-1 Minimum vertical gradient of atmospheric refractivity inside the trapping layer. Model bathymetry m This parameter is the depth of water from the surface to the bottom of the ocean. It is used by the ocean wave model to specify the propagation properties of the different waves that could be present. Note that the ocean wave model grid is too coarse to resolve some small islands and mountains on the bottom of the ocean, but they can have an impact on surface ocean waves. The ocean wave model has been modified to reduce the wave energy flowing around or over features at spatial scales smaller than the grid box. Model bathymetry m This parameter is the depth of water from the surface to the bottom of the ocean. It is used by the ocean wave model to specify the propagation properties of the different waves that could be present. Note that the ocean wave model grid is too coarse to resolve some small islands and mountains on the bottom of the ocean, but they can have an impact on surface ocean waves. The ocean wave model has been modified to reduce the wave energy flowing around or over features at spatial scales smaller than the grid box. Near IR albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Near IR albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.7 and 4 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). Values of this parameter vary between 0 and 1. In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. Normalized energy flux into ocean Dimensionless This parameter is the normalised vertical flux of turbulent kinetic energy from ocean waves into the ocean. The energy flux is calculated from an estimation of the loss of wave energy due to white capping waves. A white capping wave is one that appears white at its crest as it breaks, due to air being mixed into the water. When waves break in this way, there is a transfer of energy from the waves to the ocean. Such a flux is defined to be negative. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into ocean Dimensionless This parameter is the normalised vertical flux of turbulent kinetic energy from ocean waves into the ocean. The energy flux is calculated from an estimation of the loss of wave energy due to white capping waves. A white capping wave is one that appears white at its crest as it breaks, due to air being mixed into the water. When waves break in this way, there is a transfer of energy from the waves to the ocean. Such a flux is defined to be negative. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into waves Dimensionless This parameter is the normalised vertical flux of energy from wind into the ocean waves. A positive flux implies a flux into the waves. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized energy flux into waves Dimensionless This parameter is the normalised vertical flux of energy from wind into the ocean waves. A positive flux implies a flux into the waves. The energy flux has units of Watts per metre squared, and this is normalised by being divided by the product of air density and the cube of the friction velocity. Normalized stress into ocean Dimensionless This parameter is the normalised surface stress, or momentum flux, from the air into the ocean due to turbulence at the air-sea interface and breaking waves. It does not include the flux used to generate waves. The ECMWF convention for vertical fluxes is positive downwards. The stress has units of Newtons per metre squared, and this is normalised by being divided by the product of air density and the square of the friction velocity. Normalized stress into ocean Dimensionless This parameter is the normalised surface stress, or momentum flux, from the air into the ocean due to turbulence at the air-sea interface and breaking waves. It does not include the flux used to generate waves. The ECMWF convention for vertical fluxes is positive downwards. The stress has units of Newtons per metre squared, and this is normalised by being divided by the product of air density and the square of the friction velocity. Northward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Northward gravity wave surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with low-level, orographic blocking and orographic gravity waves. It is calculated by the ECMWF Integrated Forecasting System's sub-grid orography scheme, which represents stress due to unresolved valleys, hills and mountains with horizontal scales between 5 km and the model grid-scale. (The stress associated with orographic features with horizontal scales smaller than 5 km is accounted for by the turbulent orographic form drag scheme). Orographic gravity waves are oscillations in the flow maintained by the buoyancy of displaced air parcels, produced when air is deflected upwards by hills and mountains. This process can create stress on the atmosphere at the Earth's surface and at other levels in the atmosphere. Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Northward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Northward turbulent surface stress N m-2 s Air flowing over a surface exerts a stress (drag) that transfers momentum to the surface and slows the wind. This parameter is the component of the accumulated surface stress in a northward direction, associated with turbulent eddies near the surface and turbulent orographic form drag. It is calculated by the ECMWF Integrated Forecasting System's turbulent diffusion and turbulent orographic form drag schemes. The turbulent eddies near the surface are related to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km, which are specified from land surface data at about 1 km resolution. (The stress associated with orographic features with horizontal scales between 5 km and the model grid-scale is accounted for by the sub-grid orographic scheme.) Positive (negative) values indicate stress on the surface of the Earth in a northward (southward) direction. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Ocean surface stress equivalent 10m neutral wind direction degrees This parameter is the direction from which the "neutral wind" blows, in degrees clockwise from true north, at a height of ten metres above the surface of the Earth. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind direction used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind direction degrees This parameter is the direction from which the "neutral wind" blows, in degrees clockwise from true north, at a height of ten metres above the surface of the Earth. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind direction used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind speed m s-1 This parameter is the horizontal speed of the "neutral wind", at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind speed used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Ocean surface stress equivalent 10m neutral wind speed m s-1 This parameter is the horizontal speed of the "neutral wind", at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second. The neutral wind is calculated from the surface stress and roughness length by assuming that the air is neutrally stratified. The neutral wind is, by definition, in the direction of the surface stress. The size of the roughness length depends on the sea state. This parameter is the wind speed used to force the wave model, therefore it is only calculated over water bodies represented in the ocean wave model. It is interpolated from the atmospheric model's horizontal grid onto the horizontal grid used by the ocean wave model. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Period corresponding to maximum individual wave height s This parameter is the period of the expected highest individual wave within a 20-minute time window. It can be used as a guide to the characteristics of extreme or freak waves. Wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. Occasionally waves of different periods reinforce and interact non-linearly giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered to be a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Period corresponding to maximum individual wave height s This parameter is the period of the expected highest individual wave within a 20-minute time window. It can be used as a guide to the characteristics of extreme or freak waves. Wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. Occasionally waves of different periods reinforce and interact non-linearly giving a wave height considerably larger than the significant wave height. If the maximum individual wave height is more than twice the significant wave height, then the wave is considered to be a freak wave. The significant wave height represents the average height of the highest third of surface ocean/sea waves, generated by local winds and associated with swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is derived statistically from the two-dimensional wave spectrum. The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. Potential evaporation m This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Potential evaporation m This parameter is a measure of the extent to which near-surface atmospheric conditions are conducive to the process of evaporation. It is usually considered to be the amount of evaporation, under existing atmospheric conditions, from a surface of pure water which has the temperature of the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation. Potential evaporation in the current ECMWF Integrated Forecasting System (IFS) is based on surface energy balance calculations with the vegetation parameters set to "crops/mixed farming" and assuming "no stress from soil moisture". In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although potential evaporation is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Precipitation type Dimensionless This parameter describes the type of precipitation at the surface, at the specified time. A precipitation type is assigned wherever there is a non-zero value of precipitation. In the ECMWF Integrated Forecasting System (IFS) there are only two predicted precipitation variables: rain and snow. Precipitation type is derived from these two predicted variables in combination with atmospheric conditions, such as temperature. Values of precipitation type defined in the IFS: 0: No precipitation, 1: Rain, 3: Freezing rain (i.e. supercooled raindrops which freeze on contact with the ground and other surfaces), 5: Snow, 6: Wet snow (i.e. snow particles which are starting to melt), 7: Mixture of rain and snow, 8: Ice pellets. These precipitation types are consistent with WMO Code Table 4.201. Other types in this WMO table are not defined in the IFS. The monthly mean procedure applied to such integers, will yield non-integer values. Precipitation type Dimensionless This parameter describes the type of precipitation at the surface, at the specified time. A precipitation type is assigned wherever there is a non-zero value of precipitation. In the ECMWF Integrated Forecasting System (IFS) there are only two predicted precipitation variables: rain and snow. Precipitation type is derived from these two predicted variables in combination with atmospheric conditions, such as temperature. Values of precipitation type defined in the IFS: 0: No precipitation, 1: Rain, 3: Freezing rain (i.e. supercooled raindrops which freeze on contact with the ground and other surfaces), 5: Snow, 6: Wet snow (i.e. snow particles which are starting to melt), 7: Mixture of rain and snow, 8: Ice pellets. These precipitation types are consistent with WMO Code Table 4.201. Other types in this WMO table are not defined in the IFS. The monthly mean procedure applied to such integers, will yield non-integer values. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Sea surface temperature K This parameter (SST) is the temperature of sea water near the surface. In ERA5, this parameter is a foundation SST, which means there are no variations due to the daily cycle of the sun (diurnal variations). SST, in ERA5, is given by two external providers. Before September 2007, SST from the HadISST2 dataset is used and from September 2007 onwards, the OSTIA dataset is used. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Sea surface temperature K This parameter (SST) is the temperature of sea water near the surface. In ERA5, this parameter is a foundation SST, which means there are no variations due to the daily cycle of the sun (diurnal variations). SST, in ERA5, is given by two external providers. Before September 2007, SST from the HadISST2 dataset is used and from September 2007 onwards, the OSTIA dataset is used. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Sea-ice cover Dimensionless This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land-sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover. In ERA5, sea-ice cover is given by two external providers. Before 1979 the HadISST2 dataset is used. From 1979 to August 2007 the OSI SAF (409a) dataset is used and from September 2007 the OSI SAF oper dataset is used. Sea ice is frozen sea water which floats on the surface of the ocean. Sea ice does not include ice which forms on land such as glaciers, icebergs and ice-sheets. It also excludes ice shelves which are anchored on land, but protrude out over the surface of the ocean. These phenomena are not modelled by the IFS. Long-term monitoring of sea ice is important for understanding climate change. Sea ice also affects shipping routes through the polar regions. Sea-ice cover Dimensionless This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land-sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover. In ERA5, sea-ice cover is given by two external providers. Before 1979 the HadISST2 dataset is used. From 1979 to August 2007 the OSI SAF (409a) dataset is used and from September 2007 the OSI SAF oper dataset is used. Sea ice is frozen sea water which floats on the surface of the ocean. Sea ice does not include ice which forms on land such as glaciers, icebergs and ice-sheets. It also excludes ice shelves which are anchored on land, but protrude out over the surface of the ocean. These phenomena are not modelled by the IFS. Long-term monitoring of sea ice is important for understanding climate change. Sea ice also affects shipping routes through the polar regions. Significant height of combined wind waves and swell m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. This parameter can be used to assess sea state and swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of combined wind waves and swell m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. This parameter can be used to assess sea state and swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of total swell m This parameter represents the average height of the highest third of surface ocean/sea waves associated with swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of total swell only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional total swell spectrum. The total swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of total swell m This parameter represents the average height of the highest third of surface ocean/sea waves associated with swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of total swell only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional total swell spectrum. The total swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of wind waves m This parameter represents the average height of the highest third of surface ocean/sea waves generated by the local wind. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wind-sea wave spectrum. The wind-sea wave spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are still under the influence of the local wind. This parameter can be used to assess wind-sea waves. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant height of wind waves m This parameter represents the average height of the highest third of surface ocean/sea waves generated by the local wind. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wind-sea wave spectrum. The wind-sea wave spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are still under the influence of the local wind. This parameter can be used to assess wind-sea waves. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of first swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the first swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of first swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the first swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the first might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of second swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the second swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of second swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the second swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the second might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of third swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the third swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Significant wave height of third swell partition m This parameter represents the average height of the highest third of surface ocean/sea waves associated with the third swell partition. Wave height represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. In many situations, swell can be made up of different swell systems, for example, from two distant and separate storms. To account for this, the swell spectrum is partitioned into up to three parts. The swell partitions are labelled first, second and third based on their respective wave height. Therefore, there is no guarantee of spatial coherence (the third might be from one system at one location and another system at the neighbouring location). More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the first swell partition of the two-dimensional swell spectrum. The swell spectrum is obtained by only considering the components of the two-dimensional wave spectrum that are not under the influence of the local wind. This parameter can be used to assess swell. For example, engineers use significant wave height to calculate the load on structures in the open ocean, such as oil platforms, or in coastal applications. Skin reservoir content m of water equivalent This parameter is the amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of "skin reservoir content" a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the "skin reservoir" by evaporation. Skin reservoir content m of water equivalent This parameter is the amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of "skin reservoir content" a grid box can hold depends on the type of vegetation, and may be zero. Water leaves the "skin reservoir" by evaporation. Skin temperature K This parameter is the temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Skin temperature K This parameter is the temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Slope of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, angle and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the slope of the sub-grid valleys, hills and mountains. A flat surface has a value of 0, and a 45 degree slope has a value of 0.5. This parameter does not vary in time. Slope of sub-gridscale orography Dimensionless This parameter is one of four parameters (the others being standard deviation, angle and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the slope of the sub-grid valleys, hills and mountains. A flat surface has a value of 0, and a 45 degree slope has a value of 0.5. This parameter does not vary in time. Snow albedo Dimensionless This parameter is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter changes with snow age and also depends on vegetation height. It has a range of values between 0 and 1. For low vegetation, it ranges between 0.52 for old snow and 0.88 for fresh snow. For high vegetation with snow underneath, it depends on vegetation type and has values between 0.27 and 0.38. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean snow albedo, grid point value would be contaminated with fictitious zero snow values. Snow albedo Dimensionless This parameter is a measure of the reflectivity of the snow-covered part of the grid box. It is the fraction of solar (shortwave) radiation reflected by snow across the solar spectrum. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter changes with snow age and also depends on vegetation height. It has a range of values between 0 and 1. For low vegetation, it ranges between 0.52 for old snow and 0.88 for fresh snow. For high vegetation with snow underneath, it depends on vegetation type and has values between 0.27 and 0.38. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean snow albedo, grid point value would be contaminated with fictitious zero snow values. Snow density kg m-3 This parameter is the mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean snow density, grid point value would be contaminated with fictitious zero snow values. Snow density kg m-3 This parameter is the mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean snow density, grid point value would be contaminated with fictitious zero snow values. Snow depth m of water equivalent This parameter is the amount of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow depth m of water equivalent This parameter is the amount of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. Snow evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from snow from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the evaporated snow (from the snow-covered area of a grid box) were liquid and were spread evenly over the whole grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Snow evaporation m of water equivalent This parameter is the accumulated amount of water that has evaporated from snow from the snow-covered area of a grid box into vapour in the air above. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the evaporated snow (from the snow-covered area of a grid box) were liquid and were spread evenly over the whole grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The IFS convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate deposition. Snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Snowfall m of water equivalent This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly at spatial scales of the grid box or larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. In the IFS, precipitation is comprised of rain and snow. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Snowmelt m of water equivalent This parameter is the accumulated amount of water that has melted from snow in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the melted snow (from the snow-covered area of a grid box) were spread evenly over the whole grid box. For example, if half the grid box were covered in snow with a water equivalent depth of 0.02m, this parameter would have a value of 0.01m. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Snowmelt m of water equivalent This parameter is the accumulated amount of water that has melted from snow in the snow-covered area of a grid box. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is the depth of water there would be if the melted snow (from the snow-covered area of a grid box) were spread evenly over the whole grid box. For example, if half the grid box were covered in snow with a water equivalent depth of 0.02m, this parameter would have a value of 0.01m. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. Soil temperature level 1 K This parameter is the temperature of the soil at level 1 (in the middle of layer 1). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 1 K This parameter is the temperature of the soil at level 1 (in the middle of layer 1). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K This parameter is the temperature of the soil at level 2 (in the middle of layer 2). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 2 K This parameter is the temperature of the soil at level 2 (in the middle of layer 2). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 3 K This parameter is the temperature of the soil at level 3 (in the middle of layer 3). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 3 K This parameter is the temperature of the soil at level 3 (in the middle of layer 3). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 4 K This parameter is the temperature of the soil at level 4 (in the middle of layer 4). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil temperature level 4 K This parameter is the temperature of the soil at level 4 (in the middle of layer 4). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil, where the surface is at 0cm: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Soil temperature is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Soil type Dimensionless This parameter is the texture (or classification) of soil used by the land surface scheme of the ECMWF Integrated Forecasting System (IFS) to predict the water holding capacity of soil in soil moisture and runoff calculations. It is derived from the root zone data (30-100 cm below the surface) of the FAO/UNESCO Digital Soil Map of the World, DSMW (FAO, 2003), which exists at a resolution of 5' X 5' (about 10 km). The seven soil types are: 1: Coarse, 2: Medium, 3: Medium fine, 4: Fine, 5: Very fine, 6: Organic, 7: Tropical organic. A value of 0 indicates a non-land point. This parameter does not vary in time. Soil type Dimensionless This parameter is the texture (or classification) of soil used by the land surface scheme of the ECMWF Integrated Forecasting System (IFS) to predict the water holding capacity of soil in soil moisture and runoff calculations. It is derived from the root zone data (30-100 cm below the surface) of the FAO/UNESCO Digital Soil Map of the World, DSMW (FAO, 2003), which exists at a resolution of 5' X 5' (about 10 km). The seven soil types are: 1: Coarse, 2: Medium, 3: Medium fine, 4: Fine, 5: Very fine, 6: Organic, 7: Tropical organic. A value of 0 indicates a non-land point. This parameter does not vary in time. Standard deviation of filtered subgrid orography m Climatological parameter (scales between approximately 3 and 22 km are included). This parameter does not vary in time. Standard deviation of filtered subgrid orography m Climatological parameter (scales between approximately 3 and 22 km are included). This parameter does not vary in time. Standard deviation of orography Dimensionless This parameter is one of four parameters (the others being angle of sub-gridscale orography, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the standard deviation of the height of the sub-grid valleys, hills and mountains within a grid box. This parameter does not vary in time. Standard deviation of orography Dimensionless This parameter is one of four parameters (the others being angle of sub-gridscale orography, slope and anisotropy) that describe the features of the orography that are too small to be resolved by the model grid. These four parameters are calculated for orographic features with horizontal scales comprised between 5 km and the model grid resolution, being derived from the height of valleys, hills and mountains at about 1 km resolution. They are used as input for the sub-grid orography scheme which represents low-level blocking and orographic gravity wave effects. This parameter represents the standard deviation of the height of the sub-grid valleys, hills and mountains within a grid box. This parameter does not vary in time. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Sub-surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface latent heat flux J m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface latent heat flux J m-2 This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation, clear sky J m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net solar radiation, clear sky J m-2 This parameter is the amount of solar (shortwave) radiation reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo), assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It is the amount of radiation passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation, clear sky J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface net thermal radiation, clear sky J m-2 Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of land, sea and inland water. It is a measure of the weight of all the air in a column vertically above a point on the Earth's surface. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure weather systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa). Surface pressure Pa This parameter is the pressure (force per unit area) of the atmosphere at the surface of land, sea and inland water. It is a measure of the weight of all the air in a column vertically above a point on the Earth's surface. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure weather systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa). Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface runoff m Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is called runoff. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of runoff are depth in metres of water. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid box. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. Surface sensible heat flux J m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface sensible heat flux J m-2 This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downward, clear sky J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downward, clear sky J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface solar radiation downwards J m-2 This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter). To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downward, clear sky J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downward, clear sky J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere that reaches a horizontal plane at the surface of the Earth, assuming clear-sky (cloudless) conditions. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the corresponding total-sky quantities (clouds included), but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Surface thermal radiation downwards J m-2 This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. TOA incident solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. TOA incident solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation), received from the Sun, at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Temperature of snow layer K This parameter gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean temperature of snow layer, grid point value would be contaminated with fictitious zero snow values. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Temperature of snow layer K This parameter gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecasting System (IFS) represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box. This parameter is defined over the whole globe, even where there is no snow. Regions without snow can be masked out by only considering grid points where the monthly mean snow depth (m of water equivalent) is greater than 0.0. Grid points with relatively low values of monthly mean snow depth might include periods during the month when the snow depth is 0.0, in which case the corresponding monthly mean temperature of snow layer, grid point value would be contaminated with fictitious zero snow values. This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Top net solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation, clear sky J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net solar radiation, clear sky J m-2 This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface, assuming clear-sky (cloudless) conditions. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as the total-sky (clouds included) quantities, but assuming that the clouds are not there. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation J m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation J m-2 The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Top net thermal radiation, clear sky J m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). Note that OLR is typically shown in units of watts per square metre (W m-2 ). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. Top net thermal radiation, clear sky J m-2 This parameter is the thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere, assuming clear-sky (cloudless) conditions. It is the amount passing through a horizontal plane. Note that the ECMWF convention for vertical fluxes is positive downwards, so a flux from the atmosphere to space will be negative. Clear-sky radiation quantities are computed for exactly the same atmospheric conditions of temperature, humidity, ozone, trace gases and aerosol as total-sky quantities (clouds included), but assuming that the clouds are not there. The thermal radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR) (i.e., taking a flux from the atmosphere to space as positive). Note that OLR is typically shown in units of watts per square metre (W m-2 ). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. Total cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights. Cloud fractions vary from 0 to 1. Total cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights. Cloud fractions vary from 0 to 1. Total column cloud ice water kg m-2 This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud ice water kg m-2 This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud liquid water kg m-2 This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column cloud liquid water kg m-2 This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can also be referred to as total ozone, or vertically integrated ozone. The values are dominated by ozone within the stratosphere. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. In the IFS, the units for total ozone are kilograms per square metre, but before 12/06/2001 dobson units were used. Dobson units (DU) are still used extensively for total column ozone. 1 DU = 2.1415E-5 kg m-2 Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can also be referred to as total ozone, or vertically integrated ozone. The values are dominated by ozone within the stratosphere. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. In the IFS, the units for total ozone are kilograms per square metre, but before 12/06/2001 dobson units were used. Dobson units (DU) are still used extensively for total column ozone. 1 DU = 2.1415E-5 kg m-2 Total column rain water kg m-2 This parameter is the total amount of water in droplets of raindrop size (which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column rain water kg m-2 This parameter is the total amount of water in droplets of raindrop size (which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column snow water kg m-2 This parameter is the total amount of water in the form of snow (aggregated ice crystals which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column snow water kg m-2 This parameter is the total amount of water in the form of snow (aggregated ice crystals which can fall to the surface as precipitation) in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column supercooled liquid water kg m-2 This parameter is the total amount of supercooled water in a column extending from the surface of the Earth to the top of the atmosphere. Supercooled water is water that exists in liquid form below 0oC. It is common in cold clouds and is important in the formation of precipitation. Also, supercooled water in clouds extending to the surface (i.e., fog) can cause icing/riming of various structures. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column supercooled liquid water kg m-2 This parameter is the total amount of supercooled water in a column extending from the surface of the Earth to the top of the atmosphere. Supercooled water is water that exists in liquid form below 0oC. It is common in cold clouds and is important in the formation of precipitation. Also, supercooled water in clouds extending to the surface (i.e., fog) can cause icing/riming of various structures. This parameter represents the area averaged value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, conversion and aggregation are also highly simplified in the IFS. Total column water kg m-2 This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for. Total column water kg m-2 This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for. Total column water vapour kg m-2 This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Total column water vapour kg m-2 This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere. This parameter represents the area averaged value for a grid box. Total precipitation m This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Total precipitation m This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box. Total sky direct solar radiation at surface J m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Total sky direct solar radiation at surface J m-2 This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane. Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. The units are joules per square metre (J m-2 ). To convert to watts per square metre (W m-2 ), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards. Total totals index K This parameter gives an indication of the probability of occurrence of a thunderstorm and its severity by using the vertical gradient of temperature and humidity. The values of this index indicate the following: <44 K: Thunderstorms not likely, 44-50 K: Thunderstorms likely, 51-52 K: Isolated severe thunderstorms, 53-56 K: Widely scattered severe thunderstorms, 56-60 K: Scattered severe thunderstorms more likely. The total totals index is the temperature difference between 850 hPa (near surface) and 500 hPa (mid-troposphere) (lapse rate) plus a measure of the moisture content between 850 hPa and 500 hPa. The probability of deep convection tends to increase with increasing lapse rate and atmospheric moisture content. There are a number of limitations to this index. Also, the interpretation of the index value varies with season and location. Total totals index K This parameter gives an indication of the probability of occurrence of a thunderstorm and its severity by using the vertical gradient of temperature and humidity. The values of this index indicate the following: <44 K: Thunderstorms not likely, 44-50 K: Thunderstorms likely, 51-52 K: Isolated severe thunderstorms, 53-56 K: Widely scattered severe thunderstorms, 56-60 K: Scattered severe thunderstorms more likely. The total totals index is the temperature difference between 850 hPa (near surface) and 500 hPa (mid-troposphere) (lapse rate) plus a measure of the moisture content between 850 hPa and 500 hPa. The probability of deep convection tends to increase with increasing lapse rate and atmospheric moisture content. There are a number of limitations to this index. Also, the interpretation of the index value varies with season and location. Trapping layer base height m Trapping layer base height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer base height m Trapping layer base height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer top height m Trapping layer top height as diagnosed from the vertical gradient of atmospheric refractivity. Trapping layer top height m Trapping layer top height as diagnosed from the vertical gradient of atmospheric refractivity. Type of high vegetation Dimensionless This parameter indicates the 6 types of high vegetation recognised by the ECMWF Integrated Forecasting System: 3 = Evergreen needleleaf trees, 4 = Deciduous needleleaf trees, 5 = Deciduous broadleaf trees, 6 = Evergreen broadleaf trees, 18 = Mixed forest/woodland, 19 = Interrupted forest. A value of 0 indicates a point without high vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of high vegetation Dimensionless This parameter indicates the 6 types of high vegetation recognised by the ECMWF Integrated Forecasting System: 3 = Evergreen needleleaf trees, 4 = Deciduous needleleaf trees, 5 = Deciduous broadleaf trees, 6 = Evergreen broadleaf trees, 18 = Mixed forest/woodland, 19 = Interrupted forest. A value of 0 indicates a point without high vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of low vegetation Dimensionless This parameter indicates the 10 types of low vegetation recognised by the ECMWF Integrated Forecasting System: 1 = Crops, Mixed farming, 2 = Grass, 7 = Tall grass, 9 = Tundra, 10 = Irrigated crops, 11 = Semidesert, 13 = Bogs and marshes, 16 = Evergreen shrubs, 17 = Deciduous shrubs, 20 = Water and land mixtures. A value of 0 indicates a point without low vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. Type of low vegetation Dimensionless This parameter indicates the 10 types of low vegetation recognised by the ECMWF Integrated Forecasting System: 1 = Crops, Mixed farming, 2 = Grass, 7 = Tall grass, 9 = Tundra, 10 = Irrigated crops, 11 = Semidesert, 13 = Bogs and marshes, 16 = Evergreen shrubs, 17 = Deciduous shrubs, 20 = Water and land mixtures. A value of 0 indicates a point without low vegetation, including an oceanic or inland water location. Vegetation types are used to calculate the surface energy balance and snow albedo. This parameter does not vary in time. U-component stokes drift m s-1 This parameter is the eastward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. U-component stokes drift m s-1 This parameter is the eastward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. UV visible albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. This parameter varies between 0 and 1. UV visible albedo for diffuse radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of diffuse solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. This parameter varies between 0 and 1. UV visible albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. UV visible albedo for direct radiation Dimensionless Albedo is a measure of the reflectivity of the Earth's surface. This parameter is the fraction of direct solar (shortwave) radiation with wavelengths between 0.3 and 0.7 µm (microns, 1 millionth of a metre) reflected by the Earth's surface (for snow-free land surfaces only). In the ECMWF Integrated Forecasting System (IFS) albedo is dealt with separately for solar radiation with wavelengths greater/less than 0.7µm and for direct and diffuse solar radiation (giving 4 components to albedo). Solar radiation at the surface can be direct or diffuse. Solar radiation can be scattered in all directions by particles in the atmosphere, some of which reaches the surface (diffuse solar radiation). Some solar radiation reaches the surface without being scattered (direct solar radiation). In the IFS, a climatological (observed values averaged over a period of several years) background albedo is used which varies from month to month through the year, modified by the model over water, ice and snow. V-component stokes drift m s-1 This parameter is the northward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. V-component stokes drift m s-1 This parameter is the northward component of the surface Stokes drift. The Stokes drift is the net drift velocity due to surface wind waves. It is confined to the upper few metres of the ocean water column, with the largest value at the surface. For example, a fluid particle near the surface will slowly move in the direction of wave propagation. Vertical integral of divergence of cloud frozen water flux kg m-2 s-1 The vertical integral of the cloud frozen water flux is the horizontal rate of flow of cloud frozen water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud frozen water spreading outward from a point, per square metre. This parameter is positive for cloud frozen water that is spreading out, or diverging, and negative for the opposite, for cloud frozen water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud frozen water. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of divergence of cloud frozen water flux kg m-2 s-1 The vertical integral of the cloud frozen water flux is the horizontal rate of flow of cloud frozen water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud frozen water spreading outward from a point, per square metre. This parameter is positive for cloud frozen water that is spreading out, or diverging, and negative for the opposite, for cloud frozen water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud frozen water. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of divergence of cloud liquid water flux kg m-2 s-1 The vertical integral of the cloud liquid water flux is the horizontal rate of flow of cloud liquid water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud liquid water spreading outward from a point, per square metre. This parameter is positive for cloud liquid water that is spreading out, or diverging, and negative for the opposite, for cloud liquid water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud liquid water. Vertical integral of divergence of cloud liquid water flux kg m-2 s-1 The vertical integral of the cloud liquid water flux is the horizontal rate of flow of cloud liquid water, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of cloud liquid water spreading outward from a point, per square metre. This parameter is positive for cloud liquid water that is spreading out, or diverging, and negative for the opposite, for cloud liquid water that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of cloud liquid water. Vertical integral of divergence of geopotential flux W m-2 The vertical integral of the geopotential flux is the horizontal rate of flow of geopotential, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of geopotential spreading outward from a point, per square metre. This parameter is positive for geopotential that is spreading out, or diverging, and negative for the opposite, for geopotential that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of geopotential. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of geopotential flux W m-2 The vertical integral of the geopotential flux is the horizontal rate of flow of geopotential, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of geopotential spreading outward from a point, per square metre. This parameter is positive for geopotential that is spreading out, or diverging, and negative for the opposite, for geopotential that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of geopotential. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of kinetic energy flux W m-2 The vertical integral of the kinetic energy flux is the horizontal rate of flow of kinetic energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of kinetic energy spreading outward from a point, per square metre. This parameter is positive for kinetic energy that is spreading out, or diverging, and negative for the opposite, for kinetic energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of kinetic energy. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of kinetic energy flux W m-2 The vertical integral of the kinetic energy flux is the horizontal rate of flow of kinetic energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of kinetic energy spreading outward from a point, per square metre. This parameter is positive for kinetic energy that is spreading out, or diverging, and negative for the opposite, for kinetic energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of kinetic energy. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of mass flux kg m-2 s-1 The vertical integral of the mass flux is the horizontal rate of flow of mass, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of mass spreading outward from a point, per square metre. This parameter is positive for mass that is spreading out, or diverging, and negative for the opposite, for mass that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of mass. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of divergence of mass flux kg m-2 s-1 The vertical integral of the mass flux is the horizontal rate of flow of mass, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of mass spreading outward from a point, per square metre. This parameter is positive for mass that is spreading out, or diverging, and negative for the opposite, for mass that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of mass. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of divergence of moisture flux kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Vertical integral of divergence of moisture flux kg m-2 s-1 The vertical integral of the moisture flux is the horizontal rate of flow of moisture, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm (of liquid water) per second. Vertical integral of divergence of ozone flux kg m-2 s-1 The vertical integral of the ozone flux is the horizontal rate of flow of ozone, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of ozone spreading outward from a point, per square metre. This parameter is positive for ozone that is spreading out, or diverging, and negative for the opposite, for ozone that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of ozone. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of divergence of ozone flux kg m-2 s-1 The vertical integral of the ozone flux is the horizontal rate of flow of ozone, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of ozone spreading outward from a point, per square metre. This parameter is positive for ozone that is spreading out, or diverging, and negative for the opposite, for ozone that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of ozone. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of divergence of thermal energy flux W m-2 The vertical integral of the thermal energy flux is the horizontal rate of flow of thermal energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of thermal energy spreading outward from a point, per square metre. This parameter is positive for thermal energy that is spreading out, or diverging, and negative for the opposite, for thermal energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of thermal energy. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the flow of thermal energy through the climate system and to investigate the atmospheric energy budget. Vertical integral of divergence of thermal energy flux W m-2 The vertical integral of the thermal energy flux is the horizontal rate of flow of thermal energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of thermal energy spreading outward from a point, per square metre. This parameter is positive for thermal energy that is spreading out, or diverging, and negative for the opposite, for thermal energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of thermal energy. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the flow of thermal energy through the climate system and to investigate the atmospheric energy budget. Vertical integral of divergence of total energy flux W m-2 The vertical integral of the total energy flux is the horizontal rate of flow of total energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of total energy spreading outward from a point, per square metre. This parameter is positive for total energy that is spreading out, or diverging, and negative for the opposite, for total energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of total energy. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of divergence of total energy flux W m-2 The vertical integral of the total energy flux is the horizontal rate of flow of total energy, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of total energy spreading outward from a point, per square metre. This parameter is positive for total energy that is spreading out, or diverging, and negative for the opposite, for total energy that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of total energy. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of eastward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of eastward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the eastward direction, per meter across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the eastward direction, per meter across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of eastward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of eastward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from west to east. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of eastward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from west to east. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of eastward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of eastward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of eastward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the eastward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from west to east. Vertical integral of energy conversion W m-2 This parameter is one contribution to the amount of energy being converted between kinetic energy, and internal plus potential energy, for a column of air extending from the surface of the Earth to the top of the atmosphere. Negative values indicate a conversion to kinetic energy from potential plus internal energy. This parameter can be used to study the atmospheric energy budget. The circulation of the atmosphere can also be considered in terms of energy conversions. Vertical integral of energy conversion W m-2 This parameter is one contribution to the amount of energy being converted between kinetic energy, and internal plus potential energy, for a column of air extending from the surface of the Earth to the top of the atmosphere. Negative values indicate a conversion to kinetic energy from potential plus internal energy. This parameter can be used to study the atmospheric energy budget. The circulation of the atmosphere can also be considered in terms of energy conversions. Vertical integral of kinetic energy J m-2 This parameter is the vertical integral of kinetic energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of kinetic energy J m-2 This parameter is the vertical integral of kinetic energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of mass of atmosphere kg m-2 This parameter is the total mass of air for a column extending from the surface of the Earth to the top of the atmosphere, per square metre. This parameter is calculated by dividing surface pressure by the Earth's gravitational acceleration, g (=9.80665 m s-2 ), and has units of kilograms per square metre. This parameter can be used to study the atmospheric mass budget. Vertical integral of mass of atmosphere kg m-2 This parameter is the total mass of air for a column extending from the surface of the Earth to the top of the atmosphere, per square metre. This parameter is calculated by dividing surface pressure by the Earth's gravitational acceleration, g (=9.80665 m s-2 ), and has units of kilograms per square metre. This parameter can be used to study the atmospheric mass budget. Vertical integral of mass tendency kg m-2 s-1 This parameter is the rate of change of the mass of a column of air extending from the Earth's surface to the top of the atmosphere. An increasing mass of the column indicates rising surface pressure. In contrast, a decrease indicates a falling surface pressure. The mass of the column is calculated by dividing pressure at the Earth's surface by the gravitational acceleration, g (=9.80665 m s-2 ). This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of mass tendency kg m-2 s-1 This parameter is the rate of change of the mass of a column of air extending from the Earth's surface to the top of the atmosphere. An increasing mass of the column indicates rising surface pressure. In contrast, a decrease indicates a falling surface pressure. The mass of the column is calculated by dividing pressure at the Earth's surface by the gravitational acceleration, g (=9.80665 m s-2 ). This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of northward cloud frozen water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud frozen water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Note that "cloud frozen water" is the same as "cloud ice water". Vertical integral of northward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward cloud liquid water flux kg m-1 s-1 This parameter is the horizontal rate of flow of cloud liquid water, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward geopotential flux W m-1 This parameter is the horizontal rate of flow of geopotential in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward heat flux W m-1 This parameter is the horizontal rate of flow of heat in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Heat (or thermal energy) is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward kinetic energy flux W m-1 This parameter is the horizontal rate of flow of kinetic energy, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Atmospheric kinetic energy is the energy of the atmosphere due to its motion. Only horizontal motion is considered in the calculation of this parameter. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward mass flux kg m-1 s-1 This parameter is the horizontal rate of flow of mass, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. This parameter can be used to study the atmospheric mass and energy budgets. Vertical integral of northward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from south to north. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of northward ozone flux kg m-1 s-1 This parameter is the horizontal rate of flow of ozone in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values denote a flux from south to north. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including a representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Vertical integral of northward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward total energy flux W m-1 This parameter is the horizontal rate of flow of total energy in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of northward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of northward water vapour flux kg m-1 s-1 This parameter is the horizontal rate of flow of water vapour, in the northward direction, per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Positive values indicate a flux from south to north. Vertical integral of potential and internal energy J m-2 This parameter is the mass weighted vertical integral of potential and internal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential and internal energy J m-2 This parameter is the mass weighted vertical integral of potential and internal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential, internal and latent energy J m-2 This parameter is the mass weighted vertical integral of potential, internal and latent energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The latent energy refers to the energy associated with the water vapour in the atmosphere and is equal to the energy required to convert liquid water into water vapour. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of potential, internal and latent energy J m-2 This parameter is the mass weighted vertical integral of potential, internal and latent energy for a column of air extending from the surface of the Earth to the top of the atmosphere. The potential energy of an air parcel is the amount of work that would have to be done, against the force of gravity, to lift the air to that location from mean sea level. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The latent energy refers to the energy associated with the water vapour in the atmosphere and is equal to the energy required to convert liquid water into water vapour. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of temperature K kg m-2 This parameter is the mass-weighted vertical integral of temperature for a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can be used to study the atmospheric energy budget. Vertical integral of temperature K kg m-2 This parameter is the mass-weighted vertical integral of temperature for a column of air extending from the surface of the Earth to the top of the atmosphere. This parameter can be used to study the atmospheric energy budget. Vertical integral of thermal energy J m-2 This parameter is the mass-weighted vertical integral of thermal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Thermal energy is calculated from the product of temperature and the specific heat capacity of air at constant pressure. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of thermal energy J m-2 This parameter is the mass-weighted vertical integral of thermal energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Thermal energy is calculated from the product of temperature and the specific heat capacity of air at constant pressure. The thermal energy is equal to enthalpy, which is the sum of the internal energy and the energy associated with the pressure of the air on its surroundings. Internal energy is the energy contained within a system i.e., the microscopic energy of the air molecules, rather than the macroscopic energy associated with, for example, wind, or gravitational potential energy. The energy associated with the pressure of the air on its surroundings is the energy required to make room for the system by displacing its surroundings and is calculated from the product of pressure and volume. This parameter can be used to study the atmospheric energy budget. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. Vertical integral of total energy J m-2 This parameter is the vertical integral of total energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertical integral of total energy J m-2 This parameter is the vertical integral of total energy for a column of air extending from the surface of the Earth to the top of the atmosphere. Total atmospheric energy is made up of internal, potential, kinetic and latent energy. This parameter can be used to study the atmospheric energy budget. Vertically integrated moisture divergence kg m-2 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm. Vertically integrated moisture divergence kg m-2 The vertical integral of the moisture flux is the horizontal rate of flow of moisture (water vapour, cloud liquid and cloud ice), per metre across the flow, for a column of air extending from the surface of the Earth to the top of the atmosphere. Its horizontal divergence is the rate of moisture spreading outward from a point, per square metre. This parameter is accumulated over a particular time period which depends on the data extracted. For the monthly averaged reanalysis and the monthly averaged ensemble members, the accumulation period is 1 day. For the monthly averaged reanalysis by hour of day, the accumulation period is 1 hour and for the monthly averaged ensemble members by hour of day, the accumulation period is 3 hours. This parameter is positive for moisture that is spreading out, or diverging, and negative for the opposite, for moisture that is concentrating, or converging (convergence). This parameter thus indicates whether atmospheric motions act to decrease (for divergence) or increase (for convergence) the vertical integral of moisture, over the time period. High negative values of this parameter (i.e. large moisture convergence) can be related to precipitation intensification and floods. 1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm. Volumetric soil water layer 1 m3 m-3 This parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 1 m3 m-3 This parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 This parameter is the volume of water in soil layer 2 (7 - 28cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 2 m3 m-3 This parameter is the volume of water in soil layer 2 (7 - 28cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 3 m3 m-3 This parameter is the volume of water in soil layer 3 (28 - 100cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 3 m3 m-3 This parameter is the volume of water in soil layer 3 (28 - 100cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 4 m3 m-3 This parameter is the volume of water in soil layer 4 (100 - 289cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Volumetric soil water layer 4 m3 m-3 This parameter is the volume of water in soil layer 4 (100 - 289cm, the surface is at 0cm). The ECMWF Integrated Forecasting System (IFS) has a four-layer representation of soil: Layer 1: 0 - 7cm, Layer 2: 7 - 28cm, Layer 3: 28 - 100cm, Layer 4: 100 - 289cm. Soil water is defined over the whole globe, even over ocean. Regions with a water surface can be masked out by only considering grid points where the land-sea mask has a value greater than 0.5. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Wave spectral directional width Dimensionless This parameter indicates whether waves (generated by local winds and associated with swell) are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width Dimensionless This parameter indicates whether waves (generated by local winds and associated with swell) are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for swell Dimensionless This parameter indicates whether waves associated with swell are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for swell Dimensionless This parameter indicates whether waves associated with swell are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of all swell only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for wind waves Dimensionless This parameter indicates whether waves generated by the local wind are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width for wind waves Dimensionless This parameter indicates whether waves generated by the local wind are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of wind-sea waves only. Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral kurtosis Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It shows how much of the probability density function of the sea surface elevation exists in the tails of the distribution. So, a positive kurtosis (typical range 0.0 to 0.06) means more frequent occurrences of very extreme values (either above or below the mean), relative to a normal distribution. Wave spectral kurtosis Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It shows how much of the probability density function of the sea surface elevation exists in the tails of the distribution. So, a positive kurtosis (typical range 0.0 to 0.06) means more frequent occurrences of very extreme values (either above or below the mean), relative to a normal distribution. Wave spectral peakedness Dimensionless This parameter is a statistical measure used to forecast extreme or freak waves. It is a measure of the relative width of the ocean/sea wave frequency spectrum (i.e., whether the ocean/sea wave field is made up of a narrow or broad range of frequencies). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). When the wave field is more focussed around a narrow range of frequencies, the probability of freak/extreme waves increases. This parameter is Goda's peakedness factor and is used to calculate the Benjamin-Feir Index (BFI). The BFI is in turn used to estimate the probability and nature of extreme/freak waves. Wave spectral peakedness Dimensionless This parameter is a statistical measure used to forecast extreme or freak waves. It is a measure of the relative width of the ocean/sea wave frequency spectrum (i.e., whether the ocean/sea wave field is made up of a narrow or broad range of frequencies). The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). When the wave field is more focussed around a narrow range of frequencies, the probability of freak/extreme waves increases. This parameter is Goda's peakedness factor and is used to calculate the Benjamin-Feir Index (BFI). The BFI is in turn used to estimate the probability and nature of extreme/freak waves. Wave spectral skewness Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It is a measure of the asymmetry of the probability density function of the sea surface elevation. So, a positive/negative skewness (typical range -0.2 to 0.12) means more frequent occurrences of extreme values above/below the mean, relative to a normal distribution. Wave spectral skewness Dimensionless This parameter is a statistical measure used to forecast extreme or freak ocean/sea waves. It describes the nature of the sea surface elevation and how it is affected by waves generated by local winds and associated with swell. Under typical conditions, the sea surface elevation, as described by its probability density function, has a near normal distribution in the statistical sense. However, under certain wave conditions the probability density function of the sea surface elevation can deviate considerably from normality, signalling increased probability of freak waves. This parameter gives one measure of the deviation from normality. It is a measure of the asymmetry of the probability density function of the sea surface elevation. So, a positive/negative skewness (typical range -0.2 to 0.12) means more frequent occurrences of extreme values above/below the mean, relative to a normal distribution. Zero degree level m The height above the Earth's surface where the temperature passes from positive to negative values, corresponding to the top of a warm layer, at the specified time. This parameter can be used to help forecast snow. If more than one warm layer is encountered, then the zero degree level corresponds to the top of the second atmospheric layer. This parameter is set to zero when the temperature in the whole atmosphere is below 0℃. Zero degree level m The height above the Earth's surface where the temperature passes from positive to negative values, corresponding to the top of a warm layer, at the specified time. This parameter can be used to help forecast snow. If more than one warm layer is encountered, then the zero degree level corresponds to the top of the second atmospheric layer. This parameter is set to zero when the temperature in the whole atmosphere is below 0℃. 396 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-sea-surface-temperature-analysis-l4 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_BAL_SST_L4_NRT_OBSERVATIONS_010_007_b Baltic Sea- Sea Surface Temperature Analysis L4 Short description: For the Baltic Sea- The DMI Sea Surface Temperature analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red and microwave radiometers. Uses SST nighttime satellite products from these sensors: NOAA AVHRR, Metop AVHRR, Terra MODIS, Aqua MODIS, Aqua AMSR-E, Envisat AATSR, MSG Seviri DOI (product) :https://doi.org/10.48670/moi-00155 https://doi.org/10.48670/moi-00155 397 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-lake-water-level https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-lake-water-level satellite-lake-water-level This dataset provides lake water levels for 229 selected lakes on four continents derived from satellite radar altimetry. Lake water level information is traditionally obtained via ground-based observation systems and networks that suffer from well-known inherent problems: high cost, sparse coverage (often limited to political local/national instead of geographical/hydrological boundaries), slow dissemination of data, heterogeneous temporal coverage, destruction of the stations during floods, absence of stations in remote areas, absence of management strategy, etc. Although originally conceived to study open ocean processes, the radar altimeter satellites have nevertheless acquired numerous useful measurements over lakes. With this technique, the Lake Water Level is defined as the height, in meters above the geoid (the shape that the surface would take under the influence of the gravity and rotation of Earth) of the reflecting surface. It is observed by space radar altimeters that measure the time it takes for radar pulses to reach the ground targets, directly below the spacecraft (nadir position), and return. Hence, only lakes located along the satellite's ground tracks can be monitored, with a quality of measurement that not only depends of the size of the lake, but also on the reflecting targets in its surroundings such as topography or vegetation. For each of the key lakes monitored in the C3S Lakes production system, the Level-3 Water Level is provided in separate file along with the corresponding uncertainty. This altimetric technique, that can complement ground-based observations systems, potentially provides a major improvement in the field of continental hydrology, due to the global coverage (however limited to Earth’s portion at nadir of the orbital ground tracks), regular temporal sampling and short delivery delays. The Lake Water Level is a recognized Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Lake Water Level products are generated by CLS on behalf of the Copernicus Climate Change Services, the Earth Observation Programme of the European Commission. DATA DESCRIPTION Data type Point data Horizontal coverage Global (229 lakes on 4 continents) Horizontal resolution One value per lake per timestep Vertical coverage Single level Vertical resolution Surface Temporal coverage 1992 to present Temporal resolution Between 1 to 10 days File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.8, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 2.1 (deprecated): 1992-2019, 94 lakes 3.1 (deprecated): 1992-2021, 166 lakes 4.0 (current): 1992-2022, 229 lakes Update frequency Annually DATA DESCRIPTION DATA DESCRIPTION Data type Point data Data type Point data Horizontal coverage Global (229 lakes on 4 continents) Horizontal coverage Global (229 lakes on 4 continents) Horizontal resolution One value per lake per timestep Horizontal resolution One value per lake per timestep Vertical coverage Single level Vertical coverage Single level Vertical resolution Surface Vertical resolution Surface Temporal coverage 1992 to present Temporal coverage 1992 to present Temporal resolution Between 1 to 10 days Temporal resolution Between 1 to 10 days File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.8, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.8, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 2.1 (deprecated): 1992-2019, 94 lakes 3.1 (deprecated): 1992-2021, 166 lakes 4.0 (current): 1992-2022, 229 lakes Versions 2.1 (deprecated): 1992-2019, 94 lakes 3.1 (deprecated): 1992-2021, 166 lakes 4.0 (current): 1992-2022, 229 lakes 2.1 (deprecated): 1992-2019, 94 lakes 3.1 (deprecated): 1992-2021, 166 lakes 4.0 (current): 1992-2022, 229 lakes Update frequency Annually Update frequency Annually MAIN VARIABLES Name Units Description Water surface height m The height of the water surface with respect to geoid (the shape that the surface would take under the influence of the gravity and rotation of Earth) of the inland water body. Water surface height uncertainty m The uncertainty attributed to the water surface height for each time step. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Water surface height m The height of the water surface with respect to geoid (the shape that the surface would take under the influence of the gravity and rotation of Earth) of the inland water body. Water surface height m The height of the water surface with respect to geoid (the shape that the surface would take under the influence of the gravity and rotation of Earth) of the inland water body. Water surface height uncertainty m The uncertainty attributed to the water surface height for each time step. Water surface height uncertainty m The uncertainty attributed to the water surface height for each time step. 398 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-soil-moisture https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-soil-moisture satellite-soil-moisture This dataset provides estimates of surface soil moisture over the globe from a large set of satellite sensors. It is based on the methodology developed in the ESA Climate Change Initiative for Soil Moisture and represents the current state-of-the-art for satellite-based soil moisture climate data record production, in line with the “Systematic observation requirements for satellite-based products for climate” as defined by GCOS (Global Climate Observing System). Data are on a regular latitude/longitude grid expectedly with gaps in space and time. When dealing with satellite data it is common to encounter references to Climate Data Records (CDR) and interim-CDR (ICDR). For this dataset, both the ICDR and CDR parts of each product were generated using the same software and algorithms. The CDR is intended to have sufficient length, consistency, and continuity to detect climate variability and change. The ICDR provides a short-delay access to current data where consistency with the CDR baseline is expected but was not extensively checked. The dataset contains the following products: "Active", "Passive" and "Combined". The "Active" and "Passive" products were created by using scatterometer and radiometer soil moisture products, respectively. The "Combined" product results from a blend based on both scatterometer and radiometer soil moisture products. This dataset is produced on behalf of the Copernicus Climate Change Service (C3S). DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 0.25° x 0.25° Temporal coverage 1978 to present Temporal resolution Daily, 10-day, Monthy File format NetCDF Conventions Climate and Forecast (CF) Metadata Convention v1.8 Versions v201706: First release of the dataset. Equivalent to CCI version 3. v201812: Algorithm updates (merging, signal to noise ratio gap filling, uncertainties, masking), sensor updates (SMOS included). Equivalent to CCI version 4. v201912: Deprecated. Temporal extension of v201812 to 2019-12-31, updates in passive data pre-processing. Equivalent to CCI version 3. v201912.1: Correction to v201912 v202012: Algorithm updates (passive sensors processing, matching, sensor updates (SMAP included). Equivalent to CCI version 5. v202212: Algorithm updates (error estimates, flagging), sensor updates (ASCAT-C, FengYun 3B/C/D and GPM). Equivalent to CCI version 7 Update frequency ICDR: produced on a 10-day cycle with 10-day latency. CDR: annually updated. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.25° x 0.25° Horizontal resolution 0.25° x 0.25° Temporal coverage 1978 to present Temporal coverage 1978 to present Temporal resolution Daily, 10-day, Monthy Temporal resolution Daily, 10-day, Monthy File format NetCDF File format NetCDF Conventions Climate and Forecast (CF) Metadata Convention v1.8 Conventions Climate and Forecast (CF) Metadata Convention v1.8 Versions v201706: First release of the dataset. Equivalent to CCI version 3. v201812: Algorithm updates (merging, signal to noise ratio gap filling, uncertainties, masking), sensor updates (SMOS included). Equivalent to CCI version 4. v201912: Deprecated. Temporal extension of v201812 to 2019-12-31, updates in passive data pre-processing. Equivalent to CCI version 3. v201912.1: Correction to v201912 v202012: Algorithm updates (passive sensors processing, matching, sensor updates (SMAP included). Equivalent to CCI version 5. v202212: Algorithm updates (error estimates, flagging), sensor updates (ASCAT-C, FengYun 3B/C/D and GPM). Equivalent to CCI version 7 Versions v201706: First release of the dataset. Equivalent to CCI version 3. v201812: Algorithm updates (merging, signal to noise ratio gap filling, uncertainties, masking), sensor updates (SMOS included). Equivalent to CCI version 4. v201912: Deprecated. Temporal extension of v201812 to 2019-12-31, updates in passive data pre-processing. Equivalent to CCI version 3. v201912.1: Correction to v201912 v202012: Algorithm updates (passive sensors processing, matching, sensor updates (SMAP included). Equivalent to CCI version 5. v202212: Algorithm updates (error estimates, flagging), sensor updates (ASCAT-C, FengYun 3B/C/D and GPM). Equivalent to CCI version 7 v201706: First release of the dataset. Equivalent to CCI version 3. v201812: Algorithm updates (merging, signal to noise ratio gap filling, uncertainties, masking), sensor updates (SMOS included). Equivalent to CCI version 4. v201912: Deprecated. Temporal extension of v201812 to 2019-12-31, updates in passive data pre-processing. Equivalent to CCI version 3. v201912.1: Correction to v201912 v202012: Algorithm updates (passive sensors processing, matching, sensor updates (SMAP included). Equivalent to CCI version 5. v202212: Algorithm updates (error estimates, flagging), sensor updates (ASCAT-C, FengYun 3B/C/D and GPM). Equivalent to CCI version 7 Update frequency ICDR: produced on a 10-day cycle with 10-day latency. CDR: annually updated. Update frequency ICDR: produced on a 10-day cycle with 10-day latency. CDR: annually updated. ICDR: produced on a 10-day cycle with 10-day latency. CDR: annually updated. MAIN VARIABLES Name Units Description Surface soil moisture % Content of liquid water in a surface soil layer of 2 to 5 cm depth expressed as the percentage of total saturation. Volumetric soil moisture m3 m-3 Content of liquid water in a surface soil layer of 2 to 5 cm depth expressed as m3 water per m3 soil. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Surface soil moisture % Content of liquid water in a surface soil layer of 2 to 5 cm depth expressed as the percentage of total saturation. Surface soil moisture % Content of liquid water in a surface soil layer of 2 to 5 cm depth expressed as the percentage of total saturation. Volumetric soil moisture m3 m-3 Content of liquid water in a surface soil layer of 2 to 5 cm depth expressed as m3 water per m3 soil. Volumetric soil moisture m3 m-3 Content of liquid water in a surface soil layer of 2 to 5 cm depth expressed as m3 water per m3 soil. 399 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-ice-sheet-elevation-change https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-ice-sheet-elevation-change satellite-ice-sheet-elevation-change This dataset provides estimates of surface elevation change over the Greenland and Antarctic ice sheets since 1992, utilizing satellite radar altimetry from five missions: ERS-1, ERS-2, ENVISAT, CryoSat-2, and Sentinel-3A. The surface elevation change is modelled over successive, overlapping periods and reported monthly. The dataset production method is an evolution of those employed by the European Space Agency (ESA)'s Greenland and Antarctic Ice Sheet Climate Change Initiatives and is guided by the Global Climate Observing System targets for the Ice Sheets Essential Climate Variable. An annual Climate Data Record (CDR), and monthly intermediate CDRs (ICDRs) are issued. Each monthly record includes all previous data, from 1992 onwards, as well as that month's update. Since February 2023, the ICDRs are issued for Greenland only. This product is designed to provide data stability, so changes in the historic data, eg. if a satellite's elevation data is reprocessed or if inter-satellite cross-calibration is revised, are only introduced in the annual CDR. Each annual CDR is given a version number. The differences in the geographical location of the two sheets result in site-specific processing: Greenland: Data consists of surface elevation change rate and its uncertainty in a five-year (for the early satellites: ERS-1, ERS-2, and ENVISat) or three-year (for CryoSat-2 and Sentinel-3A) moving window. The moving window is advanced at one-month steps. Elevation measurements from satellite radar altimetry are used to build timeseries of elevation change by the most optimal combination of the crossover-, repeat-track- and plane-fitting methods. The timeseries is derived for each cell on a 25km by 25km polar stereographic grid, covering the main Greenland ice sheet, and not including peripheral glaciers and ice caps. Data gaps have been filled using an ordinary Kriging interpolation method, and the distance to the nearest observational point is provided as utility information. The distance can be used to flag filled data. The data temporal coverage is from 1992 to present. Antarctica: Data consists of surface elevation change rate over a five-year moving window that advances in one-month steps. It covers the Antarctic ice sheet, ice shelves and associated ice rises and islands on a 25km by 25km polar stereographic grid. Elevation measurements from five satellite radar altimetry missions, ERS1, ERS2, EnviSat, CryoSat-2 and Sentinel-3A, are used to produce timeseries of surface elevation change by the crossover method for each grid cell. The mission timeseries are cross-calibrated into a consistent record, which is used to derive surface elevation change rates and their uncertainty estimates in each cell and time-window. Data gaps are flagged but not filled. The data temporal coverage is from 1992 to 2022. DATA DESCRIPTION Data type Gridded Horizontal coverage Greenland and Antarctica, on a regular polar stereographic grid Horizontal resolution 25km x 25km Vertical coverage Surface Vertical resolution Single layer Temporal coverage Greenland: 1992 to present, with a two-month lag Antarctica: 1992 to April 2022 Temporal resolution Greenland: 5-year model (for the early satellites: ERS-1, ERS-2, and ENVISat) or 3-year model (for CryoSat-2 and Sentinel-3A), applied at monthly intervals Antarctica: 5-year model, applied at monthly intervals File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Versions version 2.0: Contains data from ERS1, ERS2, EnviSat, CryoSat-2 and Sentinel-3A. version 3.0: Data from Sentinel-3B added to version 2.0 sources. Data from EnviSat and CryoSat-2 revised using upgraded baselines. version 4.0: Updated timeseries and revised filtering of raw data. Update frequency Greenland: Monthly Antarctica: not updated since February 2023 DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Greenland and Antarctica, on a regular polar stereographic grid Horizontal coverage Greenland and Antarctica, on a regular polar stereographic grid Horizontal resolution 25km x 25km Horizontal resolution 25km x 25km Vertical coverage Surface Vertical coverage Surface Vertical resolution Single layer Vertical resolution Single layer Temporal coverage Greenland: 1992 to present, with a two-month lag Antarctica: 1992 to April 2022 Temporal coverage Greenland: 1992 to present, with a two-month lag Antarctica: 1992 to April 2022 Greenland: 1992 to present, with a two-month lag Antarctica: 1992 to April 2022 Temporal resolution Greenland: 5-year model (for the early satellites: ERS-1, ERS-2, and ENVISat) or 3-year model (for CryoSat-2 and Sentinel-3A), applied at monthly intervals Antarctica: 5-year model, applied at monthly intervals Temporal resolution Greenland: 5-year model (for the early satellites: ERS-1, ERS-2, and ENVISat) or 3-year model (for CryoSat-2 and Sentinel-3A), applied at monthly intervals Antarctica: 5-year model, applied at monthly intervals Greenland: 5-year model (for the early satellites: ERS-1, ERS-2, and ENVISat) or 3-year model (for CryoSat-2 and Sentinel-3A), applied at monthly intervals Antarctica: 5-year model, applied at monthly intervals File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Versions version 2.0: Contains data from ERS1, ERS2, EnviSat, CryoSat-2 and Sentinel-3A. version 3.0: Data from Sentinel-3B added to version 2.0 sources. Data from EnviSat and CryoSat-2 revised using upgraded baselines. version 4.0: Updated timeseries and revised filtering of raw data. Versions version 2.0: Contains data from ERS1, ERS2, EnviSat, CryoSat-2 and Sentinel-3A. version 3.0: Data from Sentinel-3B added to version 2.0 sources. Data from EnviSat and CryoSat-2 revised using upgraded baselines. version 4.0: Updated timeseries and revised filtering of raw data. version 2.0: Contains data from ERS1, ERS2, EnviSat, CryoSat-2 and Sentinel-3A. version 3.0: Data from Sentinel-3B added to version 2.0 sources. Data from EnviSat and CryoSat-2 revised using upgraded baselines. version 4.0: Updated timeseries and revised filtering of raw data. Update frequency Greenland: Monthly Antarctica: not updated since February 2023 Update frequency Greenland: Monthly Antarctica: not updated since February 2023 Greenland: Monthly Antarctica: not updated since February 2023 MAIN VARIABLES Name Units Description Rate of elevation change m year-1 Greenland: Surface elevation change for each pixel in the grid over a period of 5 years for ERS1 & 2 and EnviSat and 3 years for CryoSat-2 and Sentinel-3. Referred in the files as DHDT. The specific time when the 5 year period (or the 3 year period after 2010) is centered is provided through the variable Time. The radar altimeter measurements are corrected for instrument effects, dry and wet tropospheric effect, ionospheric delay and surface slope. Measurements of height change are derived from a combination of repeat-track, along-track, crossover and plane-fitting algorithms. The height change rate is derived from weighted averaging of height change maps. Antarctica: Surface elevation change over a period of 5 years for each pixel in the grid. Referred in the files as SEC. The specific value of the time when the 5 year period is centered is provided through the variable Time. Radar altimeter measurements over all surfaces are corrected for instrument effects, dry and wet troposheric effect, ionospheric delay, solid earth tide, geocentric pole tide, ocean loading tide and surface slope. Over ice shelves they are also corrected for ocean tide and the inverse barometer effect. Measurements of height change are derived from the crossover method, and altimeters from different missions cross-calibrated using a multiple regression method. The height change rate is derived from least-squares fitting. No filling is applied to surface or time gaps. Rate of elevation change uncertainty m year-1 Fitting uncertainty of the rate of elevation change. Greenland: The total uncertainty on the surface elevation change is the sum of two components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The measurement component is the combination of the uncertainties on the original altimetry measurements that went into the surface elevation change calculation, including geolocation, radar penetration, volume scattering, radar speckle and atmospheric attenuation. Antarctica: The total uncertainty on the surface elevation change is the sum of three components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The cross-calibration component is the standard deviation of any mission-pair cross-calibrations used in the 5 year period. The epoch component is the RMS of the uncertainties on the original altimetry measurements, as for Greenland. Time hour The mid-time of the surface elevation change period. Given as the number of hours since 1990-01-01 00:00:00 UTC. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Rate of elevation change m year-1 Greenland: Surface elevation change for each pixel in the grid over a period of 5 years for ERS1 & 2 and EnviSat and 3 years for CryoSat-2 and Sentinel-3. Referred in the files as DHDT. The specific time when the 5 year period (or the 3 year period after 2010) is centered is provided through the variable Time. The radar altimeter measurements are corrected for instrument effects, dry and wet tropospheric effect, ionospheric delay and surface slope. Measurements of height change are derived from a combination of repeat-track, along-track, crossover and plane-fitting algorithms. The height change rate is derived from weighted averaging of height change maps. Antarctica: Surface elevation change over a period of 5 years for each pixel in the grid. Referred in the files as SEC. The specific value of the time when the 5 year period is centered is provided through the variable Time. Radar altimeter measurements over all surfaces are corrected for instrument effects, dry and wet troposheric effect, ionospheric delay, solid earth tide, geocentric pole tide, ocean loading tide and surface slope. Over ice shelves they are also corrected for ocean tide and the inverse barometer effect. Measurements of height change are derived from the crossover method, and altimeters from different missions cross-calibrated using a multiple regression method. The height change rate is derived from least-squares fitting. No filling is applied to surface or time gaps. Rate of elevation change m year-1 Greenland: Surface elevation change for each pixel in the grid over a period of 5 years for ERS1 & 2 and EnviSat and 3 years for CryoSat-2 and Sentinel-3. Referred in the files as DHDT. The specific time when the 5 year period (or the 3 year period after 2010) is centered is provided through the variable Time. The radar altimeter measurements are corrected for instrument effects, dry and wet tropospheric effect, ionospheric delay and surface slope. Measurements of height change are derived from a combination of repeat-track, along-track, crossover and plane-fitting algorithms. The height change rate is derived from weighted averaging of height change maps. Antarctica: Surface elevation change over a period of 5 years for each pixel in the grid. Referred in the files as SEC. The specific value of the time when the 5 year period is centered is provided through the variable Time. Radar altimeter measurements over all surfaces are corrected for instrument effects, dry and wet troposheric effect, ionospheric delay, solid earth tide, geocentric pole tide, ocean loading tide and surface slope. Over ice shelves they are also corrected for ocean tide and the inverse barometer effect. Measurements of height change are derived from the crossover method, and altimeters from different missions cross-calibrated using a multiple regression method. The height change rate is derived from least-squares fitting. No filling is applied to surface or time gaps. Greenland: Surface elevation change for each pixel in the grid over a period of 5 years for ERS1 & 2 and EnviSat and 3 years for CryoSat-2 and Sentinel-3. Referred in the files as DHDT. The specific time when the 5 year period (or the 3 year period after 2010) is centered is provided through the variable Time. The radar altimeter measurements are corrected for instrument effects, dry and wet tropospheric effect, ionospheric delay and surface slope. Measurements of height change are derived from a combination of repeat-track, along-track, crossover and plane-fitting algorithms. The height change rate is derived from weighted averaging of height change maps. Antarctica: Surface elevation change over a period of 5 years for each pixel in the grid. Referred in the files as SEC. The specific value of the time when the 5 year period is centered is provided through the variable Time. Radar altimeter measurements over all surfaces are corrected for instrument effects, dry and wet troposheric effect, ionospheric delay, solid earth tide, geocentric pole tide, ocean loading tide and surface slope. Over ice shelves they are also corrected for ocean tide and the inverse barometer effect. Measurements of height change are derived from the crossover method, and altimeters from different missions cross-calibrated using a multiple regression method. The height change rate is derived from least-squares fitting. No filling is applied to surface or time gaps. Rate of elevation change uncertainty m year-1 Fitting uncertainty of the rate of elevation change. Greenland: The total uncertainty on the surface elevation change is the sum of two components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The measurement component is the combination of the uncertainties on the original altimetry measurements that went into the surface elevation change calculation, including geolocation, radar penetration, volume scattering, radar speckle and atmospheric attenuation. Antarctica: The total uncertainty on the surface elevation change is the sum of three components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The cross-calibration component is the standard deviation of any mission-pair cross-calibrations used in the 5 year period. The epoch component is the RMS of the uncertainties on the original altimetry measurements, as for Greenland. Rate of elevation change uncertainty m year-1 Fitting uncertainty of the rate of elevation change. Greenland: The total uncertainty on the surface elevation change is the sum of two components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The measurement component is the combination of the uncertainties on the original altimetry measurements that went into the surface elevation change calculation, including geolocation, radar penetration, volume scattering, radar speckle and atmospheric attenuation. Antarctica: The total uncertainty on the surface elevation change is the sum of three components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The cross-calibration component is the standard deviation of any mission-pair cross-calibrations used in the 5 year period. The epoch component is the RMS of the uncertainties on the original altimetry measurements, as for Greenland. Fitting uncertainty of the rate of elevation change. Greenland: The total uncertainty on the surface elevation change is the sum of two components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The measurement component is the combination of the uncertainties on the original altimetry measurements that went into the surface elevation change calculation, including geolocation, radar penetration, volume scattering, radar speckle and atmospheric attenuation. Antarctica: The total uncertainty on the surface elevation change is the sum of three components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The cross-calibration component is the standard deviation of any mission-pair cross-calibrations used in the 5 year period. The epoch component is the RMS of the uncertainties on the original altimetry measurements, as for Greenland. Time hour The mid-time of the surface elevation change period. Given as the number of hours since 1990-01-01 00:00:00 UTC. Time hour The mid-time of the surface elevation change period. Given as the number of hours since 1990-01-01 00:00:00 UTC. RELATED VARIABLES Below are listed a number of related variables that are common to both the Antarctic and Greenland data. Other related variables are included in the files but are not listed in this table. Users are advised to consult the product user guide and specification for further details on the file contents. Name Units Description Land mask Dimensionless Greenland: Flag indicating ice cover, from the PROMICE project. Antarctica: Flag indicating surface type, from the MODIS project. Slope mask Dimensionless Flag for the validity of the result of the computation of the ice sheet slope. Valid flag Dimensionless Flag indicating the validity of the elevation change estimate. RELATED VARIABLES RELATED VARIABLES Below are listed a number of related variables that are common to both the Antarctic and Greenland data. Other related variables are included in the files but are not listed in this table. Users are advised to consult the product user guide and specification for further details on the file contents. Below are listed a number of related variables that are common to both the Antarctic and Greenland data. Other related variables are included in the files but are not listed in this table. Users are advised to consult the product user guide and specification for further details on the file contents. Name Units Description Name Units Description Land mask Dimensionless Greenland: Flag indicating ice cover, from the PROMICE project. Antarctica: Flag indicating surface type, from the MODIS project. Land mask Dimensionless Greenland: Flag indicating ice cover, from the PROMICE project. Antarctica: Flag indicating surface type, from the MODIS project. Greenland: Flag indicating ice cover, from the PROMICE project. Antarctica: Flag indicating surface type, from the MODIS project. Slope mask Dimensionless Flag for the validity of the result of the computation of the ice sheet slope. Slope mask Dimensionless Flag for the validity of the result of the computation of the ice sheet slope. Valid flag Dimensionless Flag indicating the validity of the elevation change estimate. Valid flag Dimensionless Flag indicating the validity of the elevation change estimate. 400 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/european-seas-along-track-l3-sea-level-anomalies-nrt http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_EUR_PHY_L3_NRT_OBSERVATIONS_008_059 EUROPEAN SEAS ALONG-TRACK L3 SEA LEVEL ANOMALIES NRT TAILORED FOR DATA ASSIMILATION Short description: Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in near-real time applications. This product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, HY-2B). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details) http://duacs.cls.fr http://duacs.cls.fr “’Associated products”’ A time invariant product http://marine.copernicus.eu/services-portfolio/access-to-products/?opti… [http://marine.copernicus.eu/services-portfolio/access-to-products/?opti…] describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document. http://marine.copernicus.eu/services-portfolio/access-to-products/?opti… http://marine.copernicus.eu/services-portfolio/access-to-products/?opti… DOI (product) :https://doi.org/10.48670/moi-00140 https://doi.org/10.48670/moi-00140 401 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-cerra-pressure-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-cerra-pressure-levels reanalysis-cerra-pressure-levels The Copernicus European Regional ReAnalysis (CERRA) datasets provide spatially and temporally consistent historical reconstruction of meteorological variables in the atmosphere and at the surface. There are four subsets: single levels (atmospheric and surface quantities), height levels (upper-air fields up to 500m), pressure levels (upper-air fields up to 1hPa) and model levels (native levels of the model). This entry provides reanalysis and forecast data on pressure levels for Europe from 1984 to present. Several atmospheric parameters are common to both reanalysis and forecast (e.g. temperature, wind), whilst others are produced only by the forecast model (e.g. cloud cover). Reanalysis combines model data with observations into a complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved, reprocessed versions of the original observations, which all benefit the quality of the reanalysis product. The CERRA dataset was produced using the HARMONIE-ALADIN limited-area numerical weather prediction and data assimilation system, hereafter referred to as the CERRA system. The CERRA system employs a 3-dimensional variational data assimilation scheme of the atmospheric state at every assimilation time. The reanalysis dataset is convenient owing to its provision of atmospheric estimates at each model domain grid point over Europe for each regular output time, over a long period, and always using the same data format. The inputs to CERRA reanalysis are the observational data, lateral boundary conditions from ERA5 global reanalysis as prior estimates of the atmospheric state and physiographic datasets describing the surface characteristics of the model. The observing system has evolved over time, and although the data assimilation system can resolve data holes, the much sparser observational networks in the past periods (for example a reduced amount of satellite data in the 1980s) can impact the quality of analyses leading to less accurate estimates. The uncertainty estimates for reanalysis variables are provided by the CERRA-EDA, a 10-member ensemble of data assimilation system. The added value of the CERRA data with respect to the global reanalysis products is expected to come, for example, with the higher horizontal resolution that permits the usage of a better description of the model topography and physiographic data, and the assimilation of more surface observations. More information about the CERRA dataset can be found in the Documentation section. DATA DESCRIPTION Data type Gridded Projection Lambert conformal conic Horizontal coverage Europe. The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Horizontal resolution 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members Vertical coverage From 1000 hPa to 1hPa (1000, 975, 950, 925, 900, 875, 850, 825, 800, 750, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1) Vertical resolution 29 levels Temporal coverage September 1984 to June 2021 Temporal resolution Analysis data: 3-hourly for high-resolution, 6-hourly for ensemble members Forecast data: hourly for forecast range 1 - 6 (high-resolution and ensemble members), 3-hourly for forecast range 6 - 30 (high-resolution only) File format GRIB2 Update frequency New data will be added towards the end of 2023 DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert conformal conic Projection Lambert conformal conic Horizontal coverage Europe. The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Horizontal coverage Europe. The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Horizontal resolution 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members Horizontal resolution 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members Vertical coverage From 1000 hPa to 1hPa (1000, 975, 950, 925, 900, 875, 850, 825, 800, 750, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1) Vertical coverage From 1000 hPa to 1hPa (1000, 975, 950, 925, 900, 875, 850, 825, 800, 750, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1) Vertical resolution 29 levels Vertical resolution 29 levels Temporal coverage September 1984 to June 2021 Temporal coverage September 1984 to June 2021 Temporal resolution Analysis data: 3-hourly for high-resolution, 6-hourly for ensemble members Forecast data: hourly for forecast range 1 - 6 (high-resolution and ensemble members), 3-hourly for forecast range 6 - 30 (high-resolution only) Temporal resolution Analysis data: 3-hourly for high-resolution, 6-hourly for ensemble members Forecast data: hourly for forecast range 1 - 6 (high-resolution and ensemble members), 3-hourly for forecast range 6 - 30 (high-resolution only) Analysis data: 3-hourly for high-resolution, 6-hourly for ensemble members Forecast data: hourly for forecast range 1 - 6 (high-resolution and ensemble members), 3-hourly for forecast range 6 - 30 (high-resolution only) File format GRIB2 File format GRIB2 Update frequency New data will be added towards the end of 2023 Update frequency New data will be added towards the end of 2023 MAIN VARIABLES Name Units Description Cloud cover % This parameter is the proportion of a grid box covered by cloud (liquid or ice) and is available on multiple atmospheric pressure levels. Values are in the interval [0,1]. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level, or similarly, the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2 ). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography. Relative humidity % This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function. Values are in the interval [0,100]. Specific cloud ice water content kg kg-1 This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'. Specific cloud liquid water content kg kg-1 This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the HARMONIE-ALADIN model. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The model cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the model. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the HARMONIE-ALADIN model. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the model at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The model cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the model. Temperature K This parameter is the air temperature at the atmospheric pressure levels. Temperature measured in Kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Turbulent kinetic energy J kg-1 The turbulent kinetic energy is the mean kinetic energy per unit mass associated with eddies in turbulent flow. This parameter describes the turbulent kinetic energy at a pressure level and it is valid for the grid area. The turbulent kinetic energy is only available the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. U-component of wind m s-1 The U-component of wind is the zonal component of the wind valid for the grid area at the corresponding pressure level. By model convention, a negative(positive) value indicates air moving towards the west (east). This variable can be combined with the V-component of wind to give the speed and direction of the horizontal wind. V-component of wind m s-1 The V-component of wind is the meridional component of the wind valid for the grid area at the corresponding model level. By model convention, a negative(positive) value indicates air moving towards the south (north). This variable can be combined with the U-component of wind to give the speed and direction of the horizontal wind. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Cloud cover % This parameter is the proportion of a grid box covered by cloud (liquid or ice) and is available on multiple atmospheric pressure levels. Values are in the interval [0,1]. Cloud cover % This parameter is the proportion of a grid box covered by cloud (liquid or ice) and is available on multiple atmospheric pressure levels. Values are in the interval [0,1]. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level, or similarly, the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2 ). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level, or similarly, the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2 ). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography. Relative humidity % This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function. Values are in the interval [0,100]. Relative humidity % This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function. Values are in the interval [0,100]. Specific cloud ice water content kg kg-1 This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'. Specific cloud ice water content kg kg-1 This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'. Specific cloud liquid water content kg kg-1 This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud liquid water content kg kg-1 This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the HARMONIE-ALADIN model. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The model cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the model. Specific rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the HARMONIE-ALADIN model. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The model cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the model. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the HARMONIE-ALADIN model. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the model at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The model cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the model. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the HARMONIE-ALADIN model. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the model at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The model cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the model. Temperature K This parameter is the air temperature at the atmospheric pressure levels. Temperature measured in Kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Temperature K This parameter is the air temperature at the atmospheric pressure levels. Temperature measured in Kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. Turbulent kinetic energy J kg-1 The turbulent kinetic energy is the mean kinetic energy per unit mass associated with eddies in turbulent flow. This parameter describes the turbulent kinetic energy at a pressure level and it is valid for the grid area. The turbulent kinetic energy is only available the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Turbulent kinetic energy J kg-1 The turbulent kinetic energy is the mean kinetic energy per unit mass associated with eddies in turbulent flow. This parameter describes the turbulent kinetic energy at a pressure level and it is valid for the grid area. The turbulent kinetic energy is only available the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. U-component of wind m s-1 The U-component of wind is the zonal component of the wind valid for the grid area at the corresponding pressure level. By model convention, a negative(positive) value indicates air moving towards the west (east). This variable can be combined with the V-component of wind to give the speed and direction of the horizontal wind. U-component of wind m s-1 The U-component of wind is the zonal component of the wind valid for the grid area at the corresponding pressure level. By model convention, a negative(positive) value indicates air moving towards the west (east). This variable can be combined with the V-component of wind to give the speed and direction of the horizontal wind. V-component of wind m s-1 The V-component of wind is the meridional component of the wind valid for the grid area at the corresponding model level. By model convention, a negative(positive) value indicates air moving towards the south (north). This variable can be combined with the U-component of wind to give the speed and direction of the horizontal wind. V-component of wind m s-1 The V-component of wind is the meridional component of the wind valid for the grid area at the corresponding model level. By model convention, a negative(positive) value indicates air moving towards the south (north). This variable can be combined with the U-component of wind to give the speed and direction of the horizontal wind. 402 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/eu-hydro-river-network-database-2006-2012-vector-europe https://land.copernicus.eu/imagery-in-situ/eu-hydro/eu-hydro-river-network-database EU-Hydro River Network Database 2006-2012 (vector), Europe - version 1.3, Nov. 2020 EU-Hydro is a dataset for all EEA38 countries and the United Kingdom providing photo-interpreted river network, consistent of surface interpretation of water bodies (lakes and wide rivers), and a drainage model (also called Drainage Network), derived from EU-DEM, with catchments and drainage lines and nodes. The EU-Hydro dataset is distributed in separate files (river network and drainage network) for each of the 35 major basins of the EEA38 + UK area, in GDB and GPKG formats. The production of EU-Hydro and the derived layers was coordinated by the European Environment Agency in the frame of the EU Copernicus programme. 403 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/medium-resolution-vegetation-phenology-and-productivity-3 https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum_d1-0.pdf Medium Resolution Vegetation Phenology and Productivity: Start-of-season date (raster 500m), Oct. 2022 The raster file is the time series of the start of the vegetation growing season. The start of the growing season time-series is based on the time series of the Plant Phenology Index (PPI) derived from the MODIS BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR). The PPI index is optimized for efficient monitoring of vegetation phenology and is derived from the source MODIS data using radiative transfer solutions applied to the reflectance in visible-red and near infrared spectral domains. The start of season indicator is based on calculating the start of the vegetation growing season from the annual PPI temporal curve using the TIMESAT software for each year between and including 2000 and 2021. The Start-of-Season Date (SOSD), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Start-of-Season Date (SOSD) marks the date when the vegetation growing season starts in the time profile of the Plant Phenology Index (PPI). The start-of-season occurs, by definition, when the PPI value reaches 25% of the season amplitude during the green-up period. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Start-of-Season Date (SOSD) is one of the 13 parameters. The full list is available in the Product User Manual: https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… The Start-of-Season Date (SOSD) time series dataset is made available as raster files with 500x 500m resolution, in ETRS89-LAEA projection corresponding to the MCD43 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2000 onwards. It is updated in the first quarter of each year. The full on-line access to open and free data for this resource will be made available by the end of 2022. Until then the data will be made available 'on-demand' by filling in the form at: https://land.copernicus.eu/contact-form https://land.copernicus.eu/contact-form 404 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-chlorophyll-trend-map-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_HEALTH_CHL_GLOBAL_OCEANCOLOUR_trend Global Ocean Chlorophyll-a trend map from Observations Reprocessing DEFINITION The trend map is derived from version 5 of the global climate-quality chlorophyll time series produced by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020) and distributed by CMEMS. The trend detection method is based on the Census-I algorithm as described by Vantrepotte et al. (2009), where the time series is decomposed as a fixed seasonal cycle plus a linear trend component plus a residual component. The linear trend is expressed in % year -1, and its level of significance (p) calculated using a t-test. Only significant trends (p < 0.05) are included. CONTEXT Phytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration is the most widely used measure of the concentration of phytoplankton present in the ocean. Drivers for chlorophyll variability range from small-scale seasonal cycles to long-term climate oscillations and, most importantly, anthropogenic climate change. Due to such diverse factors, the detection of climate signals requires a long-term time series of consistent, well-calibrated, climate-quality data record. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series. CMEMS KEY FINDINGS The average global trend for the 1997-2021 period was 0.51% per year, with a maximum value of 25% per year and a minimum value of -6.1% per year. Positive trends are pronounced in the high latitudes of both northern and southern hemispheres. The significant increases in chlorophyll reported in 2016-2017 (Sathyendranath et al., 2018b) for the Atlantic and Pacific oceans at high latitudes appear to be plateauing after the 2021 extension. The negative trends shown in equatorial waters in 2020 appear to be remain consistent in 2021. DOI (product):https://doi.org/10.48670/moi-00230 https://doi.org/10.48670/moi-00230 405 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-atlantic-ocean-eutrophication-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_eutrophication North Atlantic Ocean Eutrophication from Observations Reprocessing DEFINITION We have derived an annual eutrophication and eutrophication indicator map for the North Atlantic Ocean using satellite-derived chlorophyll concentration. Using the satellite-derived chlorophyll products distributed in the regional North Atlantic CMEMS MY Ocean Colour dataset (OC- CCI), we derived P90 and P10 daily climatologies. The time period selected for the climatology was 1998-2017. For a given pixel, P90 and P10 were defined as dynamic thresholds such as 90% of the 1998-2017 chlorophyll values for that pixel were below the P90 value, and 10% of the chlorophyll values were below the P10 value. To minimise the effect of gaps in the data in the computation of these P90 and P10 climatological values, we imposed a threshold of 25% valid data for the daily climatology. For the 20-year 1998-2017 climatology this means that, for a given pixel and day of the year, at least 5 years must contain valid data for the resulting climatological value to be considered significant. Pixels where the minimum data requirements were met were not considered in further calculations. We compared every valid daily observation over 2021 with the corresponding daily climatology on a pixel-by-pixel basis, to determine if values were above the P90 threshold, below the P10 threshold or within the [P10, P90] range. Values above the P90 threshold or below the P10 were flagged as anomalous. The number of anomalous and total valid observations were stored during this process. We then calculated the percentage of valid anomalous observations (above/below the P90/P10 thresholds) for each pixel, to create percentile anomaly maps in terms of % days per year. Finally, we derived an annual indicator map for eutrophication levels: if 25% of the valid observations for a given pixel and year were above the P90 threshold, the pixel was flagged as eutrophic. Similarly, if 25% of the observations for a given pixel were below the P10 threshold, the pixel was flagged as oligotrophic. CONTEXT Eutrophication is the process by which an excess of nutrients – mainly phosphorus and nitrogen – in a water body leads to increased growth of plant material in an aquatic body. Anthropogenic activities, such as farming, agriculture, aquaculture and industry, are the main source of nutrient input in problem areas (Jickells, 1998; Schindler, 2006; Galloway et al., 2008). Eutrophication is an issue particularly in coastal regions and areas with restricted water flow, such as lakes and rivers (Howarth and Marino, 2006; Smith, 2003). The impact of eutrophication on aquatic ecosystems is well known: nutrient availability boosts plant growth – particularly algal blooms – resulting in a decrease in water quality (Anderson et al., 2002; Howarth et al.; 2000). This can, in turn, cause death by hypoxia of aquatic organisms (Breitburg et al., 2018), ultimately driving changes in community composition (Van Meerssche et al., 2019). Eutrophication has also been linked to changes in the pH (Cai et al., 2011, Wallace et al. 2014) and depletion of inorganic carbon in the aquatic environment (Balmer and Downing, 2011). Oligotrophication is the opposite of eutrophication, where reduction in some limiting resource leads to a decrease in photosynthesis by aquatic plants, reducing the capacity of the ecosystem to sustain the higher organisms in it. Eutrophication is one of the more long-lasting water quality problems in Europe (OSPAR ICG-EUT, 2017), and is on the forefront of most European Directives on water-protection. Efforts to reduce anthropogenically-induced pollution resulted in the implementation of the Water Framework Directive (WFD) in 2000. CMEMS KEY FINDINGS The coastal and shelf waters, especially between 30 and 400N that showed active oligotrophication flags for 2020 have reduced in 2021 and a reversal to eutrophic flags can be seen in places. Again, the eutrophication index is positive only for a small number of coastal locations just north of 40oN in 2021, however south of 40oN there has been a significant increase in eutrophic flags, particularly around the Azores. In general, the 2021 indicator map showed an increase in oligotrophic areas in the Northern Atlantic and an increase in eutrophic areas in the Southern Atlantic. The Third Integrated Report on the Eutrophication Status of the OSPAR Maritime Area (OSPAR ICG-EUT, 2017) reported an improvement from 2008 to 2017 in eutrophication status across offshore and outer coastal waters of the Greater North Sea, with a decrease in the size of coastal problem areas in Denmark, France, Germany, Ireland, Norway and the United Kingdom. DOI (product):https://doi.org/10.48670/moi-00195 https://doi.org/10.48670/moi-00195 406 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/medium-resolution-vegetation-phenology-and-productivity-7 https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum_d1-0.pdf Medium Resolution Vegetation Phenology and Productivity: Peak of season (raster 500m), Oct. 2022 The peak of season (POS), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European Medium Resolution Vegetation Phenology and Productivity (MR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The peak of season (POS) expresses the maximum Plant Phenology Index (PPI) values reached during the season. It is computed as the mean value of the times for which, respectively, the left edge has increased to the 80 % level and the right edge has decreased to the 80 % level. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The peak of season (POS) is one of the 13 parameters. The full list is available in the Product User Manual: https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… The peak of season (POS) time series dataset is made available as raster files with 500x 500m resolution, in ETRS89-LAEA projection corresponding to the MCD43 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2000 onwards. It is updated in the first quarter of each year. The full on-line access to open and free data for this resource will be made available by the end of 2022. Until then the data will be made available 'on-demand' by filling in the form at: https://land.copernicus.eu/contact-form https://land.copernicus.eu/contact-form 407 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-sea-ice https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-ice satellite-sea-ice This dataset provides daily values for sea ice concentration, sea ice edge and sea ice type and monthly values for sea ice thickness. These four variables are important markers for climate change studies since sea ice greatly influences the surface albedo and exchanges of energy, moisture and carbon. The sea-ice distribution, including polynyas and margins, also has an important influence on marine ecosystems. Changes in the distribution of sea ice affect these ecosystems and a number of activities such as shipping, logistic and tourist operations. sea ice concentration sea ice edge sea ice type sea ice thickness Sea ice concentration, sea ice edge, and sea ice type were computed from satellite passive microwave brightness temperatures from the series of SMMR, SSM/I and SSMIS sensors. Sea ice thickness was computed from Ku-Band radar altimeter measurements collected by the Envisat and CryoSat-2 satellite missions. Ice thicknesses from Envisat satellite (October 2002 to October 2010) have less coverage and higher uncertainty than thicknesses from CryoSat-2 satellite (November 2010 - March 2015). However the combined dataset provides a valuable and unique observational record of sea ice variability. From 1978 up to April 2015 the data records provided by this dataset have sufficient length, consistency, and continuity to detect climate variability and change. From April 2015 onwards, satellite data were processed using the same algorithms and processing environment but consistency and continuity have not been extensively verified. This dataset is produced on behalf of C3S, with the exception of sea ice concentration which is produced by the EUMETSAT Satellite Application Facility on Ocean and Sea Ice (OSI SAF). DATA DESCRIPTION Data type Gridded Projection Lambert azimuthal equal area Horizontal coverage Sea ice concentration and edge: global ocean split in Northern and Southern Hemispheres (Lambert EASE/EASE2 projection) Sea ice thickness and type: Northern Hemisphere (Lambert EASE2 projection) Horizontal resolution Sea ice concentration and edge: 12.5 km grid resolution (true spatial resolution: 40-50 km and ~15 km, respectively) Sea ice thickness and type: 25 km grid resolution (true spatial resolution: 1-10 km and 40-70 km, respectively) Temporal coverage Sea ice concentration: 1978 to 2019 Sea ice thickness: 2002 to present Sea ice edge: 1979 to present Sea ice type: 1979 to present Temporal resolution Sea ice concentration, edge and type: daily (every second day during 1978-1987) Sea ice thickness: monthly and for Arctic winter months only (October through April) File format NetCDF Versions New versions expected in 2020 for sea ice concentratio, and in 2021 for thickness, edge and type Update frequency Monthly for thickness. Daily for edge and type. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert azimuthal equal area Projection Lambert azimuthal equal area Horizontal coverage Sea ice concentration and edge: global ocean split in Northern and Southern Hemispheres (Lambert EASE/EASE2 projection) Sea ice thickness and type: Northern Hemisphere (Lambert EASE2 projection) Horizontal coverage Sea ice concentration and edge: global ocean split in Northern and Southern Hemispheres (Lambert EASE/EASE2 projection) Sea ice thickness and type: Northern Hemisphere (Lambert EASE2 projection) Sea ice concentration and edge: global ocean split in Northern and Southern Hemispheres (Lambert EASE/EASE2 projection) Sea ice thickness and type: Northern Hemisphere (Lambert EASE2 projection) Horizontal resolution Sea ice concentration and edge: 12.5 km grid resolution (true spatial resolution: 40-50 km and ~15 km, respectively) Sea ice thickness and type: 25 km grid resolution (true spatial resolution: 1-10 km and 40-70 km, respectively) Horizontal resolution Sea ice concentration and edge: 12.5 km grid resolution (true spatial resolution: 40-50 km and ~15 km, respectively) Sea ice thickness and type: 25 km grid resolution (true spatial resolution: 1-10 km and 40-70 km, respectively) Sea ice concentration and edge: 12.5 km grid resolution (true spatial resolution: 40-50 km and ~15 km, respectively) Sea ice thickness and type: 25 km grid resolution (true spatial resolution: 1-10 km and 40-70 km, respectively) Temporal coverage Sea ice concentration: 1978 to 2019 Sea ice thickness: 2002 to present Sea ice edge: 1979 to present Sea ice type: 1979 to present Temporal coverage Sea ice concentration: 1978 to 2019 Sea ice thickness: 2002 to present Sea ice edge: 1979 to present Sea ice type: 1979 to present Sea ice concentration: 1978 to 2019 Sea ice thickness: 2002 to present Sea ice edge: 1979 to present Sea ice type: 1979 to present Temporal resolution Sea ice concentration, edge and type: daily (every second day during 1978-1987) Sea ice thickness: monthly and for Arctic winter months only (October through April) Temporal resolution Sea ice concentration, edge and type: daily (every second day during 1978-1987) Sea ice thickness: monthly and for Arctic winter months only (October through April) Sea ice concentration, edge and type: daily (every second day during 1978-1987) Sea ice thickness: monthly and for Arctic winter months only (October through April) File format NetCDF File format NetCDF Versions New versions expected in 2020 for sea ice concentratio, and in 2021 for thickness, edge and type Versions New versions expected in 2020 for sea ice concentratio, and in 2021 for thickness, edge and type Update frequency Monthly for thickness. Daily for edge and type. Update frequency Monthly for thickness. Daily for edge and type. Monthly for thickness. Daily for edge and type. MAIN VARIABLES Name Units Description Sea ice concentration % Fraction of the area of the grid cell containing sea ice. Sea ice edge Dimensionless Classification which indicates the character of the sea surface. Possible values are: open-water, open-ice and closed-ice, which correspond respectively to values of the sea ice concentration less than 30%, between 30% and 70%, and larger than 70%. Sea ice thickness m Mean thickness of the sea ice layer in the area covered by ice. Sea ice type Dimensionless Classification which indicates the character of the ice surface. Values can be either first-year or multi-year, where first-year corresponds to seasonal ice that has formed since last melting season, and multi-year corresponds to older ice that has survived at least one melting season. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Sea ice concentration % Fraction of the area of the grid cell containing sea ice. Sea ice concentration % Fraction of the area of the grid cell containing sea ice. Sea ice edge Dimensionless Classification which indicates the character of the sea surface. Possible values are: open-water, open-ice and closed-ice, which correspond respectively to values of the sea ice concentration less than 30%, between 30% and 70%, and larger than 70%. Sea ice edge Dimensionless Classification which indicates the character of the sea surface. Possible values are: open-water, open-ice and closed-ice, which correspond respectively to values of the sea ice concentration less than 30%, between 30% and 70%, and larger than 70%. Sea ice thickness m Mean thickness of the sea ice layer in the area covered by ice. Sea ice thickness m Mean thickness of the sea ice layer in the area covered by ice. Sea ice type Dimensionless Classification which indicates the character of the ice surface. Values can be either first-year or multi-year, where first-year corresponds to seasonal ice that has formed since last melting season, and multi-year corresponds to older ice that has survived at least one melting season. Sea ice type Dimensionless Classification which indicates the character of the ice surface. Values can be either first-year or multi-year, where first-year corresponds to seasonal ice that has formed since last melting season, and multi-year corresponds to older ice that has survived at least one melting season. RELATED VARIABLES A number of variables accounting for the uncertainty on the data provided are also included in the files along the main variable. They provide estimates on possible variations of the values of the main variables due to changes in processing and sampling algorithms. RELATED VARIABLES RELATED VARIABLES A number of variables accounting for the uncertainty on the data provided are also included in the files along the main variable. They provide estimates on possible variations of the values of the main variables due to changes in processing and sampling algorithms. A number of variables accounting for the uncertainty on the data provided are also included in the files along the main variable. They provide estimates on possible variations of the values of the main variables due to changes in processing and sampling algorithms. 408 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-hydrology-climate-explorer https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-hydrology-climate-explorer app-hydrology-climate-explorer This application explores hydrological and climate related variables for European river catchment basins based on regional climate projections from a multi-model ensemble. Projections of water-related climate indicators are important for the water sector, in the fields of water allocation, flood management, ecological status and industrial water use, and to inform adaptation strategies in order to mitigate against the effects of climate change. This application is underpinned by climate projections of hydrological variables based on regional climate models from the European Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX) ensemble, and the hydrological models E-HYPE and VIC. The bias-adjusted regional climate model outputs are used as forcing for the hydrological models to simulate hydrological variables. Several configurations and parameterisations of the E-HYPE model are used to explore sensitivity of climate projections to the configuration of the hydrological-model. The interactive map explores a range of climate variables for various time periods and emission scenarios. Clicking on a catchment or grid point produces more detailed visualisations of the spread between climate models for each hydrological model. User-selectable parameters User-selectable parameters Indicator type: the type of hydrological or climate projections to visualise on the interactive map Impact indicator: the specific hydrological or climate variable to visualise on the interactive map Climate models: the combination of Global Climate Model (GCM) and Regional Climate Model (RCM) to visualise in the map Hydrological model: the hydrological model(s) used to produce the hydrological data Time period: the historical or projected time period over which to explore the selected variable Emissions scenario: the Representative Concentration Pathway (RCP) greenhouse gas emissions scenario to visualise for projections data Indicator type: the type of hydrological or climate projections to visualise on the interactive map Impact indicator: the specific hydrological or climate variable to visualise on the interactive map Climate models: the combination of Global Climate Model (GCM) and Regional Climate Model (RCM) to visualise in the map Hydrological model: the hydrological model(s) used to produce the hydrological data Time period: the historical or projected time period over which to explore the selected variable Emissions scenario: the Representative Concentration Pathway (RCP) greenhouse gas emissions scenario to visualise for projections data INPUT VARIABLES Name Units Description Source 2m air temperature K (ECV) oC (CII) The ambient air temperature close to the surface. The essential climate variable (ECV) data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset. The climate impact indicator (CII) of 2m air temperature is defined as the monthly/annual mean of the daily mean 2m air temperature, averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Temperature and precipitation climate impact indicators Aridity actual Dimensionless Aridity actual is calculated as the monthly mean values of the ratio between actual evapotranspiration and precipitation over a 30 year period. Actual evapotranspiration is the modelled evapotranspiration computed only with available water. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Aridity potential Dimensionless Aridity potential is calculated as the monthly mean values of the ratio between potential evapotranspiration and precipitation over a 30 year period. Potential evapotranspiration is the modelled evapotranspiration when there is abundant water. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Flood recurrence m3 s-1 Return values of annual maximum river discharge. Data are provided as the 2, 5, 10 and 50 year return period of annual daily maximum river discharge estimated using a Gumbel distribution. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Highest 5-day precipitation amount mm 5day-1 Highest five-day precipitation amount is defined as the maximum of 5-day precipitation totals. The value is given as a maximum over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Temperature and precipitation climate impact indicators Longest dry spells days Longest dry spells is defined as the maximum number of consecutive dry days (dry day: daily precipitation < 1mm) over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Temperature and precipitation climate impact indicators Maximum river discharge m3 s-1 Maximum river discharge is calculated as the mean annual daily maximum discharge over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Mean runoff mm month-1 Runoff is defined as the sum of surface and subsurface runoff to streams for each grid cell or catchment. The indicator is calculated as the monthly or annual mean values of daily runoff averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Mean soil moisture Dimensionless Soil moisture is the water stored in the soil and is affected by precipitation, temperature, soil characteristics, and more. The soil moisture is defined slightly differently in different hydrological models, and is here generally defined as soil moisture in the root zone as fraction of the field capacity volume. Data are provided as monthly or annual mean values, averaged over a 30 years period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Minimum river discharge m3 s-1 Minimum river discharge is calculated as the mean annual daily minimum discharge over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Number of dry spells Number of spells Number of dry spells is defined as the number of dry periods (dry day: daily precipitation < 1mm) for more than 5 days for a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Temperature and precipitation climate impact indicators Precipitation kg m-2 s-1 (ECV) mm day-1 (CII) Precipitation is defined as the deposition of water to the Earth"s surface in the form of rain, snow, ice or hail. The essential climate variable (ECV) data is given as the mass of water per unit area and time. The data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset. The climate impact indicator (CII) of precipitation is defined as the monthly/annual mean of the liquid water equivalent daily precipitation, averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Temperature and precipitation climate impact indicators River discharge m3 s-1 Volume rate of water flow that is transported through a given cross-sectional area. It is synonymous to streamflow. The essential climate variable (ECV) data are provided at daily resolution. The climate impact indicator (CII) of river discharge is calculated as the monthly or annual mean values of daily runoff averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Total Nitrogen concentration in catchments mg L-1 Nitrogen concentration is the mass of nitrogen divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total nitrogen concentration, from a catchment averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Total Nitrogen concentration in local streams mg L-1 Nitrogen concentration is the mass of nitrogen divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total nitrogen concentration, from a local stream averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Total Nitrogen load in catchments kg year-1 kg month-1 Nitrogen load is the product of the river discharge volume and the nitrogen concentrations. The indicator is calculated as the annual (kg/year) or monthly (kg/month) mean values of total nitrogen load from a catchment averaged over of a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Total Phosphorus concentration in catchments mg L-1 Phosphorus concentration is the mass of phosphorus divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total phosphorus concentration, from a catchment averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Total Phosphorus concentration in local streams mg L-1 Phosphorus concentration is the mass of phosphorus divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total phosphorus concentration, from a local stream averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Total Phosphorus load in catchments kg year-1 kg month-1 Phosphorus load is the product of the river discharge volume and the phosphorus concentrations. The indicator is calculated as the annual (kg/year) or monthly (kg/month) mean values of total phosphorus load from a catchment averaged over of a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Water temperature in catchments oC Water temperature is the simulated water temperature in a catchment. The indicator is calculated as mean annual values of water temperature for a 30 years period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Hydrology climate impact indicators Water temperature in local streams oC Water temperature is the simulated water temperature in local streams. The indicator is calculated as mean annual values of water temperature for a 30 years period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Hydrology climate impact indicators Wetness potential mm month-1 Wetness potential is calculated as the monthly mean values of precipitation minus potential evapotranspiration averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Hydrology climate impact indicators INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source 2m air temperature K (ECV) oC (CII) The ambient air temperature close to the surface. The essential climate variable (ECV) data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset. The climate impact indicator (CII) of 2m air temperature is defined as the monthly/annual mean of the daily mean 2m air temperature, averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Temperature and precipitation climate impact indicators 2m air temperature K (ECV) oC (CII) K (ECV) oC (CII) The ambient air temperature close to the surface. The essential climate variable (ECV) data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset. The climate impact indicator (CII) of 2m air temperature is defined as the monthly/annual mean of the daily mean 2m air temperature, averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). The ambient air temperature close to the surface. The essential climate variable (ECV) data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset. The climate impact indicator (CII) of 2m air temperature is defined as the monthly/annual mean of the daily mean 2m air temperature, averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Temperature and precipitation climate impact indicators Temperature and precipitation climate impact indicators Aridity actual Dimensionless Aridity actual is calculated as the monthly mean values of the ratio between actual evapotranspiration and precipitation over a 30 year period. Actual evapotranspiration is the modelled evapotranspiration computed only with available water. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Aridity actual Dimensionless Aridity actual is calculated as the monthly mean values of the ratio between actual evapotranspiration and precipitation over a 30 year period. Actual evapotranspiration is the modelled evapotranspiration computed only with available water. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Aridity potential Dimensionless Aridity potential is calculated as the monthly mean values of the ratio between potential evapotranspiration and precipitation over a 30 year period. Potential evapotranspiration is the modelled evapotranspiration when there is abundant water. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Aridity potential Dimensionless Aridity potential is calculated as the monthly mean values of the ratio between potential evapotranspiration and precipitation over a 30 year period. Potential evapotranspiration is the modelled evapotranspiration when there is abundant water. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Flood recurrence m3 s-1 Return values of annual maximum river discharge. Data are provided as the 2, 5, 10 and 50 year return period of annual daily maximum river discharge estimated using a Gumbel distribution. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Flood recurrence m3 s-1 Return values of annual maximum river discharge. Data are provided as the 2, 5, 10 and 50 year return period of annual daily maximum river discharge estimated using a Gumbel distribution. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Highest 5-day precipitation amount mm 5day-1 Highest five-day precipitation amount is defined as the maximum of 5-day precipitation totals. The value is given as a maximum over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Temperature and precipitation climate impact indicators Highest 5-day precipitation amount mm 5day-1 Highest five-day precipitation amount is defined as the maximum of 5-day precipitation totals. The value is given as a maximum over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Temperature and precipitation climate impact indicators Temperature and precipitation climate impact indicators Longest dry spells days Longest dry spells is defined as the maximum number of consecutive dry days (dry day: daily precipitation < 1mm) over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Temperature and precipitation climate impact indicators Longest dry spells days Longest dry spells is defined as the maximum number of consecutive dry days (dry day: daily precipitation < 1mm) over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Temperature and precipitation climate impact indicators Temperature and precipitation climate impact indicators Maximum river discharge m3 s-1 Maximum river discharge is calculated as the mean annual daily maximum discharge over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Maximum river discharge m3 s-1 Maximum river discharge is calculated as the mean annual daily maximum discharge over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Mean runoff mm month-1 Runoff is defined as the sum of surface and subsurface runoff to streams for each grid cell or catchment. The indicator is calculated as the monthly or annual mean values of daily runoff averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Mean runoff mm month-1 Runoff is defined as the sum of surface and subsurface runoff to streams for each grid cell or catchment. The indicator is calculated as the monthly or annual mean values of daily runoff averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Mean soil moisture Dimensionless Soil moisture is the water stored in the soil and is affected by precipitation, temperature, soil characteristics, and more. The soil moisture is defined slightly differently in different hydrological models, and is here generally defined as soil moisture in the root zone as fraction of the field capacity volume. Data are provided as monthly or annual mean values, averaged over a 30 years period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Mean soil moisture Dimensionless Soil moisture is the water stored in the soil and is affected by precipitation, temperature, soil characteristics, and more. The soil moisture is defined slightly differently in different hydrological models, and is here generally defined as soil moisture in the root zone as fraction of the field capacity volume. Data are provided as monthly or annual mean values, averaged over a 30 years period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Minimum river discharge m3 s-1 Minimum river discharge is calculated as the mean annual daily minimum discharge over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Minimum river discharge m3 s-1 Minimum river discharge is calculated as the mean annual daily minimum discharge over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Number of dry spells Number of spells Number of dry spells is defined as the number of dry periods (dry day: daily precipitation < 1mm) for more than 5 days for a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Temperature and precipitation climate impact indicators Number of dry spells Number of spells Number of dry spells is defined as the number of dry periods (dry day: daily precipitation < 1mm) for more than 5 days for a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Temperature and precipitation climate impact indicators Temperature and precipitation climate impact indicators Precipitation kg m-2 s-1 (ECV) mm day-1 (CII) Precipitation is defined as the deposition of water to the Earth"s surface in the form of rain, snow, ice or hail. The essential climate variable (ECV) data is given as the mass of water per unit area and time. The data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset. The climate impact indicator (CII) of precipitation is defined as the monthly/annual mean of the liquid water equivalent daily precipitation, averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Temperature and precipitation climate impact indicators Precipitation kg m-2 s-1 (ECV) mm day-1 (CII) kg m-2 s-1 (ECV) mm day-1 (CII) Precipitation is defined as the deposition of water to the Earth"s surface in the form of rain, snow, ice or hail. The essential climate variable (ECV) data is given as the mass of water per unit area and time. The data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset. The climate impact indicator (CII) of precipitation is defined as the monthly/annual mean of the liquid water equivalent daily precipitation, averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Precipitation is defined as the deposition of water to the Earth"s surface in the form of rain, snow, ice or hail. The essential climate variable (ECV) data is given as the mass of water per unit area and time. The data originate from EURO-CORDEX RCM simulations and are bias adjusted using the EFAS-Meteo reference dataset. The climate impact indicator (CII) of precipitation is defined as the monthly/annual mean of the liquid water equivalent daily precipitation, averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Temperature and precipitation climate impact indicators Temperature and precipitation climate impact indicators River discharge m3 s-1 Volume rate of water flow that is transported through a given cross-sectional area. It is synonymous to streamflow. The essential climate variable (ECV) data are provided at daily resolution. The climate impact indicator (CII) of river discharge is calculated as the monthly or annual mean values of daily runoff averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators River discharge m3 s-1 Volume rate of water flow that is transported through a given cross-sectional area. It is synonymous to streamflow. The essential climate variable (ECV) data are provided at daily resolution. The climate impact indicator (CII) of river discharge is calculated as the monthly or annual mean values of daily runoff averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Volume rate of water flow that is transported through a given cross-sectional area. It is synonymous to streamflow. The essential climate variable (ECV) data are provided at daily resolution. The climate impact indicator (CII) of river discharge is calculated as the monthly or annual mean values of daily runoff averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Total Nitrogen concentration in catchments mg L-1 Nitrogen concentration is the mass of nitrogen divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total nitrogen concentration, from a catchment averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Total Nitrogen concentration in catchments mg L-1 Nitrogen concentration is the mass of nitrogen divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total nitrogen concentration, from a catchment averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Total Nitrogen concentration in local streams mg L-1 Nitrogen concentration is the mass of nitrogen divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total nitrogen concentration, from a local stream averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Total Nitrogen concentration in local streams mg L-1 Nitrogen concentration is the mass of nitrogen divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total nitrogen concentration, from a local stream averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Total Nitrogen load in catchments kg year-1 kg month-1 Nitrogen load is the product of the river discharge volume and the nitrogen concentrations. The indicator is calculated as the annual (kg/year) or monthly (kg/month) mean values of total nitrogen load from a catchment averaged over of a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Total Nitrogen load in catchments kg year-1 kg month-1 kg year-1 kg month-1 Nitrogen load is the product of the river discharge volume and the nitrogen concentrations. The indicator is calculated as the annual (kg/year) or monthly (kg/month) mean values of total nitrogen load from a catchment averaged over of a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Total Phosphorus concentration in catchments mg L-1 Phosphorus concentration is the mass of phosphorus divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total phosphorus concentration, from a catchment averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Total Phosphorus concentration in catchments mg L-1 Phosphorus concentration is the mass of phosphorus divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total phosphorus concentration, from a catchment averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Total Phosphorus concentration in local streams mg L-1 Phosphorus concentration is the mass of phosphorus divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total phosphorus concentration, from a local stream averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Total Phosphorus concentration in local streams mg L-1 Phosphorus concentration is the mass of phosphorus divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total phosphorus concentration, from a local stream averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Total Phosphorus load in catchments kg year-1 kg month-1 Phosphorus load is the product of the river discharge volume and the phosphorus concentrations. The indicator is calculated as the annual (kg/year) or monthly (kg/month) mean values of total phosphorus load from a catchment averaged over of a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Total Phosphorus load in catchments kg year-1 kg month-1 kg year-1 kg month-1 Phosphorus load is the product of the river discharge volume and the phosphorus concentrations. The indicator is calculated as the annual (kg/year) or monthly (kg/month) mean values of total phosphorus load from a catchment averaged over of a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Water temperature in catchments oC Water temperature is the simulated water temperature in a catchment. The indicator is calculated as mean annual values of water temperature for a 30 years period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Hydrology climate impact indicators Water temperature in catchments oC Water temperature is the simulated water temperature in a catchment. The indicator is calculated as mean annual values of water temperature for a 30 years period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Water temperature in local streams oC Water temperature is the simulated water temperature in local streams. The indicator is calculated as mean annual values of water temperature for a 30 years period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Hydrology climate impact indicators Water temperature in local streams oC Water temperature is the simulated water temperature in local streams. The indicator is calculated as mean annual values of water temperature for a 30 years period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators Wetness potential mm month-1 Wetness potential is calculated as the monthly mean values of precipitation minus potential evapotranspiration averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Hydrology climate impact indicators Wetness potential mm month-1 Wetness potential is calculated as the monthly mean values of precipitation minus potential evapotranspiration averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Hydrology climate impact indicators Hydrology climate impact indicators 409 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-sea-ice-concentration-and-thickness-charts http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_004 Baltic Sea - Sea Ice Concentration and Thickness Charts Short description: For the Baltic Sea- The operational sea ice service at FMI provides ice parameters over the Baltic Sea. The parameters are based on ice chart produced on daily basis during the Baltic Sea ice season and show the ice concentration in a 1 km grid. Ice thickness chart (ITC) is a product based on the most recent available ice chart (IC) and a SAR image. The SAR data is used to update the ice information in the IC. The ice regions in the IC are updated according to a SAR segmentation and new ice thickness values are assigned to each SAR segment based on the SAR backscattering and the ice IC thickness range at that location. DOI (product) :https://doi.org/10.48670/moi-00132 https://doi.org/10.48670/moi-00132 410 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/small-woody-features-2015-raster-5-m-europe-3-yearly-nov https://land.copernicus.eu/pan-european/high-resolution-layers/small-woody-features/small-woody-features-2015 Small Woody Features 2015 (raster 5 m), Europe, 3-yearly - Nov. 2019 The HRL Small Woody Features (SWF) is a new Copernicus Land Monitoring Service (CMLS) product, which provides harmonized information on linear structures such as hedgerows, as well as patches (200 m² ≤ area ≤ 5000 m²) of woody features across the EEA39 countries. Small woody landscape features are important vectors of biodiversity and provide information on fragmentation of habitats with a direct potential for restoration while also providing a link to hazard protection and green infrastructure, amongst others. The SWF layer contains woody linear, and small patchy elements, but is not differentiated into trees, hedges, bushes and scrub. The spatial pattern are limited to linear structures and isolated patches (patchy structures) on the basis of geometric characteristics. Additional Woody Features (AWF) are also included in this product. They consist of woody structures that do not fulfil the SWF geometric specifications but which are connected to valid SWFs structures. VHR imagery (DEIMOS-2, Pleiades 1A, Pleiades 1B, GeoEye-1, SPOT 6, SPOT 7, WorldView-2, WorldView-3 images from 2015) made available in the ESA Copernicus DWH are the main data source for the detection of small woody features identifiable within the given image resolution. The dataset is available for the 2015 reference year and is produced in three different formats. This metadata corresponds to the SWF 5m spatial resolution raster layer, which distinguishes between SWF (code =1) and AWF (code =3) ). This layer is derived from the SWF vector product in order to be more in line with other HR layers, and for allowing raster processing of the results. It describes the SWF landscape according to the high resolution of the input data, but without taking into account the possible small geometric inaccuracy of the vector product (due to VHR geometric imprecision, automatic processing such as smoothing, etc.). The geometric resolution is consistent with the EEA reference grid. 411 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-daily-gridded-reprocessed-l3-sea-surface http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=WIND_GLO_WIND_L3_REP_OBSERVATIONS_012_005 Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer Short description: For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products: *0.5 degrees grid for the 50 km scatterometer L2 inputs, *0.25 degrees grid based on 25 km scatterometer swath observations, *and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately. The reported wind is stress-equivalent wind with wind stress, wind stress curl and divergence. The REP L3 products follow the REP availability of the EUMETSAT OSI SAF L2 products and are available for: The ASCAT scatterometer on MetOp-A and Metop-B at 0.125 and 0.25 degrees; The Seawinds scatterometer on QuikSCAT at 0.25 and 0.5 degrees; The AMI scatterometer on ERS-1 and ERS-2 at 0.25 degrees; The OSCAT scatterometer on Oceansat-2 at 0.25 and 0.5 degrees; DOI (product) :https://doi.org/10.48670/moi-00183 https://doi.org/10.48670/moi-00183 412 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-cora-situ-observations-yearly-delivery http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_GLO_PHY_TS_DISCRETE_MY_013_001 Global Ocean- CORA- In-situ Observations Yearly Delivery in Delayed Mode Short description: For the Global Ocean- In-situ observation yearly delivery in delayed mode. The In Situ delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for temperature and salinity measurements. These data are collected from main global networks (Argo, GOSUD, OceanSITES, World Ocean Database) completed by European data provided by EUROGOOS regional systems and national system by the regional INS TAC components. It is updated on a yearly basis. This version is a merged product between the previous verion of CORA and EN4 distributed by the Met Office for the period 1950-1990. DOI (product) :https://doi.org/10.17882/46219 https://doi.org/10.17882/46219 413 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-biodiversity-climate-suitability-explorer https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-biodiversity-climate-suitability-explorer app-biodiversity-climate-suitability-explorer This application visualises 43 bioclimatic indicators that provide relevant information for a variety of biodiversity and wildlife conservation applications. Users can further explore the indicators at the country and region level and estimate their climate suitability to host an ecosystem or species based on a simple climate envelope model. The application uses data from the Global bioclimatic indicators from 1950 to 2100 derived from climate projections and Downscaled bioclimatic indicators for selected regions from 1950 to 2100 derived from climate projections. Both of these datasets are based on CMIP5 model output that has been bias-adjusted to ERA5 and represent the state of the art in this field. Global bioclimatic indicators from 1950 to 2100 derived from climate projections Downscaled bioclimatic indicators for selected regions from 1950 to 2100 derived from climate projections The simple envelope model uses a minimum and maximum threshold applied to each of the CMIP5 model fields where each grid-cell is assigned a value of one if within the thresholds or zero if not. The climate suitability is then represented as the mean accross the models for the maps or the median with the 15-85 percentile range accross the models for the time-series. The interactive livemap shows the 20-year mean of the selected indicator for the historical (1980-1999), near-future (2041-2060) and far-future (2081-2100) for two climate scenarios, i.e. the Representative Concentration Pathways RCP4.5 (moderate emission scenario) and RCP8.5 (high emission scenario). Users can click on a country, or Natura 2000 region, to open a detailed view of that country/region. Upon first click the detailed view provides a time-series of the indicator for the period 1980 to 2090. If a minimum and maximum threshold is provided then the detailed view will produce a time-series and carousel of maps which display the climate suitability based on these thresholds. Climate suitability calculations may take several minutes to calculate, therefore users can type "default" into both input boxes to use the variable specific default thresholds for a pre-calculated result. The default thresholds are provided in the product user guide. User-selectable parameters User-selectable parameters Indicator: one of the 43 indicators listed below Region: Global - standard resolution Europe - high resolution Central Africa - high resolution Period: A subset of indicators allows exploration by month Annual January to December Indicator: one of the 43 indicators listed below Indicator Region: Global - standard resolution Europe - high resolution Central Africa - high resolution Region Global - standard resolution Europe - high resolution Central Africa - high resolution Global - standard resolution Europe - high resolution Central Africa - high resolution Period: A subset of indicators allows exploration by month Annual January to December Period Annual January to December Annual January to December Given the complexity of the variables used in this application, users are advised to read the product user guide for a full understanding of what the results represent. Given the complexity of the variables used in this application, users are advised to read the product user guide for a full understanding of what the results represent. INPUT VARIABLES Name Units Description Source BIO01 - Annual mean temperature K Annual mean of the daily mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Global bioclimatic indicators BIO02 - Mean diurnal range K Mean of the daily maximum temperature minus the daily minimum temperature. The data is aggregated over the months. This indicator corresponds to the official BIOCLIM variable BIO02. Global bioclimatic indicators BIO03 - Isothermality % Monthly mean diurnal range divided by temperature annual range multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO03. Global bioclimatic indicators BIO04 - Temperature seasonality K Standard deviation of the monthly mean temperature multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO04. Global bioclimatic indicators BIO05 - Maximum temperature of warmest month K Maximum daily temperature of the month with the highest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO05. Global bioclimatic indicators BIO06 - Minimum temperature of the coldest month K Minimum daily temperature of the month with the lowest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO06. Global bioclimatic indicators BIO07 - Temperature annual range K Maximum temperature of the warmest month minus minimum temperature of the coldest month. This indicator corresponds to the official BIOCLIM variable BIO07. Global bioclimatic indicators BIO08 - Mean temperature of wettest quarter K The mean of monthly mean temperature during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO08. Global bioclimatic indicators BIO09 - Mean temperature of driest quarter K The mean of monthly mean temperature during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO09. Global bioclimatic indicators BIO10 - Mean temperature of warmest quarter K The mean of monthly mean temperature during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO10. Global bioclimatic indicators BIO11 - Mean temperature of coldest quarter K The mean of monthly mean temperature during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO11. Global bioclimatic indicators BIO12 - Annual precipitation m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Global bioclimatic indicators BIO13 - Precipitation of wettest month m s-1 Maximum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO13. Global bioclimatic indicators BIO14 - Precipitation of driest month m s-1 Minimum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO14. Global bioclimatic indicators BIO15 - Precipitation seasonality (coefficient of variation) % Annual coefficient of variation of the monthly precipitation sums. This indicator corresponds to the official BIOCLIM variable BIO15. Global bioclimatic indicators BIO16 - Precipitation of wettest quarter m s-1 The mean of monthly mean precipitation during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO16. Global bioclimatic indicators BIO17 - Precipitation of driest quarter m s-1 The mean of monthly mean precipitation during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO17. Global bioclimatic indicators BIO18 - Precipitation of warmest quarter m s-1 The mean of monthly mean precipitation during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO18. Global bioclimatic indicators BIO19 - Precipitation of coldest quarter m s-1 The mean of monthly mean precipitation during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO19. Global bioclimatic indicators Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Global bioclimatic indicators Dry spells maximum length day Maximum number of consecutive dry spell days within a year. Global bioclimatic indicators Dry spells mean intensity day Determine the consecutive dry days at each day in a year, then take the average of these daily values over the year. Global bioclimatic indicators Dry spells mean length day Mean length of dry spell days with a minimum of 5 days within a year. Global bioclimatic indicators Dry spells number Dimensionless Number of dry spells with a minimum of 5 days that occur in a year. Global bioclimatic indicators Summer days day Number of days in a year for which the daily maximum temperature is not lower than 298.15 K (25 oC). Global bioclimatic indicators Growing degree days during growing season length K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC) during the period between the growing season start and end. Global bioclimatic indicators Growing season length day Number of days between the start and the end of the growing season. Global bioclimatic indicators Potential evaporation annual mean m s-1 Annual averaged amount of water that would evaporate and transpire if there is unlimited water supply. Global bioclimatic indicators Evaporative fraction annual mean Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the year. Global bioclimatic indicators Surface latent heat flux annual mean W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Global bioclimatic indicators Surface sensible heat flux annual mean W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Global bioclimatic indicators Volumetric soil water layer 1 annual mean m3 m-3 The volume of water in soil layer 1 (0-7cm, the surface is at 0 cm) averaged over the year. The ECMWF Integrated Forecasting System model has a four-layer representation of soil; Layer 1: 0-7 cm; Layer 2: 7-28 cm; Layer 3: 28-100 cm; Layer 4: 100-289 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Global bioclimatic indicators Temperature K Monthly mean of the temperature near the surface. Global bioclimatic indicators Precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Global bioclimatic indicators Water vapour pressure Pa Contribution to the total atmospheric pressure provided by the water vapour over the period 00-24h local time per unit of time. Indicator offered as a monthly or annual mean. Global bioclimatic indicators Cloud cover Dimensionless Fraction of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. Global bioclimatic indicators Sea ice concentration Dimensionless The fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land sea mask and lake cover, at the resolution being used. The data is available per month. Global bioclimatic indicators Sea surface temperature K Temperature of sea water near the surface. The data is available per month. Global bioclimatic indicators Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). The data is aggregated over the months. Global bioclimatic indicators Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). The data is aggregated over the months. Global bioclimatic indicators Zonal wind speed m s-1 Monthly mean of the eastward component of the two-dimensional horizontal air velocity near the surface. Indicator offered as a monthly or annual mean. Global bioclimatic indicators Meridional wind speed m s-1 Monthly mean of the northward component of the two-dimensional horizontal air velocity near the surface. Global bioclimatic indicators Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Global bioclimatic indicators INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source BIO01 - Annual mean temperature K Annual mean of the daily mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Global bioclimatic indicators BIO01 - Annual mean temperature K Annual mean of the daily mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Global bioclimatic indicators Global bioclimatic indicators BIO02 - Mean diurnal range K Mean of the daily maximum temperature minus the daily minimum temperature. The data is aggregated over the months. This indicator corresponds to the official BIOCLIM variable BIO02. Global bioclimatic indicators BIO02 - Mean diurnal range K Mean of the daily maximum temperature minus the daily minimum temperature. The data is aggregated over the months. This indicator corresponds to the official BIOCLIM variable BIO02. Global bioclimatic indicators Global bioclimatic indicators BIO03 - Isothermality % Monthly mean diurnal range divided by temperature annual range multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO03. Global bioclimatic indicators BIO03 - Isothermality % Monthly mean diurnal range divided by temperature annual range multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO03. Global bioclimatic indicators Global bioclimatic indicators BIO04 - Temperature seasonality K Standard deviation of the monthly mean temperature multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO04. Global bioclimatic indicators BIO04 - Temperature seasonality K Standard deviation of the monthly mean temperature multiplied by 100. This indicator corresponds to the official BIOCLIM variable BIO04. Global bioclimatic indicators Global bioclimatic indicators BIO05 - Maximum temperature of warmest month K Maximum daily temperature of the month with the highest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO05. Global bioclimatic indicators BIO05 - Maximum temperature of warmest month K Maximum daily temperature of the month with the highest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO05. Global bioclimatic indicators Global bioclimatic indicators BIO06 - Minimum temperature of the coldest month K Minimum daily temperature of the month with the lowest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO06. Global bioclimatic indicators BIO06 - Minimum temperature of the coldest month K Minimum daily temperature of the month with the lowest monthly mean of daily mean temperature. This indicator corresponds to the official BIOCLIM variable BIO06. Global bioclimatic indicators Global bioclimatic indicators BIO07 - Temperature annual range K Maximum temperature of the warmest month minus minimum temperature of the coldest month. This indicator corresponds to the official BIOCLIM variable BIO07. Global bioclimatic indicators BIO07 - Temperature annual range K Maximum temperature of the warmest month minus minimum temperature of the coldest month. This indicator corresponds to the official BIOCLIM variable BIO07. Global bioclimatic indicators Global bioclimatic indicators BIO08 - Mean temperature of wettest quarter K The mean of monthly mean temperature during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO08. Global bioclimatic indicators BIO08 - Mean temperature of wettest quarter K The mean of monthly mean temperature during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO08. Global bioclimatic indicators Global bioclimatic indicators BIO09 - Mean temperature of driest quarter K The mean of monthly mean temperature during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO09. Global bioclimatic indicators BIO09 - Mean temperature of driest quarter K The mean of monthly mean temperature during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO09. Global bioclimatic indicators Global bioclimatic indicators BIO10 - Mean temperature of warmest quarter K The mean of monthly mean temperature during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO10. Global bioclimatic indicators BIO10 - Mean temperature of warmest quarter K The mean of monthly mean temperature during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO10. Global bioclimatic indicators Global bioclimatic indicators BIO11 - Mean temperature of coldest quarter K The mean of monthly mean temperature during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO11. Global bioclimatic indicators BIO11 - Mean temperature of coldest quarter K The mean of monthly mean temperature during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. This indicator corresponds to the official BIOCLIM variable BIO11. Global bioclimatic indicators Global bioclimatic indicators BIO12 - Annual precipitation m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Global bioclimatic indicators BIO12 - Annual precipitation m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Global bioclimatic indicators Global bioclimatic indicators BIO13 - Precipitation of wettest month m s-1 Maximum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO13. Global bioclimatic indicators BIO13 - Precipitation of wettest month m s-1 Maximum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO13. Global bioclimatic indicators Global bioclimatic indicators BIO14 - Precipitation of driest month m s-1 Minimum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO14. Global bioclimatic indicators BIO14 - Precipitation of driest month m s-1 Minimum of the monthly precipitation rate. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x30.4 (average number of days per month)x1000. This indicator corresponds to the official BIOCLIM variable BIO14. Global bioclimatic indicators Global bioclimatic indicators BIO15 - Precipitation seasonality (coefficient of variation) % Annual coefficient of variation of the monthly precipitation sums. This indicator corresponds to the official BIOCLIM variable BIO15. Global bioclimatic indicators BIO15 - Precipitation seasonality (coefficient of variation) % Annual coefficient of variation of the monthly precipitation sums. This indicator corresponds to the official BIOCLIM variable BIO15. Global bioclimatic indicators Global bioclimatic indicators BIO16 - Precipitation of wettest quarter m s-1 The mean of monthly mean precipitation during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO16. Global bioclimatic indicators BIO16 - Precipitation of wettest quarter m s-1 The mean of monthly mean precipitation during the wettest quarter, defined as the quarter with the highest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)*1000. This indicator corresponds to the official BIOCLIM variable BIO16. Global bioclimatic indicators Global bioclimatic indicators BIO17 - Precipitation of driest quarter m s-1 The mean of monthly mean precipitation during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO17. Global bioclimatic indicators BIO17 - Precipitation of driest quarter m s-1 The mean of monthly mean precipitation during the driest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) precipitation using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO17. Global bioclimatic indicators Global bioclimatic indicators BIO18 - Precipitation of warmest quarter m s-1 The mean of monthly mean precipitation during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO18. Global bioclimatic indicators BIO18 - Precipitation of warmest quarter m s-1 The mean of monthly mean precipitation during the warmest quarter, defined as the quarter with the highest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO18. Global bioclimatic indicators Global bioclimatic indicators BIO19 - Precipitation of coldest quarter m s-1 The mean of monthly mean precipitation during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO19. Global bioclimatic indicators BIO19 - Precipitation of coldest quarter m s-1 The mean of monthly mean precipitation during the coldest quarter, defined as the quarter with the lowest monthly mean (of the daily mean) temperature using a moving average of 3 consecutive months. To compute the total precipitation sum over the month, a conversion factor should be applied of 3600x24x91.3 (average number of days per quarter)x1000. This indicator corresponds to the official BIOCLIM variable BIO19. Global bioclimatic indicators Global bioclimatic indicators Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Global bioclimatic indicators Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Global bioclimatic indicators Global bioclimatic indicators Dry spells maximum length day Maximum number of consecutive dry spell days within a year. Global bioclimatic indicators Dry spells maximum length day Maximum number of consecutive dry spell days within a year. Global bioclimatic indicators Global bioclimatic indicators Dry spells mean intensity day Determine the consecutive dry days at each day in a year, then take the average of these daily values over the year. Global bioclimatic indicators Dry spells mean intensity day Determine the consecutive dry days at each day in a year, then take the average of these daily values over the year. Global bioclimatic indicators Global bioclimatic indicators Dry spells mean length day Mean length of dry spell days with a minimum of 5 days within a year. Global bioclimatic indicators Dry spells mean length day Mean length of dry spell days with a minimum of 5 days within a year. Global bioclimatic indicators Global bioclimatic indicators Dry spells number Dimensionless Number of dry spells with a minimum of 5 days that occur in a year. Global bioclimatic indicators Dry spells number Dimensionless Number of dry spells with a minimum of 5 days that occur in a year. Global bioclimatic indicators Global bioclimatic indicators Summer days day Number of days in a year for which the daily maximum temperature is not lower than 298.15 K (25 oC). Global bioclimatic indicators Summer days day Number of days in a year for which the daily maximum temperature is not lower than 298.15 K (25 oC). Global bioclimatic indicators Global bioclimatic indicators Growing degree days during growing season length K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC) during the period between the growing season start and end. Global bioclimatic indicators Growing degree days during growing season length K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC) during the period between the growing season start and end. Global bioclimatic indicators Global bioclimatic indicators Growing season length day Number of days between the start and the end of the growing season. Global bioclimatic indicators Growing season length day Number of days between the start and the end of the growing season. Global bioclimatic indicators Global bioclimatic indicators Potential evaporation annual mean m s-1 Annual averaged amount of water that would evaporate and transpire if there is unlimited water supply. Global bioclimatic indicators Potential evaporation annual mean m s-1 Annual averaged amount of water that would evaporate and transpire if there is unlimited water supply. Global bioclimatic indicators Global bioclimatic indicators Evaporative fraction annual mean Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the year. Global bioclimatic indicators Evaporative fraction annual mean Dimensionless Monthly surface latent heat divided by the monthly total sensible and latent heat flux, averaged over the year. Global bioclimatic indicators Global bioclimatic indicators Surface latent heat flux annual mean W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Global bioclimatic indicators Surface latent heat flux annual mean W m-2 The transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Global bioclimatic indicators Global bioclimatic indicators Surface sensible heat flux annual mean W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Global bioclimatic indicators Surface sensible heat flux annual mean W m-2 The transfer of heat between the Earths surface and the atmosphere through the effects of turbulent air motion, averaged over the year. The vector component is positive when directed upward (negative downward). Global bioclimatic indicators Global bioclimatic indicators Volumetric soil water layer 1 annual mean m3 m-3 The volume of water in soil layer 1 (0-7cm, the surface is at 0 cm) averaged over the year. The ECMWF Integrated Forecasting System model has a four-layer representation of soil; Layer 1: 0-7 cm; Layer 2: 7-28 cm; Layer 3: 28-100 cm; Layer 4: 100-289 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Global bioclimatic indicators Volumetric soil water layer 1 annual mean m3 m-3 The volume of water in soil layer 1 (0-7cm, the surface is at 0 cm) averaged over the year. The ECMWF Integrated Forecasting System model has a four-layer representation of soil; Layer 1: 0-7 cm; Layer 2: 7-28 cm; Layer 3: 28-100 cm; Layer 4: 100-289 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level. Global bioclimatic indicators Global bioclimatic indicators Temperature K Monthly mean of the temperature near the surface. Global bioclimatic indicators Temperature K Monthly mean of the temperature near the surface. Global bioclimatic indicators Global bioclimatic indicators Precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Global bioclimatic indicators Precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Global bioclimatic indicators Global bioclimatic indicators Water vapour pressure Pa Contribution to the total atmospheric pressure provided by the water vapour over the period 00-24h local time per unit of time. Indicator offered as a monthly or annual mean. Global bioclimatic indicators Water vapour pressure Pa Contribution to the total atmospheric pressure provided by the water vapour over the period 00-24h local time per unit of time. Indicator offered as a monthly or annual mean. Global bioclimatic indicators Global bioclimatic indicators Cloud cover Dimensionless Fraction of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. Global bioclimatic indicators Cloud cover Dimensionless Fraction of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered. Global bioclimatic indicators Global bioclimatic indicators Sea ice concentration Dimensionless The fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land sea mask and lake cover, at the resolution being used. The data is available per month. Global bioclimatic indicators Sea ice concentration Dimensionless The fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land sea mask and lake cover, at the resolution being used. The data is available per month. Global bioclimatic indicators Global bioclimatic indicators Sea surface temperature K Temperature of sea water near the surface. The data is available per month. Global bioclimatic indicators Sea surface temperature K Temperature of sea water near the surface. The data is available per month. Global bioclimatic indicators Global bioclimatic indicators Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). The data is aggregated over the months. Global bioclimatic indicators Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). The data is aggregated over the months. Global bioclimatic indicators Global bioclimatic indicators Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). The data is aggregated over the months. Global bioclimatic indicators Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). The data is aggregated over the months. Global bioclimatic indicators Global bioclimatic indicators Zonal wind speed m s-1 Monthly mean of the eastward component of the two-dimensional horizontal air velocity near the surface. Indicator offered as a monthly or annual mean. Global bioclimatic indicators Zonal wind speed m s-1 Monthly mean of the eastward component of the two-dimensional horizontal air velocity near the surface. Indicator offered as a monthly or annual mean. Global bioclimatic indicators Global bioclimatic indicators Meridional wind speed m s-1 Monthly mean of the northward component of the two-dimensional horizontal air velocity near the surface. Global bioclimatic indicators Meridional wind speed m s-1 Monthly mean of the northward component of the two-dimensional horizontal air velocity near the surface. Global bioclimatic indicators Global bioclimatic indicators Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Global bioclimatic indicators Wind speed m s-1 Magnitude of the two-dimensional horizontal air velocity near the surface. Global bioclimatic indicators Global bioclimatic indicators OUTPUT VARIABLES Name Units Description Climate suitability Dimensionless The climate suitability on a 0-1 scale (0 completely unsuitable, 1 completely suitable) derived from simple climate envelope model, see documentation for more information. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Climate suitability Dimensionless The climate suitability on a 0-1 scale (0 completely unsuitable, 1 completely suitable) derived from simple climate envelope model, see documentation for more information. Climate suitability Dimensionless The climate suitability on a 0-1 scale (0 completely unsuitable, 1 completely suitable) derived from simple climate envelope model, see documentation for more information. 414 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-uerra-europe-soil-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-uerra-europe-soil-levels reanalysis-uerra-europe-soil-levels The UERRA dataset provides estimations of the climate in Europe based on model data combined with observations using the UERRA-HARMONIE system and MESCAN-SURFEX system. UERRA-HARMONIE is a 3-dimensional data assimilation system, whereas MESCAN-SURFEX is a complementary surface analysis system. In general, the assimilation systems are able to estimate biases between observations and to sift good-quality data from poor data. The laws of physics allow for estimates at locations where data coverage is low. The provision of estimates at each grid point in Europe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with. The land surface platform SURFEX is forced with downscaled forecast fields from UERRA-HARMONIE as well as MESCAN analyses. It is run offline, i.e. without feedback to the atmospheric analysis performed in MESCAN or the UERRA-HARMONIE data assimilation cycles. The observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will have an impact on the quality of analyses leading to less accurate estimates. The improvement over global reanalysis products comes with the higher horizontal resolution that allows incorporating more regional details (e.g. topography). Moreover, it enables the system even to use more observations at places with dense observation networks. DATA DESCRIPTION Data type Gridded Projection Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Horizontal coverage Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Horizontal resolution 11km x 11km for the UERRA-HARMONIE system. 5.5km x 5.5km for the MESCAN-SURFEX system. Vertical coverage From the surface to a depth of 12m for the MESCAN-SURFEX system. For the UERRA-HARMONIE system the vertical coordinates have no precise depth values. They are defined in terms of a time constant determining how quickly they adjust and restore. Please, see the documenation section for more information. Vertical resolution 3 levels of the soil model for the UERRA-HARMONIE system. 14 for the MESCAN-SURFEX system: 0.01m, 0.04m, 0.1m, 0.2m, 0.4m, 0.6m, 0.8m, 1m, 1.5m, 2m, 3m, 5m, 8m, 12m. Temporal coverage January 1961 to July 2019. Temporal resolution Analysis are availabe each day at 00, 06, 12 and 18 UTC. File format GRIB2 Update frequency No expected updates. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Projection Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the MESCAN-SURFEX system. Horizontal coverage Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Horizontal coverage Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural. Horizontal resolution 11km x 11km for the UERRA-HARMONIE system. 5.5km x 5.5km for the MESCAN-SURFEX system. Horizontal resolution 11km x 11km for the UERRA-HARMONIE system. 5.5km x 5.5km for the MESCAN-SURFEX system. 11km x 11km for the UERRA-HARMONIE system. 5.5km x 5.5km for the MESCAN-SURFEX system. Vertical coverage From the surface to a depth of 12m for the MESCAN-SURFEX system. For the UERRA-HARMONIE system the vertical coordinates have no precise depth values. They are defined in terms of a time constant determining how quickly they adjust and restore. Please, see the documenation section for more information. Vertical coverage From the surface to a depth of 12m for the MESCAN-SURFEX system. For the UERRA-HARMONIE system the vertical coordinates have no precise depth values. They are defined in terms of a time constant determining how quickly they adjust and restore. Please, see the documenation section for more information. From the surface to a depth of 12m for the MESCAN-SURFEX system. For the UERRA-HARMONIE system the vertical coordinates have no precise depth values. They are defined in terms of a time constant determining how quickly they adjust and restore. Please, see the documenation section for more information. Vertical resolution 3 levels of the soil model for the UERRA-HARMONIE system. 14 for the MESCAN-SURFEX system: 0.01m, 0.04m, 0.1m, 0.2m, 0.4m, 0.6m, 0.8m, 1m, 1.5m, 2m, 3m, 5m, 8m, 12m. Vertical resolution 3 levels of the soil model for the UERRA-HARMONIE system. 14 for the MESCAN-SURFEX system: 0.01m, 0.04m, 0.1m, 0.2m, 0.4m, 0.6m, 0.8m, 1m, 1.5m, 2m, 3m, 5m, 8m, 12m. 3 levels of the soil model for the UERRA-HARMONIE system. 14 for the MESCAN-SURFEX system: 0.01m, 0.04m, 0.1m, 0.2m, 0.4m, 0.6m, 0.8m, 1m, 1.5m, 2m, 3m, 5m, 8m, 12m. Temporal coverage January 1961 to July 2019. Temporal coverage January 1961 to July 2019. Temporal resolution Analysis are availabe each day at 00, 06, 12 and 18 UTC. Temporal resolution Analysis are availabe each day at 00, 06, 12 and 18 UTC. File format GRIB2 File format GRIB2 Update frequency No expected updates. Update frequency No expected updates. MAIN VARIABLES Name Units Description Soil temperature K Model temperature valid for the grid cell at the corresponding soil level. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter soil valid for the cell grid at the corresponding soil level. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. To interpret soil water and to compare different models the Soil Wetness Index (SWI) is used: SWI = (soil_water - wilting_point) / (field_capacity - wilting_point). Volumetric transpiration stress-onset m3 m-3 Soil water content after the soil has been saturated and allowed to drain freely. The values are valid for the cell grid at the corresponding soil level. Volumetric wilting point m3 m-3 Model soil water content at which plants wilt and can no longer recover. It is given for a grid cell in the corresponding soil level. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Soil temperature K Model temperature valid for the grid cell at the corresponding soil level. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Soil temperature K Model temperature valid for the grid cell at the corresponding soil level. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter soil valid for the cell grid at the corresponding soil level. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. To interpret soil water and to compare different models the Soil Wetness Index (SWI) is used: SWI = (soil_water - wilting_point) / (field_capacity - wilting_point). Volumetric soil moisture m3 m-3 Amount of water in a cubic meter soil valid for the cell grid at the corresponding soil level. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. To interpret soil water and to compare different models the Soil Wetness Index (SWI) is used: SWI = (soil_water - wilting_point) / (field_capacity - wilting_point). Volumetric transpiration stress-onset m3 m-3 Soil water content after the soil has been saturated and allowed to drain freely. The values are valid for the cell grid at the corresponding soil level. Volumetric transpiration stress-onset m3 m-3 Soil water content after the soil has been saturated and allowed to drain freely. The values are valid for the cell grid at the corresponding soil level. Volumetric wilting point m3 m-3 Model soil water content at which plants wilt and can no longer recover. It is given for a grid cell in the corresponding soil level. Volumetric wilting point m3 m-3 Model soil water content at which plants wilt and can no longer recover. It is given for a grid cell in the corresponding soil level. RELATED VARIABLES In order to make data access more manageable, the UERRA dataset has been split into several records. Complemetary records to the present one are: UERRA on height levels, UERRA on pressure levels and UERRA on single levels. For the present soil level dataset, forecast data are not accessible through this form. However, the complet dataset can be accessed through the CDS application programming interface (API). RELATED VARIABLES RELATED VARIABLES In order to make data access more manageable, the UERRA dataset has been split into several records. Complemetary records to the present one are: UERRA on height levels, UERRA on pressure levels and UERRA on single levels. For the present soil level dataset, forecast data are not accessible through this form. However, the complet dataset can be accessed through the CDS application programming interface (API). In order to make data access more manageable, the UERRA dataset has been split into several records. Complemetary records to the present one are: UERRA on height levels, UERRA on pressure levels and UERRA on single levels. For the present soil level dataset, forecast data are not accessible through this form. However, the complet dataset can be accessed through the CDS application programming interface (API). 415 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-high-resolution-l4-sea-surface-temperature http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_BS_SST_L4_REP_OBSERVATIONS_010_022 Black Sea - High Resolution L4 Sea Surface Temperature Reprocessed Short description: The CMEMS Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05° resolution grid covering the period from January 1st 1982 to present (currently, up to six months before real time). The BS-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record provided by the ESA Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) initiatives, but also includes in input an adjusted version of the AVHRR Pathfinder dataset version 5.3 to increase the input observation coverage. DOI (product) :https://doi.org/10.48670/moi-00160 https://doi.org/10.48670/moi-00160 416 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-european-north-west-shelf-ocean-physics http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009 Atlantic- European North West Shelf- Ocean Physics Reanalysis Short Description: The ocean physics reanalysis for the North-West European Shelf is produced using an ocean assimilation model, with tides, at 7 km horizontal resolution. The ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature and vertical profiles of temperature and salinity. The model is forced by lateral boundary conditions from the GloSea5, one of the multi-models used by [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… GLOBAL_REANALYSIS_PHY_001_026] and at the Baltic boundary by the [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… BALTICSEA_REANALYSIS_PHY_003_011]. The atmospheric forcing is given by the ECMWF ERA5 atmospheric reanalysis. The river discharge is from a daily climatology. https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… Further details of the model, including the product validation are provided in the [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004… CMEMS-NWS-QUID-004-009]. http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004… Products are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually provinding six-month extension of the time series. See [http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-0… CMEMS-NWS-PUM-004-009_011] for further details. http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-0… Associated products: This model is coupled with a biogeochemistry model (ERSEM) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro…]. An analysis-forecast product is available from [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NWSHELF_ANALYSISFORECAST_PHY_LR_004_011]. The product is updated biannually provinding six-month extension of the time series. https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… DOI (product) :https://doi.org/10.48670/moi-00059 https://doi.org/10.48670/moi-00059 417 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/eu-hydro-coastline-version-13-nov-2020 https://land.copernicus.eu/imagery-in-situ/eu-hydro/eu-hydro-coastline EU-Hydro – Coastline - version 1.3, Nov. 2020 EU-Hydro is a dataset for all EEA38 countries and United Kingdom providing photo-interpreted river network, consistent of surface interpretation of water bodies (lakes and wide rivers), and a drainage model (also called Drainage Network), derived from EU-DEM, with catchments and drainage lines and nodes. This metadata refers to the EU-Hydro coastline, which is disseminated as one shapefile merged for all 35 basins. The coastline is also included for each basin in the EU-Hydro River Network Database. The production of EU-Hydro and the derived layers was coordinated by the European Environment Agency in the frame of the EU Copernicus programme. 418 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/n2k-2018-vector-europe-6-yearly-jul-2021 https://land.copernicus.eu/local/natura/n2k-2018?tab=download N2K 2018 (vector), Europe, 6-yearly, Jul. 2021 This metadata refers to CLMS N2K 2018 product, the Copernicus Land Cover/Land Use (LC/LU) status map, with 2018 as reference year for the classification, tailored to the needs of biodiversity monitoring in selected Natura2000 sites: 4790 sites of natural and semi-natural grassland formations listed in Annex I of the Habitats Directive, including a 2 km buffer zone surrounding the sites and covering an area of 631.820 km² across Europe (EU27, the United Kingdom and Switzerland). The product includes three Emerald sites in Switzerland. LC/LU has been extracted from VHR satellite data and other available data. The production of N2K updates was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 419 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-mean-sea-level-time-series-and-trend http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MEDSEA_OMI_SL_area_averaged_anomalies Mediterranean Sea Mean Sea Level time series and trend from Observations Reprocessing DEFINITION The ocean monitoring indicator of regional mean sea level is derived from the DUACS delayed-time (DT-2021 version) altimeter gridded maps of sea level anomalies based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The mean sea level evolution estimated in the Mediterranean Sea is derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed (least square fit of sinusoidal function) and the time series is low-pass filtered (175 days cut-off). The curve is corrected for the regional mean effect of the Glacial Isostatic Adjustment (GIA) using the ICE5G-VM2 GIA model (Peltier, 2004). During 1993-1998, the Global men sea level (hereafter GMSL) has been known to be affected by a TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018; Legeais et al., 2020). This drift led to overestimate the trend of the GMSL during the first 6 years of the altimetry record (about 0.04 mm/y at global scale over the whole altimeter period). A correction of the drift is proposed for the Global mean sea level (Legeais et al., 2020). Whereas this TOPEX-A instrumental drift should also affect the regional mean sea level (hereafter RMSL) trend estimation, this empirical correction is currently not applied to the altimeter sea level dataset and resulting estimated for RMSL. Indeed, the pertinence of the global correction applied at regional scale has not been demonstrated yet and there is no clear consensus achieved on the way to proceed at regional scale. Additionally, the estimate of such a correction at regional scale is not obvious, especially in areas where few accurate independent measurements (e.g. in situ)- necessary for this estimation - are available. The trend uncertainty is provided in a 90% confidence interval (Prandi et al., 2021). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not taken into account. CONTEXT The indicator on area averaged sea level is a crucial index of climate change, and individual components contribute to sea level rise, including expansion due to ocean warming and melting of glaciers and ice sheets (WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report, global mean sea level (GMSL) increased by 0.20 (0.15 to 0.25) m over the period 1901 to 2018 with a rate 25 of rise that has accelerated since the 1960s to 3.7 (3.2 to 4.2) mm yr-1 for the period 2006–2018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). At regional scale, sea level does not change homogenously, and RMSL rise can also be influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). Rising sea level can strongly affect population and infrastructures in coastal areas, increase their vulnerability and risks for food security, particularly in low lying areas and island states. Adverse impacts from floods, storms and tropical cyclones with related losses and damages have increased due to sea level rise, and increase their vulnerability and increase risks for food security, particularly in low lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). Beside a clear long-term trend, the regional mean sea level variation in the Mediterranean Sea shows an important interannual variability, with a high trend observed before 1999 and lower values afterward. This variability is associated with a variation of the different forcing. Steric effect has been the most important forcing before 1999 (Fenoglio-Marc, 2002; Vigo et al., 2005). Important change of the deep-water formation site also occurred in 1995. The latest is preconditioned by an important change of the sea surface circulation observed in the Ionian Sea in 1997-1998 (e.g. Gačić et al., 2011), under the influence of the North Atlantic Oscillation (NAO) and negative Atlantic Multidecadal Oscillation (AMO) phases (Incarbona et al., 2016). They may also impact the sea level trend in the basin (Vigo et al., 2005). In 2010-2011, high regional mean sea level has been related to enhanced water mass exchange at Gibraltar, under the influence of wind forcing during the negative phase of NAO (Landerer and Volkov, 2013). CMEMS KEY FINDINGS Over the [1993/01/01, 2021/08/02] period, the basin-wide RMSL in the Mediterranean Sea rises at a rate of 2.7  0.83 mm/year. DOI (product):https://doi.org/10.48670/moi-00264 https://doi.org/10.48670/moi-00264 420 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-total-column-water-vapour-land-ocean https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-total-column-water-vapour-land-ocean satellite-total-column-water-vapour-land-ocean Water vapour is an Essential Climate Variable as it provides the basis for all cloud formation, cloud physics and furthermore influences the Earth's heat budget due to its high absorbance of long and short-wave radiation. Total column water vapour (TCWV) is a measure of the integrated water vapour content of the atmosphere. This catalogue entry provides a combination of the Medium Resolution Imaging Spectrometer (MERIS) retrievals in the near-infrared (NIR) over land surfaces and coastal areas with the Special Sensor Microwave Imager (SSM/I) TCWV retrievals in the microwave spectra over ocean surfaces. A related dataset providing TCWV data over ocean only but at a higher time resolution computed using a different set of sensors and algorithms is also available (the link to that dataset is under the Related data section) SSM/I is a well-established instrument for TCWV retrieval over open ocean surfaces and provides a long time series and global coverage at a reasonable resolution. The MERIS TCWV dataset provides a continuous data record from May 2002 to March 2012 in line with World Mereorological Organization's (WMO) Observing Systems Capability Analysis and Review Tool (OSCAR) requirements for TCWV. The differential absorption technique used for MERIS is proven to be a reliable retrieval method for sensors with bands in the NIR at the water vapour absorption band centered at 940 nm. This MERIS dataset provides global coverage for almost 10 years. Although not necessarily needed as input for the retrieval, ERA-Interim reanalysis TCWV was used as a priori. Compared to this background TCWV field, the MERIS TCWV has a much higher spatial resolution and a good uncertainty estimate. Further input to the algorithm for MERIS TCWV is surface pressure and surface temperature over land, as well as surface winds over ocean. These data are taken from ERA Interim. They are not provided in the final dataset. SSM/I and MERIS retrievals were merged in order to have global coverage with each instrument used to its full potential. This dataset is produced by the Copernicus Climate Change Service (C3S). DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 0.5° x 0.5° and 0.05° x 0.05° Vertical coverage Total atmospheric column Vertical resolution Single level Temporal coverage Form May 2002 to March 2012 Temporal resolution Monthly File format NetCDF4 Versions 1.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.5° x 0.5° and 0.05° x 0.05° Horizontal resolution 0.5° x 0.5° and 0.05° x 0.05° Vertical coverage Total atmospheric column Vertical coverage Total atmospheric column Vertical resolution Single level Vertical resolution Single level Temporal coverage Form May 2002 to March 2012 Temporal coverage Form May 2002 to March 2012 Temporal resolution Monthly Temporal resolution Monthly File format NetCDF4 File format NetCDF4 Versions 1.0 Versions 1.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Total column water vapour kg m-2 Total Column Water Vapour (also called integrated Water Vapour (IWV) or Precipitable Water Vapour (PWV)) is the integrated mass of gaseous water in the total column of the atmosphere over an area of 1 m². MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Total column water vapour kg m-2 Total Column Water Vapour (also called integrated Water Vapour (IWV) or Precipitable Water Vapour (PWV)) is the integrated mass of gaseous water in the total column of the atmosphere over an area of 1 m². Total column water vapour kg m-2 Total Column Water Vapour (also called integrated Water Vapour (IWV) or Precipitable Water Vapour (PWV)) is the integrated mass of gaseous water in the total column of the atmosphere over an area of 1 m². RELATED VARIABLES The files contain a number of auxiliary variables describing the uncertainty and statistical distribution of the main variables. For more information on the contents of the downloaded files, please refer to the documentation. RELATED VARIABLES RELATED VARIABLES The files contain a number of auxiliary variables describing the uncertainty and statistical distribution of the main variables. For more information on the contents of the downloaded files, please refer to the documentation. The files contain a number of auxiliary variables describing the uncertainty and statistical distribution of the main variables. For more information on the contents of the downloaded files, please refer to the documentation. 421 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/provider-c3s-data-rescue-without https://cds.climate.copernicus.eu/cdsapp#!/dataset/provider-c3s-data-rescue-without provider-c3s-data-rescue-without The present Catalogue entry introduces the Data Rescue Service online portal which is run by a contract team coordinated by the Copernicus Climate Change Service (C3S). Registration with, and login to, the C3S Data Rescue Service portal is independent of the present C3S Climate Data Store catalogue. Data Rescue Service online portal Data rescue is the discovery, preservation, quality control, digitisation and consolidation of past measurements of weather conditions. The C3S Data Rescue Service does not provide rescued data - that data is sent to international repositories. The C3S Data Rescue Service facilitates and coordinates the rescue of weather and climate data from around the world. The service collects and shares information on past, current and planned data rescue projects, feeds data into international repositories and promotes tools, best practice and standards for all aspects of the data rescue process. .dataset-main-column{width:100%} .dataset-additional-block{display:none} .dataset-details-panel table {width:100%;font-size:0.9em} .dataset-details-panel table td {vertical-align:middle} 422 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-daily-gridded-sea-surface-winds http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=WIND_GLO_WIND_L3_NRT_OBSERVATIONS_012_002 Global Ocean Daily Gridded Sea Surface Winds from Scatterometer Short description: For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products: *0.5 degrees grid for the 50 km scatterometer L2 inputs, *0.25 degrees grid based on 25 km scatterometer swath observations, *and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately. The reported wind is stress-equivalent wind and wind stress. Moreover, wind stress curl and divergence are also available. The NRT L3 products follow the NRT availability of the EUMETSAT OSI SAF L2 products and are available for: *The ASCAT scatterometers on Metop-A (discontinued on 15/11/2021), Metop-B and Metop-C at 0.125 and 0.25 degrees; *The OSCAT scatterometer on Scatsat-1 at 0.25 and 0.5 degrees (discontinued on 28/2/2021); *The HSCAT scatterometer on HY-2B, HY-2C and HY-2D at 0.25 and 0.5 degrees DOI (product) :https://doi.org/10.48670/moi-00182 https://doi.org/10.48670/moi-00182 423 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/medium-resolution-vegetation-phenology-and-12 https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum_d1-0.pdf Medium Resolution Vegetation Phenology and Productivity: Base level value (raster 500m), Oct. 2022 The Base Level Value (Base level), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European Medium Resolution Vegetation Phenology and Productivity (MR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Base Level Value (Base level) is the average Plant Phenology Index (PPI) value of the minima before and after the growing season. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Base Level Value (Base level) is one of the 13 parameters. The full list is available in the Product User Manual: https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… The Base Level Value (Base level) time series dataset is made available as raster files with 500x 500m resolution, in ETRS89-LAEA projection corresponding to the MCD43 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2000 onwards. It is updated in the first quarter of each year. The full on-line access to open and free data for this resource will be made available by the end of 2022. Until then the data will be made available 'on-demand' by filling in the form at: https://land.copernicus.eu/contact-form https://land.copernicus.eu/contact-form 424 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cems-glofas-historical https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical cems-glofas-historical This dataset provides a modelled time series of gridded river discharge. It is a product of the Global Flood Awareness System (GloFAS) and offers a consistent representation of a key hydrological variable across the global domain. This dataset is accompanied by two ancillary files for interpretation, one containing upstream area data and the other elevation data (see the table of related variables and the associated link in the documentation). This dataset was produced by forcing the open-source LISFLOOD hydrological model with ERA5 meteorological reanalysis data, interpolated to the GloFAS resolution, produced at a 24-hourly timestep. Two variations of the ERA5 forcing data are used, resulting in two types of river discharge data, intermediate and consolidated. Intermediate river discharge data is produced using ERA5 Near Real Time (ERA5T) data and is updated daily, whilst consolidated data is produced using the consolidated ERA5 reanalysis and is updated monthly. Companion datasets, also available through the CDS, are forecasts for users who are looking for medium-range forecasts, reforecasts for research, local skill assessment and post-processing, and seasonal forecasts and reforecasts for users looking for long-term forecasts. For users specifically interested in European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All the GloFAS and EFAS datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS), which is managed, technically implemented and developed by the European Commission’s Joint Research Centre. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal resolution 0.05° x 0.05° for version 4.0, 0.1° x 0.1° for version 3.1 and older Vertical resolution Surface level for river discharge Temporal coverage 1 January 1979 to near real time for v4.0, and various dates for legacy versions Temporal resolution Daily data File format GRIB2 Conventions WMO standards for GRIB2 Versions Operational version - GloFAS v4.0 released 2023-07-26. A new river discharge reanalysis will be published with every major update of the GloFAS system. For more information on versions, we refer to the documentation. Update frequency Updated daily DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal resolution 0.05° x 0.05° for version 4.0, 0.1° x 0.1° for version 3.1 and older Horizontal resolution 0.05° x 0.05° for version 4.0, 0.1° x 0.1° for version 3.1 and older Vertical resolution Surface level for river discharge Vertical resolution Surface level for river discharge Temporal coverage 1 January 1979 to near real time for v4.0, and various dates for legacy versions Temporal coverage 1 January 1979 to near real time for v4.0, and various dates for legacy versions Temporal resolution Daily data Temporal resolution Daily data File format GRIB2 File format GRIB2 Conventions WMO standards for GRIB2 Conventions WMO standards for GRIB2 Versions Operational version - GloFAS v4.0 released 2023-07-26. A new river discharge reanalysis will be published with every major update of the GloFAS system. For more information on versions, we refer to the documentation. Versions Operational version - GloFAS v4.0 released 2023-07-26. A new river discharge reanalysis will be published with every major update of the GloFAS system. For more information on versions, we refer to the documentation. Update frequency Updated daily Update frequency Updated daily MAIN VARIABLES Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over a 24-hour period. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over a 24-hour period. River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over a 24-hour period. RELATED VARIABLES Name Units Description Elevation m The mean height elevation above sea level for each pixel in the GloFAS domain. Accessible via the link in the Documentation tab. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Accessible via the link in the Documentation tab. RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Elevation m The mean height elevation above sea level for each pixel in the GloFAS domain. Accessible via the link in the Documentation tab. Elevation m The mean height elevation above sea level for each pixel in the GloFAS domain. Accessible via the link in the Documentation tab. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Accessible via the link in the Documentation tab. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Accessible via the link in the Documentation tab. 425 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-precipitation https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-precipitation satellite-precipitation The analysis of the Global Precipitation Climatology Project (GPCP) provides global estimates of precipitation as monthly means since 1979 (GPCP monthly v2.3) and as daily means since 1996 (GPCP daily v1.3), based on estimates using microwave imagers on polar-orbiting satellites and infrared imagers on geostationary satellites. The monthly product also includes information from rain-gauge observations analyzed by the Global Precipitation Climatology Centre (GPCC). The GPCP daily product is tied to GPCC indirectly via its calibration with the GPCP monthly product. Within the hydrological cycle, precipitation is the main component of water transport from the atmosphere to the Earth’s surface. Precipitation varies strongly, depending on geographical location, season, synopsis, and other meteorological factors. The supply with freshwater through precipitation is vital for many subsystems of the climate and the environment, but there are also hazards related to extensive precipitation or the lack of precipitation. This dataset is brokered from the GPCP: see licence text on the right. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 1.0°x1.0° for daily mean values 2.5°x2.5° for monthly mean values Vertical coverage Surface Vertical resolution Single level Temporal coverage January 1979 to present for monthly mean values October 1996 to present for daily mean values Temporal resolution Monthly and daily Temporal gaps No gaps File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 2.3 (monthly) and 1.3 (daily) Update frequency Quarterly DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 1.0°x1.0° for daily mean values 2.5°x2.5° for monthly mean values Horizontal resolution 1.0°x1.0° for daily mean values 2.5°x2.5° for monthly mean values 1.0°x1.0° for daily mean values 2.5°x2.5° for monthly mean values Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage January 1979 to present for monthly mean values October 1996 to present for daily mean values Temporal coverage January 1979 to present for monthly mean values October 1996 to present for daily mean values January 1979 to present for monthly mean values October 1996 to present for daily mean values Temporal resolution Monthly and daily Temporal resolution Monthly and daily Temporal gaps No gaps Temporal gaps No gaps File format NetCDF4 File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 2.3 (monthly) and 1.3 (daily) Versions 2.3 (monthly) and 1.3 (daily) Update frequency Quarterly Update frequency Quarterly MAIN VARIABLES Name Units Description Precipitation mm day-1 This variable represents the water-equivalent volume rate per area and per day of atmospheric water in liquid or solid phase reaching the Earth's surface MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Precipitation mm day-1 This variable represents the water-equivalent volume rate per area and per day of atmospheric water in liquid or solid phase reaching the Earth's surface Precipitation mm day-1 This variable represents the water-equivalent volume rate per area and per day of atmospheric water in liquid or solid phase reaching the Earth's surface RELATED VARIABLES The monthly mean of surface precipitation is accompanied by a respective uncertainty. RELATED VARIABLES RELATED VARIABLES The monthly mean of surface precipitation is accompanied by a respective uncertainty. The monthly mean of surface precipitation is accompanied by a respective uncertainty. 426 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-fisheries-abundance https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-fisheries-abundance sis-fisheries-abundance The dataset contains model projections of fish catch and abundance in European seas out to 2098 produced using the Size Spectra – Dynamic Bioclimate Envelope Model (SS-DBEM). The SS-DBEM is a mechanistic model, which means that it takes into account aspects of ecology (e.g. habitat preference, migration) and physiology (e.g. growth and reproduction) to determine biomass and distribution of fish species in response to changes in the environment (e.g. temperature, competition with other species, food availability). The SS-DBEM projects the impact of changes in the environment (e.g. warming, deoxygenation) and human activity (fishing pressure) on the abundance and biomass of modelled species. All this makes it a state of the art model in regard to projecting fish distribution and trends in both abundance and catch in response to climate change. Model outputs consist of fish abundance (number of fish per grid cell) and fish catch (number of fish caught per grid cell) considering three different fishing activity scenarios, termed the Maximum Sustainable Yield (MSY). Whilst the model units are expressed as “Number of individuals”, they are not to be used to predict actual future stocks but rather numbers relative to the initial starting values of the model. This is because the model was not intialised with actual fish numbers and subsequently the significance of this dataset is to show temporal and geographical trends, relative to other years and other grid points, in response to changes in the climate and the applied Maximum Sustainable Yield (MSY). In order to assess the impact of climate change, simulations under two future climate scenarios based on different Representative Concentration Pathways (RCP) for future greenhouse gas concentrations are conducted: the intermediate scenario, RCP4.5, in which greenhouse gas concentrations peak around 2040 before declining mainly due to successful mitigation measures in place; and the more pessimistic scenario, RCP8.5, where greenhouse gas concentrations continue to rise throughout the century. Fishing activity was defined according to the MSY under these environmental conditions. The combination of MSY and RCP scenarios included in this dataset are: Global sustainability: RCP4.5 with a MSY of 0.6 (fish stock is managed globally toward sustainability) World Markets: RCP8.5 with a MSY of 0.8 (fish stock is managed globally to avoid overfishing) National Enterprise: RCP8.5 with a MSY of 1.1 (fish stocks are managed at the national level resulting in overfishing) Global sustainability: RCP4.5 with a MSY of 0.6 (fish stock is managed globally toward sustainability) Global sustainability: RCP4.5 with a MSY of 0.6 (fish stock is managed globally toward sustainability) World Markets: RCP8.5 with a MSY of 0.8 (fish stock is managed globally to avoid overfishing) World Markets: RCP8.5 with a MSY of 0.8 (fish stock is managed globally to avoid overfishing) National Enterprise: RCP8.5 with a MSY of 1.1 (fish stocks are managed at the national level resulting in overfishing) National Enterprise: RCP8.5 with a MSY of 1.1 (fish stocks are managed at the national level resulting in overfishing) The simulations were run using inputs from two marine hydrodynamic-biogeochemical models: the POLCOM-ERSEM and NEMO-ERSEM models. These models were driven by one Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate model (GCM) projections with downscaled atmospheric data from a regional climate model (RCM), the Swedish Meteorological and Hydrological Institute (SMHI) Rossby Centre Regional Atmospheric Model (RCA4). Using only one of the many possible combinations of GCM-RCM pairs leads to an incomplete estimate of the true uncertainty in the outcome in a changing climate by, most likely, indicating a smaller spread of outcomes than if the estimate were based on a larger ensemble of such GCM-RCM combinations. This does not necessarily mean a reduction in the true uncertainty, but simply an incomplete estimate of it. The fish model outputs were created and post-processed by Plymouth Marine Laboratory. The fish model outputs created using POLCOMS-ERSEM input data used funding from the EU H2020 project CERES (grant agreement No 678193). This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) Horizontal resolution 0.5° x 0.5° Vertical coverage Full water column Vertical resolution Variables are provided on a single level which may differ among variables and/or species Temporal coverage POLCOMS-ERSEM: 2006 up to 2098 NEMO-ERSEM: 2006 up to 2050 Temporal resolution Annual File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions v0.1 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) Horizontal coverage POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) Horizontal resolution 0.5° x 0.5° Horizontal resolution 0.5° x 0.5° Vertical coverage Full water column Vertical coverage Full water column Vertical resolution Variables are provided on a single level which may differ among variables and/or species Vertical resolution Variables are provided on a single level which may differ among variables and/or species Temporal coverage POLCOMS-ERSEM: 2006 up to 2098 NEMO-ERSEM: 2006 up to 2050 Temporal coverage POLCOMS-ERSEM: 2006 up to 2098 NEMO-ERSEM: 2006 up to 2050 POLCOMS-ERSEM: 2006 up to 2098 NEMO-ERSEM: 2006 up to 2050 Temporal resolution Annual Temporal resolution Annual File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions v0.1 Versions v0.1 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Species abundance Count The relative number of individuals of the given species relative to the initial starting values of the model. The relative numbers may be used to compare different periods or model grid locations of the dataset, or different climate forcing scenarios and fishing management strategies. Species catch Count The relative number of individuals in the catch of the given species relative to the initial starting values of the model. The relative numbers may be used to compare different periods or model grid locations of the dataset, or different climate forcing scenarios and fishing management strategies. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Species abundance Count The relative number of individuals of the given species relative to the initial starting values of the model. The relative numbers may be used to compare different periods or model grid locations of the dataset, or different climate forcing scenarios and fishing management strategies. Species abundance Count The relative number of individuals of the given species relative to the initial starting values of the model. The relative numbers may be used to compare different periods or model grid locations of the dataset, or different climate forcing scenarios and fishing management strategies. Species catch Count The relative number of individuals in the catch of the given species relative to the initial starting values of the model. The relative numbers may be used to compare different periods or model grid locations of the dataset, or different climate forcing scenarios and fishing management strategies. Species catch Count The relative number of individuals in the catch of the given species relative to the initial starting values of the model. The relative numbers may be used to compare different periods or model grid locations of the dataset, or different climate forcing scenarios and fishing management strategies. 427 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-iberian-biscay-irish-ocean-physics-analysis-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=IBI_ANALYSISFORECAST_PHY_005_001 Atlantic-Iberian Biscay Irish- Ocean Physics Analysis and Forecast "''Short description: The IBI-MFC provides a high-resolution ocean analysis and forecast product (daily run by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia–Biscay–Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as forecasts of different temporal resolutions with a horizon of 5 days (updated on a daily basis) are available on the catalogue. The system is based on a eddy-resolving NEMO model application at 1/36º horizontal resolution, being Mercator-Ocean in charge of the model code development. The hydrodynamic forecast includes high frequency processes of paramount importance to characterize regional scale marine processes: tidal forcing, surges and high frequency atmospheric forcing, fresh water river discharge, wave forcing in forecast, etc. A weekly update of IBI downscaled analysis is also delivered as historic IBI best estimates. The product offers 3D daily and monthly ocean fields, as well as hourly mean and 15-minute instantaneous values for some surface variables. Daily and monthly averages of 3D Temperature, 3D Salinity, 3D Zonal and Meridional Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are delivered. Finally, 15-minute instantaneous values of Sea Surface Height and Currents are also given. Product Citation: Please refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag…] http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (Product):https://doi.org/10.48670/moi-00027 https://doi.org/10.48670/moi-00027 428 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-biogeochemistry-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTICSEA_ANALYSISFORECAST_BGC_003_007 Baltic Sea Biogeochemistry Analysis and Forecast Short description: This Baltic Sea biogeochemical model product provides forecasts for the biogeochemical conditions in the Baltic Sea. The Baltic forecast is updated daily providing a new six days forecast. Three different datasets are provided. One with daily means and one with monthly means values for these parameters: nitrate, phosphate, chl-a, ammonium, dissolved oxygen, ph, phytoplankton, zooplankton, silicate, dissolved inorganic carbon, and partial pressure of co2 at the surface. Instantaenous hourly values for the Secchi Depth and light attenuation valid for noon (12Z) are included in the daily mean files/dataset. Additionally a third dataset with daily accumulated values of the netto primary production is available. The product is produced by the biogeochemical model ERGOM (Neumann, 2000) one way coupled to a Baltic Sea set up of the NEMO ocean model, which provides the CMEMS Baltic physical ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). This biogeochemical product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and up to 56 vertical depth levels. The product covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). DOI (product) :https://doi.org/10.48670/moi-00009 https://doi.org/10.48670/moi-00009 429 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/medium-resolution-vegetation-phenology-and-productivity-8 https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum_d1-0.pdf Medium Resolution Vegetation Phenology and Productivity: Largest value for the fitted function during the season (raster 500m), Oct. 2022 The largest value for the fitted function during the season, one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European Medium Resolution Vegetation Phenology and Productivity (MR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The largest value for the fitted function during the season expresses the highest value of the season but may occur on a different date than the peak of season. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The largest value for the fitted function during the season is one of the 13 parameters. The full list is available in the Product User Manual: https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… The largest value time series dataset is made available as raster files with 500x 500m resolution, in ETRS89-LAEA projection corresponding to the MCD43 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2000 onwards. It is updated in the first quarter of each year. The full on-line access to open and free data for this resource will be made available by the end of 2022. Until then the data will be made available 'on-demand' by filling in the form at: https://land.copernicus.eu/contact-form https://land.copernicus.eu/contact-form 430 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-along-track-l3-sea-surface-heights-nrt http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_L3_NRT_OBSERVATIONS_008_044 GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS NRT Short description: Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in near-real time applications. This product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, HY-2B). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details) http://duacs.cls.fr http://duacs.cls.fr “’Associated products”’ A time invariant product http://marine.copernicus.eu/services-portfolio/access-to-products/?opti… [http://marine.copernicus.eu/services-portfolio/access-to-products/?opti…] describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document. http://marine.copernicus.eu/services-portfolio/access-to-products/?opti… http://marine.copernicus.eu/services-portfolio/access-to-products/?opti… DOI (product) :https://doi.org/10.48670/moi-00147 https://doi.org/10.48670/moi-00147 431 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-surface-ocean-colour-plankton-sentinel-3-olci http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_BAL_BGC_L4_NRT_009_132 Baltic Sea Surface Ocean Colour Plankton from Sentinel-3 OLCI L4 monthly observations Short description: For the Baltic Sea Ocean Satellite Observations, the Italian National Research Council (CNR – Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets: * ''plankton'' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) Upstreams: OLCI-S3A & S3B Temporal resolution: monthly Spatial resolution: 300 meters To find this product in the catalogue, use the search keyword ""OCEANCOLOUR_BAL_BGC_L4_NRT"". DOI (product) :https://doi.org/10.48670/moi-00295 https://doi.org/10.48670/moi-00295 432 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-meridional-overturning-circulation-amoc-profile http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_NATLANTIC_amoc_26N_profile Atlantic Meridional Overturning Circulation AMOC profile at 26N from Reanalysis DEFINITION The Atlantic Meridional Overturning profile at 26.5N is obtained by integrating the meridional transport at 26.5 N across the Atlantic basin (zonally) and then doing a cumulative integral in depth. A climatological mean is then taken over time. This is done over the whole time period (1993-2016) and over the period for which there are comparable observations (Apr 2004-Mar 2016). The observations come from the RAPID array (Smeed et al, 2017). CONTEXT The Atlantic Meridional Overturning Circulation (AMOC) transports heat northwards in the Atlantic and plays a key role in regional and global climate (Srokosz et al, 2012). There is a northwards transport in the upper kilometer resulting from northwards flow in the Gulf Stream and wind-driven Ekman transport, and southwards flow in the ocean interior and in deep western boundary currents (Srokosz et al, 2012). There are uncertainties in the deep profile associated with how much transport is returned in the upper (1-3km) or lower (3-5km) North Atlantic deep waters (Roberts et al 2013, Sinha et al 2018). CMEMS KEY FINDINGS The AMOC strength at 1000m is found to be 17.1 ± 4.7 Sv (1 Sverdrup=106m3/s; range is 2 x standard deviation of multi-product). See also Jackson et al (2018). Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00231 https://doi.org/10.48670/moi-00231 433 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/tree-cover-change-mask-2015-2018-raster-20-m-europe-3 https://land.copernicus.eu/pan-european/high-resolution-layers/forests/tree-cover-density/change-maps/tree-cover-change-mask-2015-2018?tab=download Tree Cover Change Mask 2015-2018 (raster 20 m), Europe, 3-yearly, Dec. 2020 The Copernicus High Resolution Forest Layer Tree Cover Change Mask (TCCM) 2015-2018 raster product provides information on the change between the reference years 2015 and 2018 and consists of 4 thematic classes (unchanged areas with no tree cover / new tree cover / loss of tree cover / unchanged areas with tree cover) at 20m spatial resolution and covers EEA38 area and the United Kingdom. The production of the High Resolution Forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. The High Resolution Forest product consists of three types of (status) products and additional change products. The status products are available for the 2012, 2015 and 2018 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. 434 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-climate-monitoring-cci https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-climate-monitoring-cci app-climate-monitoring-cci The "Climate monitoring facility: comparing model and observations datasets" is an interactive web based application designed to demonstrate and study regional and global climate impacts of climate change. It allows users to display Climate Data Records (CDRs), including the essential climate variable (ECV) datasets produced by the European Space Agency (ESA) Climate Change Initiative (CCI) ECV projects, and reanalysis products. The graphical output from this application allows users to study signals in the time-series of monthly average statistics and long-range trends and, where data is available, it is possible to compare observation and model data on the same charts. The geographical regions used in this application contain areas defined in the UN Intergovernmental Panel on Climate Change 6th Assessment Report (IPCC AR6). The AR6 regions are climatologically consistent regions encompassing a representative number of grid boxes. This application is the result of collaboration between Copernicus Climate Change Service (C3S) and the European Space Agency Climate Model User Group (CMUG). It is driven by the ESA CCI ECVs comprising of satellite measurements, ERA5 - the fifth generation ECMWF atmospheric reanalysis of the global climate, and ORAS5 - Ocean Reanalysis System 5. ERA5 currently describes the global history of the atmosphere for the period from 1979 till present time, using a combination of forecast models and data assimilation systems to 'reanalyse' past observations. ORAS5 is the ECMWF OCEAN5 ocean analysis-reanalysis system, it uses the Nucleus for European Modelling of the Ocean (NEMO) ocean model and the NEMOVAR ocean assimilation system. The application provides a choice of several climate variables and data sources, spatial aggregation is presented as selectable regions on the interactive map. Selecting a region produces three charts for that region: Time series of absolute values for a range of statistics: monthly mean, seasonal trends, annual trends. Time series of anomalies for a range of statistics: monthly mean, seasonal trends, annual trends. The statistics are calculated from monthly mean anomalies and the anomalies are calculated with respect to the data availability period with only full years included. Climate stripes for the monthly mean anomalies. Time series of absolute values for a range of statistics: monthly mean, seasonal trends, annual trends. Time series of anomalies for a range of statistics: monthly mean, seasonal trends, annual trends. The statistics are calculated from monthly mean anomalies and the anomalies are calculated with respect to the data availability period with only full years included. Climate stripes for the monthly mean anomalies. User-selectable parameters User-selectable parameters Variable: monthly mean climate fields. Some variables are available from several datasets. Variables from different datasets have undergone a unit conversion to make them homogeneous across different datasets. Data source: source of data. Options are: Observations and Model. Observations cover all satellite observations. Model data cover reanalyses (currently ERA5). Geographical zones (AR6 IPCC defined) for spatial averaging are displayed as a selectable layer on the map and colour coded according to the land-sea mask: light blue - seas only, light orange - land only, light grey - land and seas. Variable: monthly mean climate fields. Some variables are available from several datasets. Variables from different datasets have undergone a unit conversion to make them homogeneous across different datasets. Data source: source of data. Options are: Observations and Model. Observations cover all satellite observations. Model data cover reanalyses (currently ERA5). Geographical zones (AR6 IPCC defined) for spatial averaging are displayed as a selectable layer on the map and colour coded according to the land-sea mask: light blue - seas only, light orange - land only, light grey - land and seas. INPUT VARIABLES Name Units Description Source Aerosol optical depth ~ Aerosol optical depth (AOD, or sometimes Aerosol optical thickness) is a measure of the degree to which transmission of light through a volume of atmosphere is reduced due to extinction (scattering and absorption) by aerosol. It is equivalent to the integral of the extinction coefficient over a vertical column of unit cross section. Typical global average aerosol optical depth is about 0.15; in rare cases atmospheric aerosol optical depth can reach 3. Typically aerosol optical depth observations are reported at the mid-visible reference wavelength of 550 nm. Sensor on satellite: AATSR on Envisat; Algorithm: ENS (product based on an ensemble of algorithms); Version: 3.0. Satellite aerosol properties Carbon dioxide ppm Column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) L3 product derived from satellite observations. Average molar mixing ratio (or mole fraction in micro mole carbon dioxide (CO2) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CO2 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the 'X' in XCO2 indicates that the reported quantity is a 'mole fraction'. Satellite carbon dioxide Chlorophyll-A mg m-3 Mass chlorophyll-a per unit of volume of near-surface water. Ocean colour Methane ppb Column-average dry-air mole fraction of atmospheric methane (XCH4) L3 product derived from satellite observations. Average molar mixing ratio (or mole fraction in micro mole methane (CH4) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CH4 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the 'X' in XCH4 indicates that the reported quantity is a 'mole fraction'. Satellite methane Sea ice concentration % This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land-sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover. ERA5 Sea ice concentration satellite % Original ('raw') estimates of sea ice concentration produced by the algorithm before the application of various processing filters, the ESA CCI AMRS product. Satellite Sea Ice Concentration Sea surface height m Vertical distance between the actual sea surface and a reference surface of constant geopotential with which mean sea level would coincide if the ocean were at rest. This variable is a 2D field. ORAS5 Sea surface height satellite m This variable is an absolute dynamic topography. Sea surface height above the geoid computed as the sum of the sea level anomaly with the mean dynamic topography. Satellite Sea Level Sea surface temperature oC This parameter (SST) is the temperature of sea water near the surface. In ERA5, this parameter is given by HadISST2 before September 2007 and OSTIA from September 2007 onwards. In observations, this parameter is the global and spatially complete estimate of daily average ocean temperature adjusted to a standard depth of 20 cm; available as a Combined Product at Level 4. ERA5, Satellite SST Soil moisture % This parameter (SM) is the volume of water in the upper soil level. In ERA5, this parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). In observations, this parameter is the content of liquid water in a surface soil layer of 2 to 5 cm depth expressed as the percentage of total saturation. ERA5, Satellite SM Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. In ERA5, this parameter can also be referred to as total ozone, or vertically integrated ozone. In observations, the parent variable is the Atmosphere mole content of ozone: Vertical integration from the surface to the top of the atmosphere of the number of moles of ozone above a unit area; it was converted to kg m-2. ERA5, Satellite ozone INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Aerosol optical depth ~ Aerosol optical depth (AOD, or sometimes Aerosol optical thickness) is a measure of the degree to which transmission of light through a volume of atmosphere is reduced due to extinction (scattering and absorption) by aerosol. It is equivalent to the integral of the extinction coefficient over a vertical column of unit cross section. Typical global average aerosol optical depth is about 0.15; in rare cases atmospheric aerosol optical depth can reach 3. Typically aerosol optical depth observations are reported at the mid-visible reference wavelength of 550 nm. Sensor on satellite: AATSR on Envisat; Algorithm: ENS (product based on an ensemble of algorithms); Version: 3.0. Satellite aerosol properties Aerosol optical depth ~ Aerosol optical depth (AOD, or sometimes Aerosol optical thickness) is a measure of the degree to which transmission of light through a volume of atmosphere is reduced due to extinction (scattering and absorption) by aerosol. It is equivalent to the integral of the extinction coefficient over a vertical column of unit cross section. Typical global average aerosol optical depth is about 0.15; in rare cases atmospheric aerosol optical depth can reach 3. Typically aerosol optical depth observations are reported at the mid-visible reference wavelength of 550 nm. Sensor on satellite: AATSR on Envisat; Algorithm: ENS (product based on an ensemble of algorithms); Version: 3.0. Satellite aerosol properties Satellite aerosol properties Carbon dioxide ppm Column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) L3 product derived from satellite observations. Average molar mixing ratio (or mole fraction in micro mole carbon dioxide (CO2) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CO2 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the 'X' in XCO2 indicates that the reported quantity is a 'mole fraction'. Satellite carbon dioxide Carbon dioxide ppm Column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) L3 product derived from satellite observations. Average molar mixing ratio (or mole fraction in micro mole carbon dioxide (CO2) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CO2 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the 'X' in XCO2 indicates that the reported quantity is a 'mole fraction'. Satellite carbon dioxide Satellite carbon dioxide Chlorophyll-A mg m-3 Mass chlorophyll-a per unit of volume of near-surface water. Ocean colour Chlorophyll-A mg m-3 Mass chlorophyll-a per unit of volume of near-surface water. Ocean colour Ocean colour Methane ppb Column-average dry-air mole fraction of atmospheric methane (XCH4) L3 product derived from satellite observations. Average molar mixing ratio (or mole fraction in micro mole methane (CH4) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CH4 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the 'X' in XCH4 indicates that the reported quantity is a 'mole fraction'. Satellite methane Methane ppb Column-average dry-air mole fraction of atmospheric methane (XCH4) L3 product derived from satellite observations. Average molar mixing ratio (or mole fraction in micro mole methane (CH4) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CH4 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the 'X' in XCH4 indicates that the reported quantity is a 'mole fraction'. Satellite methane Satellite methane Sea ice concentration % This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land-sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover. ERA5 Sea ice concentration % This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land-sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover. ERA5 ERA5 Sea ice concentration satellite % Original ('raw') estimates of sea ice concentration produced by the algorithm before the application of various processing filters, the ESA CCI AMRS product. Satellite Sea Ice Concentration Sea ice concentration satellite % Original ('raw') estimates of sea ice concentration produced by the algorithm before the application of various processing filters, the ESA CCI AMRS product. Satellite Sea Ice Concentration Satellite Sea Ice Concentration Sea surface height m Vertical distance between the actual sea surface and a reference surface of constant geopotential with which mean sea level would coincide if the ocean were at rest. This variable is a 2D field. ORAS5 Sea surface height m Vertical distance between the actual sea surface and a reference surface of constant geopotential with which mean sea level would coincide if the ocean were at rest. This variable is a 2D field. ORAS5 ORAS5 Sea surface height satellite m This variable is an absolute dynamic topography. Sea surface height above the geoid computed as the sum of the sea level anomaly with the mean dynamic topography. Satellite Sea Level Sea surface height satellite m This variable is an absolute dynamic topography. Sea surface height above the geoid computed as the sum of the sea level anomaly with the mean dynamic topography. Satellite Sea Level Satellite Sea Level Sea surface temperature oC This parameter (SST) is the temperature of sea water near the surface. In ERA5, this parameter is given by HadISST2 before September 2007 and OSTIA from September 2007 onwards. In observations, this parameter is the global and spatially complete estimate of daily average ocean temperature adjusted to a standard depth of 20 cm; available as a Combined Product at Level 4. ERA5, Satellite SST Sea surface temperature oC This parameter (SST) is the temperature of sea water near the surface. In ERA5, this parameter is given by HadISST2 before September 2007 and OSTIA from September 2007 onwards. In observations, this parameter is the global and spatially complete estimate of daily average ocean temperature adjusted to a standard depth of 20 cm; available as a Combined Product at Level 4. ERA5, Satellite SST ERA5 Satellite SST Soil moisture % This parameter (SM) is the volume of water in the upper soil level. In ERA5, this parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). In observations, this parameter is the content of liquid water in a surface soil layer of 2 to 5 cm depth expressed as the percentage of total saturation. ERA5, Satellite SM Soil moisture % This parameter (SM) is the volume of water in the upper soil level. In ERA5, this parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). In observations, this parameter is the content of liquid water in a surface soil layer of 2 to 5 cm depth expressed as the percentage of total saturation. ERA5, Satellite SM ERA5 Satellite SM Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. In ERA5, this parameter can also be referred to as total ozone, or vertically integrated ozone. In observations, the parent variable is the Atmosphere mole content of ozone: Vertical integration from the surface to the top of the atmosphere of the number of moles of ozone above a unit area; it was converted to kg m-2. ERA5, Satellite ozone Total column ozone kg m-2 This parameter is the total amount of ozone in a column of air extending from the surface of the Earth to the top of the atmosphere. In ERA5, this parameter can also be referred to as total ozone, or vertically integrated ozone. In observations, the parent variable is the Atmosphere mole content of ozone: Vertical integration from the surface to the top of the atmosphere of the number of moles of ozone above a unit area; it was converted to kg m-2. ERA5, Satellite ozone ERA5 Satellite ozone OUTPUT VARIABLES Name Units Description Area averaged absolute values Varies Absolute values, averaged over a selected geographical domain, for a range of statistics. Area averaged anomalies Varies Anomalies with respect to a selected climate reference interval, averaged over a selected geographical domain, for a range of statistics. Climatological period used in this application equals to the data availability period minus 1 year. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Area averaged absolute values Varies Absolute values, averaged over a selected geographical domain, for a range of statistics. Area averaged absolute values Varies Absolute values, averaged over a selected geographical domain, for a range of statistics. Area averaged anomalies Varies Anomalies with respect to a selected climate reference interval, averaged over a selected geographical domain, for a range of statistics. Climatological period used in this application equals to the data availability period minus 1 year. Area averaged anomalies Varies Anomalies with respect to a selected climate reference interval, averaged over a selected geographical domain, for a range of statistics. Climatological period used in this application equals to the data availability period minus 1 year. 435 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/tree-cover-density-mask-2012-2015-raster-20-m-europe-3 https://land.copernicus.eu/pan-european/high-resolution-layers/forests/tree-cover-density/change-maps/tree-cover-change-mask-2012-2015?tab=download Tree Cover Density Mask 2012-2015 (raster 20 m), Europe, 3-yearly, Dec. 2020 The Copernicus High Resolution Forest Layer Tree Cover Change Mask (TCCM) 2012-2015 raster product provides information on the change between the reference years 2012 and 2015 and consists of 4 thematic classes (unchanged areas with no tree cover / new tree cover / loss of tree cover / unchanged areas with tree cover) at 20m spatial resolution and covers EEA38 area and the United Kingdom. The production of the High Resolution Forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. The High Resolution Forest product consists of three types of (status) products and additional change products. The status products are available for the 2012, 2015 and 2018 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. 436 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cems-glofas-reforecast https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-reforecast cems-glofas-reforecast This dataset provides a gridded modelled time series of river discharge, forced with medium- to sub-seasonal range meteorological reforecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation). This dataset was produced by forcing the open-source LISFLOOD hydrological model with input from the European Centre for Medium-range Weather Forecasts (ECMWF) 11-member ensemble ECMWF-ENS reforecasts. Reforecasts are forecasts run over past dates, and those presented here are used for providing a suitably long time period against which the skill of the 30-day real-time operational forecast can be assessed. The reforecasts are initialised twice weekly with lead times up to 46 days, at 24-hour steps for 20 years in the recent history. For more specific information on the how the reforecast dataset is produced we refer to the documentation. Companion datasets, also available through the Climate Data Store (CDS), are the operational forecasts, historical simulations that can be used to derive the hydrological climatology, and seasonal forecasts and reforecasts for users looking for long term forecasts. For users looking specifically for European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS), which is managed, technically implemented and developed by the European Commission’s Joint Research Centre. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal resolution 0.05° x 0.05° for version 4.0, 0.1° x 0.1° for version 3.1 and older Vertical resolution Surface level for river discharge Temporal coverage For the operational version reforecasts are provided for years for 2003 to 2022 (inclusive); they are produced for calendar dates from March 2023 to near real-time. Temporal resolution Reforecasts are initialised at 00 UTC twice weekly with a 24-hour time step and 46-day lead time File format GRIB2 Conventions WMO standards for GRIB2 Versions Current version - GloFAS v4.0 released 2023-07-26. For more information on versions we refer to the documentation Update frequency Weekly for the operational version DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal resolution 0.05° x 0.05° for version 4.0, 0.1° x 0.1° for version 3.1 and older Horizontal resolution 0.05° x 0.05° for version 4.0, 0.1° x 0.1° for version 3.1 and older Vertical resolution Surface level for river discharge Vertical resolution Surface level for river discharge Temporal coverage For the operational version reforecasts are provided for years for 2003 to 2022 (inclusive); they are produced for calendar dates from March 2023 to near real-time. Temporal coverage For the operational version reforecasts are provided for years for 2003 to 2022 (inclusive); they are produced for calendar dates from March 2023 to near real-time. Temporal resolution Reforecasts are initialised at 00 UTC twice weekly with a 24-hour time step and 46-day lead time Temporal resolution Reforecasts are initialised at 00 UTC twice weekly with a 24-hour time step and 46-day lead time File format GRIB2 File format GRIB2 Conventions WMO standards for GRIB2 Conventions WMO standards for GRIB2 Versions Current version - GloFAS v4.0 released 2023-07-26. For more information on versions we refer to the documentation Versions Current version - GloFAS v4.0 released 2023-07-26. For more information on versions we refer to the documentation Update frequency Weekly for the operational version Update frequency Weekly for the operational version MAIN VARIABLES Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over the 24-hour time step. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over the 24-hour time step. River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over the 24-hour time step. RELATED VARIABLES Name Units Description Elevation m The mean height elevation above sea level for each pixel in the GloFAS domain. Accessible via the link in the Documentation tab. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Accessible via the link in the Documentation tab. RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Elevation m The mean height elevation above sea level for each pixel in the GloFAS domain. Accessible via the link in the Documentation tab. Elevation m The mean height elevation above sea level for each pixel in the GloFAS domain. Accessible via the link in the Documentation tab. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Accessible via the link in the Documentation tab. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Accessible via the link in the Documentation tab. 437 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-european-north-west-shelf-ocean-physics-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013 Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast Short description: The ocean physics analysis and forecast for the North-West European Shelf is produced using a forecasting ocean assimilation model, with tides, at 1.5 km horizontal resolution coupled with a wave model. The ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature, vertical profiles of temperature and salinity, and along track satellite sea level anomaly data. The model is forced by lateral boundary conditions from the UK Met Office North Atlantic Ocean forecast model and by the CMEMS Baltic forecast product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… BALTICSEA_ANALYSISFORECAST_PHY_003_006]. The atmospheric forcing is given by the operational ECMWF Numerical Weather Prediction model. The river discharge is from a daily climatology. Further details of the model, including the product validation are provided in the [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004… CMEMS-NWS-QUID-004-013]. The wave model is described in [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014]. Products are provided as hourly instantaneous, quarter-hourly, and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 33 standard geopotential depths (z-levels) and from the model rotated grid to a regular lat-lon grid. The product is updated daily, providing a 6-day forecast and the previous 2-day assimilative hindcast. See [http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-0… CMEMS-NWS-PUM-004-013_014] for further details. https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004… https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-0… Associated products: [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014]. https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… DOI (product) :https://doi.org/10.48670/moi-00054 https://doi.org/10.48670/moi-00054 438 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-colour-plankton-my-l4-monthly-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_GLO_BGC_L4_MY_009_108 Global Ocean Colour Plankton MY L4 monthly observations Short description: For the Global Ocean Satellite Observations, Brockmann Consult (BC) is providing Bio-Geo_Chemical (BGC) products based on the ESA-CCI inputs. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the ""multi"" products. * Variables: Chlorophyll-a (CHL). * Temporal resolutions: monthly. * Spatial resolutions: 4 km (multi). * Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY). To find these products in the catalogue, use the search keyword ""ESA-CCI"". DOI (product) :https://doi.org/10.48670/moi-00283 https://doi.org/10.48670/moi-00283 439 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-waves-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MEDSEA_MULTIYEAR_WAV_006_012 Mediterranean Sea Waves Reanalysis Short description: MEDSEA_MULTIYEAR_WAV_006_012 is the multi-year wave product of the Mediterranean Sea Waves forecasting system (Med-WAV). It contains a Reanalysis dataset and an Interim dataset covering the period after the reanalysis until 1 month before present. The Reanalysis dataset is a multi-year wave reanalysis starting from January 1993, composed by hourly wave parameters at 1/24° horizontal resolution, covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The Med-WAV modelling system is based on wave model WAM 4.6.2 and has been developed as a nested sequence of two computational grids (coarse and fine) to ensure that swell propagating from the North Atlantic (NA) towards the strait of Gibraltar is correctly entering the Mediterranean Sea. The coarse grid covers the North Atlantic Ocean from 75°W to 10°E and from 70° N to 10° S in 1/6° resolution while the nested fine grid covers the Mediterranean Sea from 18.125° W to 36.2917° E and from 30.1875° N to 45.9792° N with a 1/24° resolution. The modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins. The wave system also includes an optimal interpolation assimilation scheme assimilating significant wave height along track satellite observations available through CMEMS and it is forced with daily averaged currents from Med-Physics and with 1-h, 0.25° horizontal-resolution ERA5 reanalysis 10m-above-sea-surface winds from ECMWF. Product Citation: Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (Product):https://doi.org/10.25423/cmcc/medsea_multiyear_wav_006_012 https://doi.org/10.25423/cmcc/medsea_multiyear_wav_006_012 DOI (Interim dataset):https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I 440 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-chlorophyll-time-series-and-trend http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_HEALTH_CHL_ARCTIC_OCEANCOLOUR_area_averaged_mean Arctic Ocean Chlorophyll-a time series and trend from Observations Reprocessing DEFINITION The time series are derived from the regional chlorophyll reprocessed (REP) products as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithms to remote sensing reflectances (Rrs) provided by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020). Daily regional mean values are calculated by performing the average (weighted by pixel area) over the region of interest. A fixed annual cycle is extracted from the original signal, using the Census-I method as described in Vantrepotte et al. (2009). The deasonalised time series is derived by subtracting the seasonal cycle from the original time series, and then fitted to a linear regression to, finally, obtain the linear trend. CONTEXT Phytoplankton – and chlorophyll concentration , which is a measure of phytoplankton concentration – respond rapidly to changes in environmental conditions. Chlorophyll concentration is highly seasonal in the Arctic Ocean region due to a strong dependency on light and nutrient availability, which in turn are driven by seasonal sunlight and sea-ice cover dynamics, as well as changes in mixed layer. In the past two decades, an increase in annual net primary production by Arctic Ocean phytoplankton has been observed and linked to sea-ice decline (Arrigo and van Dijken, 2015); in the same line Kahru et al. (2011) have showed that chlorophyll concentration peaks are appearing increasingly earlier in the year in parts of the Arctic. It is therefore of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales in the area, in order to be able to separate potential long-term climate signals from natural variability in the short term. CMEMS KEY FINDINGS While the overall trend average for the 1997-2021 period in the Arctic Sea is positive (0.86 ± 0.17 % per year), a continued plateau in the linear trend, initiated in 2013 is observed in the time series extension, with both the amplitude and the baseline of the cycle continuing to decrease during 2021 as reported for previous years (Sathyendranath et al., 2018). In particular, the annual average for the region in 2021 is 1.05 mg m-3 - a 30% reduction on 2020 values. There appears to be no appreciable changes in the timings or amplitude of the 2021 spring and autumn blooms. DOI (product):https://doi.org/10.48670/moi-00188 https://doi.org/10.48670/moi-00188 441 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/small-woody-features-2018-raster-100-m-europe-3-yearly https://land.copernicus.eu/pan-european/high-resolution-layers/small-woody-features/small-woody-features-2018?tab=download Small Woody Features 2018 (raster 100 m), Europe, 3-yearly, May 2023 High Resolution land cover characteristics for the 2018 reference year. Small woody landscape features are important vectors of biodiversity and provide information on fragmentation of habitats with a direct potential for restoration while also providing a link to hazard protection and green infrastructure, amongst others. VHR_IMAGE_2018 made available in the ESA Copernicus DWH will be the main data source for the detection of small woody features identifiable within the given image resolution. The Small Woody Features (SWFs) layer contains woody linear and patchy elements but will not be further differentiated into trees, hedges, bushes and scrub. The spatial pattern shall be limited to linear structures and isolated patches on the basis of geometric characteristics. 442 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/small-woody-features-2018-vector-europe-3-yearly-may-2023 https://land.copernicus.eu/pan-european/high-resolution-layers/small-woody-features/small-woody-features-2018?tab=download Small Woody Features 2018 (vector), Europe, 3-yearly, May 2023 High Resolution land cover characteristics for the 2018 reference year. Small woody landscape features are important vectors of biodiversity and provide information on fragmentation of habitats with a direct potential for restoration while also providing a link to hazard protection and green infrastructure, amongst others. VHR_IMAGE_2018 made available in the ESA Copernicus DWH will be the main data source for the detection of small woody features identifiable within the given image resolution. The Small Woody Features (SWFs) layer contains woody linear and patchy elements but will not be further differentiated into trees, hedges, bushes and scrub. The spatial pattern shall be limited to linear structures and isolated patches on the basis of geometric characteristics. 443 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/small-woody-features-2018-raster-5-m-europe-3-yearly-may https://land.copernicus.eu/pan-european/high-resolution-layers/small-woody-features/small-woody-features-2018?tab=download Small Woody Features 2018 (raster 5 m), Europe, 3-yearly, May 2023 High Resolution land cover characteristics for the 2018 reference year. Small woody landscape features are important vectors of biodiversity and provide information on fragmentation of habitats with a direct potential for restoration while also providing a link to hazard protection and green infrastructure, amongst others. VHR_IMAGE_2018 made available in the ESA Copernicus DWH will be the main data source for the detection of small woody features identifiable within the given image resolution. The Small Woody Features (SWFs) layer contains woody linear and patchy elements but will not be further differentiated into trees, hedges, bushes and scrub. The spatial pattern shall be limited to linear structures and isolated patches on the basis of geometric characteristics. 444 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-ostia-sea-surface-temperature-and-sea-ice-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_011 Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed Short description : The OSTIA (Good et al., 2020) global sea surface temperature reprocessed product provides daily gap-free maps of foundation sea surface temperature and ice concentration (referred to as an L4 product) at 0.05deg.x 0.05deg. horizontal grid resolution, using in-situ and satellite data. This product provides the foundation Sea Surface Temperature, which is the temperature free of diurnal variability. DOI (product) :https://doi.org/10.48670/moi-00168 https://doi.org/10.48670/moi-00168 445 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/season-minimum-value-2017-present-raster-10-m-europe https://www.wekeo.eu/data?view=viewer&t=1577905116279&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1QSEVOT0xPR1ktQU5ELVBST0RVQ1RJVklUWS1QQVJBTUVURVJTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVlBQX01JTlZfU0VBU09OMV8xME0iLCJ6SW5kZXgiOjgwfV0%3D&initial=1 Season Minimum Value 2017-present (raster 10 m), Europe, yearly, Sept. 2021 The Minimum Value (MINV), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Minimum Value (MINV) is the average Plant Phenology Index (PPI) value of the minima before the growing season. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Minimum Value is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-m… https://land.copernicus.eu/user-corner/technical-library/product-user-m… A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The MINV dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year. 446 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cems-glofas-seasonal https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-seasonal cems-glofas-seasonal This dataset provides a gridded modelled time series of river discharge, forced with seasonal range meteorological forecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation). This dataset was produced by forcing the open-source LISFLOOD hydrological model at a 0.05° (~5 km at the equator) resolution with seasonal meteorological forecasts from the European Centre for Medium-range Weather Forecasts (ECMWF). For version 3.1 and older, the open-source LISFLOOD hydrological model was forced at a 0.1° (~11 km at the equator) resolution. The forecasts are initialised on the first of each month with a 24-hourly time step, and cover 123 days. Companion datasets, also available through the Climate Data Store (CDS), are the operational forecasts, historical simulations that can be used to derive the hydrological climatology, and medium-range and seasonal reforecasts. The latter dataset enables research, local skill assessment and post-processing of the seasonal forecasts. In addition, the seasonal reforecasts are also used to derive a specific range dependent climatology for the seasonal system. For users looking specifically for European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS), which is managed, technically implemented and developed by the European Commission’s Joint Research Centre. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal resolution 0.05° x 0.05° for version 4.0, 0.1° x 0.1° for version 3.1 and older Vertical resolution Surface level for river discharge Temporal coverage 1 December 2020 to near-real time for operational, and various dates for legacy versions Temporal resolution Forecasts are initialised the first of each month with a 24-hourly time step, and cover 123 days File format GRIB2 Conventions WMO standards for GRIB2 Versions Current version - GloFAS v4.0 released 2023-07-26. For more information on versions we refer to the documentation Update frequency Monthly, made available on the 10th of each month DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal resolution 0.05° x 0.05° for version 4.0, 0.1° x 0.1° for version 3.1 and older Horizontal resolution 0.05° x 0.05° for version 4.0, 0.1° x 0.1° for version 3.1 and older Vertical resolution Surface level for river discharge Vertical resolution Surface level for river discharge Temporal coverage 1 December 2020 to near-real time for operational, and various dates for legacy versions Temporal coverage 1 December 2020 to near-real time for operational, and various dates for legacy versions Temporal resolution Forecasts are initialised the first of each month with a 24-hourly time step, and cover 123 days Temporal resolution Forecasts are initialised the first of each month with a 24-hourly time step, and cover 123 days File format GRIB2 File format GRIB2 Conventions WMO standards for GRIB2 Conventions WMO standards for GRIB2 Versions Current version - GloFAS v4.0 released 2023-07-26. For more information on versions we refer to the documentation Versions Current version - GloFAS v4.0 released 2023-07-26. For more information on versions we refer to the documentation Update frequency Monthly, made available on the 10th of each month Update frequency Monthly, made available on the 10th of each month MAIN VARIABLES Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over the 24-hour time step. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over the 24-hour time step. River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over the 24-hour time step. RELATED VARIABLES Name Units Description Elevation m The mean height elevation above sea level for each pixel in the GloFAS domain. Accessible via the link in the Documentation tab. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Accessible via the link in the Documentation tab. RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Elevation m The mean height elevation above sea level for each pixel in the GloFAS domain. Accessible via the link in the Documentation tab. Elevation m The mean height elevation above sea level for each pixel in the GloFAS domain. Accessible via the link in the Documentation tab. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Accessible via the link in the Documentation tab. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Accessible via the link in the Documentation tab. 447 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-ensemble-physics-reanalysis-low-resolution http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_026 Global Ocean Ensemble Physics Reanalysis - Low resolution Short description: You can find here the new Mercator Ocean (Toulouse, FR) Global Ocean Ensemble Reanalysis: monthly means of Temperature, Salinity, Currents and Ice variables at 1 degree horizontal resolution for 75 vertical levels, starting from 1993 onward. Global ocean reanalyses are homogeneous 3D gridded descriptions of the physical state of the ocean spanning several decades, produced with a numerical ocean model constrained with data assimilation of satellite and in situ observations. The multi-model ensemble approach allows uncertainties or error bars in the ocean state to be estimated. The ensemble mean may even provide, for certain regions and/or periods, a more reliable estimate than any individual reanalysis product. The four reanalyses, used to create the ensemble, covering “altimetric era” period (starting from 1st of January 1993) during which altimeter altimetry data observations are available: * GLORYS2V4 from Mercator Ocean (Fr) ; * ORAS5 from ECMWF ; * GloSea5 from Met Office (UK) ; * and C-GLORS05 from CMCC (It). ; provided as four different time series of global ocean simulations 3D monthly estimates, post-processed to create this Global Reanalysis Ensemble Product (GREP). Available variables are temperature, salinity, velocities and ice variables. These reanalyses are built to be as close as possible to the observations (i.e. realistic) and in agreement with the model physics. The numerical products available for users are monthly mean averages describing the ocean from surface to bottom (5900 m). DOI (product) :https://doi.org/10.48670/moi-00023 https://doi.org/10.48670/moi-00023 448 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-ocean-colour-plankton-reflectance-transparency http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_BLK_BGC_L3_NRT_009_151 Black Sea Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations Short description: For the Black Sea Ocean Satellite Observations, the Italian National Research Council (CNR – Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets: * ''plankton'' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm * ''reflectance'' with the spectral Remote Sensing Reflectance (RRS) * ''transparency'' with the diffuse attenuation coefficient of light at 490 nm (KD490) * ''optics'' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates) Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the ""multi"" products, and OLCI-S3A & S3B for the ""olci"" products Temporal resolution: daily Spatial resolutions: 1 km for ""multi"" and 300 meters for ""olci"" To find this product in the catalogue, use the search keyword ""OCEANCOLOUR_BLK_BGC_L3_NRT"". DOI (product) :https://doi.org/10.48670/moi-00301 https://doi.org/10.48670/moi-00301 449 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/end-season-value-2017-present-raster-10-m-europe-yearly https://www.wekeo.eu/data?view=viewer&t=1562219742857&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1QSEVOT0xPR1ktQU5ELVBST0RVQ1RJVklUWS1QQVJBTUVURVJTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVlBQX0VPU1ZfU0VBU09OMV8xME0iLCJ6SW5kZXgiOjgwfV0%3D&initial=1 End-of-season Value 2017-present (raster 10 m), Europe, yearly, Sept. 2021 The End-of-Season Value (EOSV), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The End-of-Season Value (EOSV) provides the value of the Plant Phenology Index (PPI) at the end of the vegetation growing season. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The End-of-Season Value is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-m…. https://land.copernicus.eu/user-corner/technical-library/product-user-m… A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The EOSV dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year. 450 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-sea-surface-temperature-reprocessed http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 Baltic Sea- Sea Surface Temperature Reprocessed Short description: For the Baltic Sea- The DMI Sea Surface Temperature reprocessed analysis aims at providing daily gap-free maps of sea surface temperature, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. The product uses SST satellite products from the ESA CCI and Copernicus C3S projects, including the sensors: NOAA AVHRRs 7, 9, 11, 12, 14, 15, 16, 17, 18 , 19, Metop, ATSR1, ATSR2, AATSR and SLSTR. DOI (product) :https://doi.org/10.48670/moi-00156 https://doi.org/10.48670/moi-00156 451 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-l3-spectral-parameters-nrt-satellite http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=WAVE_GLO_WAV_L3_SPC_NRT_OBSERVATIONS_014_002 GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS Short description: Near-Real-Time mono-mission satellite-based integral parameters derived from the directional wave spectra. Using linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered. The dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land). The integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.This product is processed by the WAVE-TAC multi-mission SAR data processing system. It serves in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes near-real-time data from the following SAR missions: Sentinel-1A and Sentinel-1B.One file is produced for each mission and is available in two formats: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land. DOI (product) :https://doi.org/10.48670/moi-00178 https://doi.org/10.48670/moi-00178 452 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-ensemble-physics-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_031 Global Ocean Ensemble Physics Reanalysis Short description: You can find here the CMEMS Global Ocean Ensemble Reanalysis product at ¼ degree resolution : monthly means of Temperature, Salinity, Currents and Ice variables for 75 vertical levels, starting from 1993 onward. Global ocean reanalyses are homogeneous 3D gridded descriptions of the physical state of the ocean covering several decades, produced with a numerical ocean model constrained with data assimilation of satellite and in situ observations. These reanalyses are built to be as close as possible to the observations (i.e. realistic) and in agreement with the model physics The multi-model ensemble approach allows uncertainties or error bars in the ocean state to be estimated. The ensemble mean may even provide for certain regions and/or periods a more reliable estimate than any individual reanalysis product. The four reanalyses, used to create the ensemble, covering “altimetric era” period (starting from 1st of January 1993) during which altimeter altimetry data observations are available: * GLORYS2V4 from Mercator Ocean (Fr); * ORAS5 from ECMWF; * GloSea5 from Met Office (UK); * and C-GLORSv7 from CMCC (It); These four products provided four different time series of global ocean simulations 3D monthly estimates. All numerical products available for users are monthly or daily mean averages describing the ocean. DOI (product) :https://doi.org/10.48670/moi-00024 https://doi.org/10.48670/moi-00024 453 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/season-length-2017-present-raster-10-m-europe-yearly-sept https://www.wekeo.eu/data?view=viewer&t=1577905116279&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMSIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1QSEVOT0xPR1ktQU5ELVBST0RVQ1RJVklUWS1QQVJBTUVURVJTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVlBQX0xFTkdUSF9TRUFTT04xXzEwTSIsInpJbmRleCI6ODB9XQ%3D%3D Season Length 2017-present (raster 10 m), Europe, yearly, Sept. 2021 The Season Length (LENGTH), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Season Length is the number of days between the start and end dates of the vegetation growing season in the time profile of the Plant Phenology Index (PPI). The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Season Length is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-m… https://land.copernicus.eu/user-corner/technical-library/product-user-m… A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The LENGTH dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year. 454 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/n2k-change-2006-2012-vector-europe-6-yearly-jul-2021 https://land.copernicus.eu/local/natura/n2k-change-2006-2012?tab=download N2K Change 2006-2012 (vector), Europe, 6-yearly, Jul. 2021 This metadata refers to the CLMS N2K Change 2006-2012 product, the Copernicus Land Cover/Land Use (LC/LU) change map tailored to the needs of biodiversity monitoring in selected Natura2000 sites: 4790 sites of natural and semi-natural grassland formations listed in Annex I of the Habitats Directive, including a 2km buffer zone surrounding the sites. The change mapping exercise between the reference years 2006 and 2012, based on the outputs of the status layers, occurred over an area of 631.800 km² across Europe (i.e. EU27, the United Kingdom and Switzerland). The change dataset only shows the areas that have changed between 2006 and 2012, covering an area of 1.054.684 ha. LC/LU is extracted from VHR satellite data and other available data. Change detection is based on the analysis of VHR satellite data from the reference years 2006 ±2 years and 2012 ±2 years. The production of N2K updates was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 455 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-colour-plankton-and-reflectances-my-l3-daily http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_GLO_BGC_L3_MY_009_107 Global Ocean Colour Plankton and Reflectances MY L3 daily observations Short description: For the Global Ocean Satellite Observations, Brockmann Consult (BC) is providing Bio-Geo_Chemical (BGC) products based on the ESA-CCI inputs. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the ""multi"" products. * Variables: Chlorophyll-a (CHL), Phytoplankton Functional types and sizes (PFT) and Reflectance (RRS). * Temporal resolutions: daily, monthly. * Spatial resolutions: 4 km (multi). * Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY). To find these products in the catalogue, use the search keyword ""ESA-CCI"". DOI (product) :https://doi.org/10.48670/moi-00282 https://doi.org/10.48670/moi-00282 456 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-colour-plankton-and-transparency-l4-nrt http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_ARC_BGC_L4_NRT_009_122 Arctic Ocean Colour Plankton and Transparency L4 NRT monthly observations Short description: For the Arctic Ocean Satellite Observations, Italian National Research Council (CNR – Rome, Italy) is providing Bio-Geo_Chemical (BGC) products. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the ""multi"" products, and S3A & S3B only for the ""olci"" products. * Variables: Chlorophyll-a (CHL) and Diffuse Attenuation (KD490). * Temporal resolutions:monthly. * Spatial resolutions: 1 km (multi) or 300 meters (olci). * Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY). DOI (product) :https://doi.org/10.48670/moi-00291 https://doi.org/10.48670/moi-00291 457 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-europe-air-quality-forecasts https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-forecasts cams-europe-air-quality-forecasts This dataset provides daily air quality analyses and forecasts for Europe. CAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of eleven air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the eleven models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels. Note that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Horizontal coverage Europe (west boundary=25.0° W, east=45.0° E, south=30.0° N, north=72.0°) Horizontal resolution 0.1°x0.1° (10 km x 10 km) Vertical coverage Surface, 50m, 100m, 250m, 500m, 750m, 1000m, 2000m, 3000m, 5000m Temporal coverage three-year rolling archive Temporal resolution 1-hourly File format GRIB, NetCDF Update frequency daily DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Europe (west boundary=25.0° W, east=45.0° E, south=30.0° N, north=72.0°) Horizontal coverage Europe (west boundary=25.0° W, east=45.0° E, south=30.0° N, north=72.0°) Horizontal resolution 0.1°x0.1° (10 km x 10 km) Horizontal resolution 0.1°x0.1° (10 km x 10 km) Vertical coverage Surface, 50m, 100m, 250m, 500m, 750m, 1000m, 2000m, 3000m, 5000m Vertical coverage Surface, 50m, 100m, 250m, 500m, 750m, 1000m, 2000m, 3000m, 5000m Temporal coverage three-year rolling archive Temporal coverage three-year rolling archive Temporal resolution 1-hourly Temporal resolution 1-hourly File format GRIB, NetCDF File format GRIB, NetCDF Update frequency daily Update frequency daily MAIN VARIABLES Name Units Alder pollen grains m-3 Ammonia GRIB: kg m-3; netCDF: µg m-3 Birch pollen grains m-3 Carbon monoxide GRIB: kg m-3; netCDF: µg m-3 Dust GRIB: kg m-3; netCDF: µg m-3 Formaldehyde GRIB: kg m-3; netCDF: µg m-3 Glyoxal GRIB: kg m-3; netCDF: µg m-3 Grass pollen grains m-3 Mugwort pollen grains m-3 Nitrogen dioxide GRIB: kg m-3; netCDF: µg m-3 Nitrogen monoxide GRIB: kg m-3; netCDF: µg m-3 Non-methane VOCs GRIB: kg m-3; netCDF: µg m-3 Olive pollen grains m-3 Ozone GRIB: kg m-3; netCDF: µg m-3 PM10, wildfires only GRIB: kg m-3; netCDF: µg m-3 Particulate matter < 10 µm (PM10) GRIB: kg m-3; netCDF: µg m-3 Particulate matter < 2.5 µm (PM2.5) GRIB: kg m-3; netCDF: µg m-3 Peroxyacyl nitrates GRIB: kg m-3; netCDF: µg m-3 Ragweed pollen grains m-3 Residential elementary carbon GRIB: kg m-3; netCDF: µg m-3 Secondary inorganic aerosol GRIB: kg m-3; netCDF: µg m-3 Sulphur dioxide GRIB: kg m-3; netCDF: µg m-3 Total elementary carbon GRIB: kg m-3; netCDF: µg m-3 MAIN VARIABLES MAIN VARIABLES Name Units Name Units Alder pollen grains m-3 Alder pollen grains m-3 Ammonia GRIB: kg m-3; netCDF: µg m-3 Ammonia GRIB: kg m-3; netCDF: µg m-3 Birch pollen grains m-3 Birch pollen grains m-3 Carbon monoxide GRIB: kg m-3; netCDF: µg m-3 Carbon monoxide GRIB: kg m-3; netCDF: µg m-3 Dust GRIB: kg m-3; netCDF: µg m-3 Dust GRIB: kg m-3; netCDF: µg m-3 Formaldehyde GRIB: kg m-3; netCDF: µg m-3 Formaldehyde GRIB: kg m-3; netCDF: µg m-3 Glyoxal GRIB: kg m-3; netCDF: µg m-3 Glyoxal GRIB: kg m-3; netCDF: µg m-3 Grass pollen grains m-3 Grass pollen grains m-3 Mugwort pollen grains m-3 Mugwort pollen grains m-3 Nitrogen dioxide GRIB: kg m-3; netCDF: µg m-3 Nitrogen dioxide GRIB: kg m-3; netCDF: µg m-3 Nitrogen monoxide GRIB: kg m-3; netCDF: µg m-3 Nitrogen monoxide GRIB: kg m-3; netCDF: µg m-3 Non-methane VOCs GRIB: kg m-3; netCDF: µg m-3 Non-methane VOCs GRIB: kg m-3; netCDF: µg m-3 Olive pollen grains m-3 Olive pollen grains m-3 Ozone GRIB: kg m-3; netCDF: µg m-3 Ozone GRIB: kg m-3; netCDF: µg m-3 PM10, wildfires only GRIB: kg m-3; netCDF: µg m-3 PM10, wildfires only GRIB: kg m-3; netCDF: µg m-3 Particulate matter < 10 µm (PM10) GRIB: kg m-3; netCDF: µg m-3 Particulate matter < 10 µm (PM10) GRIB: kg m-3; netCDF: µg m-3 Particulate matter < 2.5 µm (PM2.5) GRIB: kg m-3; netCDF: µg m-3 Particulate matter < 2.5 µm (PM2.5) GRIB: kg m-3; netCDF: µg m-3 Peroxyacyl nitrates GRIB: kg m-3; netCDF: µg m-3 Peroxyacyl nitrates GRIB: kg m-3; netCDF: µg m-3 Ragweed pollen grains m-3 Ragweed pollen grains m-3 Residential elementary carbon GRIB: kg m-3; netCDF: µg m-3 Residential elementary carbon GRIB: kg m-3; netCDF: µg m-3 Secondary inorganic aerosol GRIB: kg m-3; netCDF: µg m-3 Secondary inorganic aerosol GRIB: kg m-3; netCDF: µg m-3 Sulphur dioxide GRIB: kg m-3; netCDF: µg m-3 Sulphur dioxide GRIB: kg m-3; netCDF: µg m-3 Total elementary carbon GRIB: kg m-3; netCDF: µg m-3 Total elementary carbon GRIB: kg m-3; netCDF: µg m-3 458 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-thermosteric-sea-level-anomaly-0-700m-time http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_700 Global Ocean Thermosteric Sea Level anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing DEFINITION The temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60°S-60°N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). CONTEXT The global mean sea level is reflecting changes in the Earth’s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction. Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019). CMEMS KEY FINDINGS Since the year 2005 the upper (0-700m) near-global (60°S-60°N) thermosteric sea level rises at a rate of 0.9±0.1 mm/year. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00239 https://doi.org/10.48670/moi-00239 459 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/water-and-wetness-2018-raster-100-m-europe-3-yearly https://land.copernicus.eu/pan-european/high-resolution-layers/water-wetness/status-maps/water-wetness-2018 Water and Wetness 2018 (raster 100 m), Europe, 3-yearly - version 2, Nov. 2020 The Copernicus High Resolution Layer Water and Wetness (WAW) 2018 is a thematic product showing the occurrence of water and wet surfaces over the period from 2012 to 2018 for the EEA38 area and the United Kingdom. This metadata refers to the 100 meter aggregate raster, provided as a full EEA38 and United Kingdom mosaic (fully conformant to with the EEA reference grid). The production of the High Resolution Water and Wetness layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme Two Water and Wetness products are available: - The main Water and Wetness (WAW) product, with defined classes of (1) permanent water, (2) temporary water, (3) permanent wetness and (4) temporary wetness. - The additional expert product: Water and Wetness Probability Index (WWPI). The products show the occurrence of water and indicate the degree of wetness in a physical sense, assessed independently of the actual vegetation cover and are thus not limited to a specific land cover class and their relative frequencies. 460 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-glacier-elevation-mass-explorer https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-glacier-elevation-mass-explorer app-glacier-elevation-mass-explorer This application is a data explorer for the CDS datasets: Glaciers distribution data from the Randolph Glacier Inventory for year 2000 and Glaciers elevation and mass change data from 1850 to present from the Fluctuations of Glaciers Database. Glaciers distribution data from the Randolph Glacier Inventory for year 2000 Glaciers elevation and mass change data from 1850 to present from the Fluctuations of Glaciers Database The Glaciers distribution data from the Randolph Glacier Inventory for year 2000 provides the extent and the hypsometry information. Hypsometry describes the distribution of the glacier extent with altitude. The Glaciers elevation and mass change data from 1850 to present from the Fluctuations of Glaciers Database provides the historical records of changes to the the mass and elevation of the glaciers. Glaciers distribution data from the Randolph Glacier Inventory for year 2000 Glaciers elevation and mass change data from 1850 to present from the Fluctuations of Glaciers Database Glaciers, and their fluctuations, are key indicators of the state of the climate and climate change. Glaciers are the largest store of freshwater on the planet and variations to their extent has the potential to impact a number environmental systems, for example they are a key driver to variations in sea level. These detailed datasets provide insight into the response of glaciers to long-term climatic changes and allow analysis of the impact such responses will have on the Earth system. Users can explore the glacier extent dataset globally using the interactive livemap. Clicking on a glacier will produce a summary table and graphs of hypsometry for the glacier, if such data is available for that glacier. Further clicks will append rows to the table and lines to the hypsometry graphs so that the glaciers can be compared. Filtering the glaciers by country enables exploration of the records of glacier elevation change (blue markers) and/or mass (red markers). Clicking on the elevation/mass markers produces a summary table of the observations of elevation/mass for that glacier. User selectable parameters User selectable parameters Country: Global or one of the 45 countries with records of glacier elevation and/or mass. Country: Global or one of the 45 countries with records of glacier elevation and/or mass. INPUT VARIABLES Name Units Description Source Extent km2 The the vector outline of the glaciers and the horizontal extent Glaciers distribution data from the Randolph Glacier Inventory for year 2000 Hypsometry % The distribution of the glacier with height Glaciers distribution data from the Randolph Glacier Inventory for year 2000 Mass change kg m-2 Annual mass balance of glacier divided by the area of the glacier Glaciers elevation and mass change data Elevation change mm year-1 Historical records of changes to the specific ice thickness between reference and survey dates Glaciers elevation and mass change data INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Extent km2 The the vector outline of the glaciers and the horizontal extent Glaciers distribution data from the Randolph Glacier Inventory for year 2000 Extent km2 The the vector outline of the glaciers and the horizontal extent Glaciers distribution data from the Randolph Glacier Inventory for year 2000 Glaciers distribution data from the Randolph Glacier Inventory for year 2000 Hypsometry % The distribution of the glacier with height Glaciers distribution data from the Randolph Glacier Inventory for year 2000 Hypsometry % The distribution of the glacier with height Glaciers distribution data from the Randolph Glacier Inventory for year 2000 Glaciers distribution data from the Randolph Glacier Inventory for year 2000 Mass change kg m-2 Annual mass balance of glacier divided by the area of the glacier Glaciers elevation and mass change data Mass change kg m-2 Annual mass balance of glacier divided by the area of the glacier Glaciers elevation and mass change data Glaciers elevation and mass change data Elevation change mm year-1 Historical records of changes to the specific ice thickness between reference and survey dates Glaciers elevation and mass change data Elevation change mm year-1 Historical records of changes to the specific ice thickness between reference and survey dates Glaciers elevation and mass change data Glaciers elevation and mass change data 461 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-mean-sea-level-time-series-and-trend http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BLKSEA_OMI_SL_area_averaged_anomalies Black Sea Mean Sea Level time series and trend from Observations Reprocessing DEFINITION The ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version) altimeter gridded maps of sea level anomalies based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The mean sea level evolution estimated in the Black Sea is derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed (least square fit of sinusoidal function) and the time series is low-pass filtered (175 days cut-off). The curve is corrected for the regional mean effect of the Glacial Isostatic Adjustment using the ICE5G-VM2 GIA model (Peltier, 2004). During 1993-1998, the Global men sea level (hereafter GMSL) has been known to be affected by a TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018; Legeais et al., 2020). This drift led to overestimate the trend of the GMSL during the first 6 years of the altimetry record (about 0.04 mm/y at global scale over the whole altimeter period). A correction of the drift is proposed for the Global mean sea level (Legeais et al., 2020). Whereas this TOPEX-A instrumental drift should also affect the regional mean sea level (hereafter RMSL) trend estimation, this empirical correction is currently not applied to the altimeter sea level dataset and resulting estimated for RMSL. Indeed, the pertinence of the global correction applied at regional scale has not been demonstrated yet and there is no clear consensus achieved on the way to proceed at regional scale. Additionally, the estimate of such a correction at regional scale is not obvious, especially in areas where few accurate independent measurements (e.g. in situ)- necessary for this estimation - are available. The trend uncertainty is provided in a 90% confidence interval (Prandi et al., 2021). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not taken into account. CONTEXT The indicator on area averaged sea level is a crucial index of climate change, and individual components contribute to sea level rise, including expansion due to ocean warming and melting of glaciers and ice sheets (WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report, global mean sea level (GMSL) increased by 0.20 (0.15 to 0.25) m over the period 1901 to 2018 with a rate 25 of rise that has accelerated since the 1960s to 3.7 (3.2 to 4.2) mm yr-1 for the period 2006–2018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGI, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). At regional scale, sea level does not change homogenously, and RMSL rise can also be influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). Rising sea level can strongly affect population and infrastructures in coastal areas, increase their vulnerability and risks for food security, particularly in low lying areas and island states. Adverse impacts from floods, storms and tropical cyclones with related losses and damages have increased due to sea level rise, and increase their vulnerability and increase risks for food security, particularly in low lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). In the Black Sea, major drivers of change have been attributed to anthropogenic climate change (steric expansion), and mass changes induced by various water exchanges with the Mediterranean Sea, river discharge, and precipitation/evaporation changes (e.g. Volkov and Landerer, 2015). The sea level variation in the basin also shows an important interannual variability, with an increase observed before 1999 predominantly linked to steric effects, and comparable lower values afterward (Vigo et al., 2005). CMEMS KEY FINDINGS Over the [1993/01/01, 2021/08/02] period, the basin-wide RMSL in the Black Sea rises at a rate of 1.7  0.83 mm/year. DOI (product):https://doi.org/10.48670/moi-00215 https://doi.org/10.48670/moi-00215 462 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-thermosteric-sea-level-anomaly-0-2000m-time http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_SL_thsl_area_averaged_anomalies_0_2000 Global Ocean Thermosteric Sea Level anomaly (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing DEFINITION The temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60°S-60°N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). CONTEXT The global mean sea level is reflecting changes in the Earth’s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction. Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019). CMEMS KEY FINDINGS Since the year 2005 the upper (0-2000m) near-global (60°S-60°N) thermosteric sea level rises at a rate of 1.3±0.2 mm/year. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00240 https://doi.org/10.48670/moi-00240 463 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-colour-plankton-my-l4-daily-climatology-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_ARC_BGC_L4_MY_009_124 Arctic Ocean Colour Plankton MY L4 daily climatology and monthly observations Short description: For the Arctic Ocean Satellite Observations, Italian National Research Council (CNR – Rome, Italy) is providing Bio-Geo_Chemical (BGC) products. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the ""multi"" products, and S3A & S3B only for the ""olci"" products. * Variables: Chlorophyll-a (CHL) and Diffuse Attenuation (KD490) and Diffuse Attenuation (KD490). * Temporal resolutions: monthly. * Spatial resolutions: 1 km (multi) or 300 meters (olci). * Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY). DOI (product) :https://doi.org/10.48670/moi-00293 https://doi.org/10.48670/moi-00293 464 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-ocean-colour-plankton-reflectances http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_BAL_BGC_L3_NRT_009_131 Baltic Sea Ocean Colour Plankton, Reflectances, Transparency and Optics L3 NRT daily observations Short description: For the Baltic Sea Ocean Satellite Observations, the Italian National Research Council (CNR – Rome, Italy), is providing Bio-Geo_Chemical (BGC) regional datasets: * ''plankton'' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm * ''reflectance'' with the spectral Remote Sensing Reflectance (RRS) * ''transparency'' with the diffuse attenuation coefficient of light at 490 nm (KD490) * ''optics'' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates) Upstreams: OLCI-S3A & S3B Temporal resolution: daily Spatial resolution: 300 meters To find this product in the catalogue, use the search keyword ""OCEANCOLOUR_BAL_BGC_L3_NRT"". DOI (product) :https://doi.org/10.48670/moi-00294 https://doi.org/10.48670/moi-00294 465 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-ice-volume-observations-reprocessing http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTIC_OMI_SI_volume Baltic Sea Ice Volume from Observations Reprocessing DEFINITION The sea ice volume is a product of sea ice concentration and sea ice thickness integrated over respective area. Sea ice concentration is the fractional coverage of an ocean area covered with sea ice. The Baltic Sea area having more than 15% of sea ice concentration is included into the sea ice volume analysis. Daily sea ice volume values are computed from the daily sea ice concentration and sea ice thickness maps. The data used to produce the charts are Synthetic Aperture Radar images as well as in situ observations from ice breakers (Uiboupin et al., 2010; https://www.smhi.se/data/oceanografi/havsis). The annual course of the sea ice volume has been calculated as daily mean ice volume for each day-of-year over the period October 1992 – September 2014. Weekly smoothed time series of the sea ice volume have been calculated from daily values using a 7-day moving average filter. https://www.smhi.se/data/oceanografi/havsis CONTEXT Sea ice coverage has a vital role in the annual course of physical and ecological conditions in the Baltic Sea. Knowledge of the sea ice volume facilitates planning of icebreaking activity and operation of the icebreakers (Valdez Banda et al., 2015; Boström and Österman, 2017). A long-term monitoring of ice parameters is required for design and installation of offshore constructions in seasonally ice covered seas (Heinonen and Rissanen, 2017). A reduction of the sea ice volume in the Baltic Sea has a critical impact on the population of ringed seals (Harkonen et al., 2008). Ringed seals need stable ice conditions for about two months for breeding and moulting (Sundqvist et al., 2012). The sea ice is a habitat for diverse biological assemblages (Enberg et al., 2018). CMEMS KEY FINDINGS In the Baltic Sea, ice season may start in October and may last until June. Maximum sea ice volume is observed in March on average. The ice season 2020/21 had low maximum sea ice volume in the Baltic Sea reaching about 18 km3. During the preceding period 1993-2019, the yearly maximum ice volume varied from 4 km3 in 2020 to 60 km3 in 1996. There is a statistically significant decreasing trend of -0.75 km3/year (p=0.02) in the maximum sea ice volume of the Baltic Sea. DOI (product):https://doi.org/10.48670/moi-00201 https://doi.org/10.48670/moi-00201 466 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-high-resolution-l3s-sea-surface-temperature http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_BS_PHY_L3S_MY_010_041 Black Sea - High Resolution L3S Sea Surface Temperature Reprocessed Short description: The CMEMS Reprocessed (REP) Black Sea (BS) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Black Sea developed for climate applications. This product consists of daily (nighttime), merged multi-sensor (L3S), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05° resolution grid covering the period from January 1st 1982 to present (currently, up to six months before real time). The BS-REP-L3S product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record provided by the ESA Climate Change Initiative (CCI) and the Copernicus Climate Change Service (C3S) initiatives, but also includes in input an adjusted version of the AVHRR Pathfinder dataset version 5.3 to increase the input observation coverage. DOI (product) :https://doi.org/10.48670/moi-00313 https://doi.org/10.48670/moi-00313 467 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/insitu-observations-surface-marine https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-observations-surface-marine insitu-observations-surface-marine This set of holdings provides access to surface marine meteorological weather reports and observations made by merchant and naval ships, drifting buoys and other platforms and vessels over the global ocean. Data have been collated and harmonised and quality control checks have been performed, but no attempt has been made to assess or correct for potential biases. Data are provided for a range of commonly observed variables. The weather reports contain instantaneous, or point, observations of a number of key Essential Climate Variables (ECVs) and other parameters as denoted in the main variables table, made at routine intervals (typically 1, 3, or 6 hourly). Note that not all reports will contain information for all variables. Attributes are described in the related-variables table and users can choose whether to receive all attributes or solely essential attributes. Data are downloaded as comma-seperated values (CSV) files organised as one observation of one variable per row. The data available is released in the CDS as version 1 and consist in a reprocessing of the International Comprehensive Ocean-Atmosphere Data Set (ICOADS), Release 3.0 with major improvements made to the quality control and duplicate flagging of observations and improved availability of metadata. The current release represents a preliminary reprocessing of the period 1851-2010, with additional data and flags planned to be added in subsequent releases. Users should ensure an appropriate assessment of long-term data quality prior to use in those applications which require consideration of such aspects. Users should be aware that data availability is spatio-temporally incomplete and varies significantly throughout the period of record. More details about the marine data holdings are available in the product user guide, and details around data formatting can be found in the common data model documentation, both of which can be found in the documentation section. This work is being completed on behalf of Copernicus Climate Change Services in sustained collaboration with colleagues at NOAA's National Centres for Environmental Information who are the WMO designated World Data Centre for meteorology. DATA DESCRIPTION Data type Point observation Horizontal coverage Global Horizontal resolution Irregular Temporal coverage From 1851 to 2010 Temporal resolution Variable File format csv Versions Dataset version 1 Update frequency Not defined DATA DESCRIPTION DATA DESCRIPTION Data type Point observation Data type Point observation Horizontal coverage Global Horizontal coverage Global Horizontal resolution Irregular Horizontal resolution Irregular Temporal coverage From 1851 to 2010 Temporal coverage From 1851 to 2010 Temporal resolution Variable Temporal resolution Variable File format csv File format csv Versions Dataset version 1 Versions Dataset version 1 Update frequency Not defined Update frequency Not defined MAIN VARIABLES Name Units Description Air pressure at sea level Pa Sea level means mean sea level, which is close to the geoid in sea areas. Air pressure at sea level is the quantity often abbreviated as MSLP or PMSL. Air temperature K Air temperature is the bulk temperature of the air, not the surface (skin) temperature. Dew point temperature K Dew point temperature is the temperature at which a parcel of air reaches saturation upon being cooled at constant pressure and specific humidity. Water temperature K Water (sea, river, lake) temperature at depth indicated. Wind from direction degree Direction from which the wind is blowing. dd - WMO abbrev. Wind speed m s-1 Speed is the magnitude of velocity. Wind is defined as a two-dimensional (horizontal) air velocity vector, with no vertical component. (Vertical motion in the atmosphere has the standard name upward air velocity.) The wind speed is the magnitude of the wind velocity. ff - WMO abbrev. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Air pressure at sea level Pa Sea level means mean sea level, which is close to the geoid in sea areas. Air pressure at sea level is the quantity often abbreviated as MSLP or PMSL. Air pressure at sea level Pa Sea level means mean sea level, which is close to the geoid in sea areas. Air pressure at sea level is the quantity often abbreviated as MSLP or PMSL. Air temperature K Air temperature is the bulk temperature of the air, not the surface (skin) temperature. Air temperature K Air temperature is the bulk temperature of the air, not the surface (skin) temperature. Dew point temperature K Dew point temperature is the temperature at which a parcel of air reaches saturation upon being cooled at constant pressure and specific humidity. Dew point temperature K Dew point temperature is the temperature at which a parcel of air reaches saturation upon being cooled at constant pressure and specific humidity. Water temperature K Water (sea, river, lake) temperature at depth indicated. Water temperature K Water (sea, river, lake) temperature at depth indicated. Wind from direction degree Direction from which the wind is blowing. dd - WMO abbrev. Wind from direction degree Direction from which the wind is blowing. dd - WMO abbrev. Wind speed m s-1 Speed is the magnitude of velocity. Wind is defined as a two-dimensional (horizontal) air velocity vector, with no vertical component. (Vertical motion in the atmosphere has the standard name upward air velocity.) The wind speed is the magnitude of the wind velocity. ff - WMO abbrev. Wind speed m s-1 Speed is the magnitude of velocity. Wind is defined as a two-dimensional (horizontal) air velocity vector, with no vertical component. (Vertical motion in the atmosphere has the standard name upward air velocity.) The wind speed is the magnitude of the wind velocity. ff - WMO abbrev. RELATED VARIABLES Name Units Description Data policy licence Not applicable Data policy licence (presently either open or WMO Resolution 40) (optional metadata) Date time Not applicable Timestamp for observation (basic metadata) specified as YYYY-MM-DD HH:MM:00+00 Date time meaning Not applicable Whether the date and time of observation given denotes start, middle or end of period of observation (optional metadata) Height above surface m Altitude of the station as reported (if known) (basic metadata) Latitude Degrees north Observation latitude, bounded between -90 and 90 (basic metadata) Longitude Degrees east Observation longitude bounded between -180 and 180 (basic metadata) Observation duration Seconds The period over which the observation was taken (basic metadata) Observation id Not applicable Unique identifier associated with each individual observation consisting of the station identifier, station configuration and date time stamp (basic metadata) Observation value As specified in the units column Value of measurement Observed variable Not applicable The variable being observed / measured (basic metadata) Platform type Not applicable The generic type of observing system (optional metadata) Primary station id Not applicable Identifier used within C3S and by NOAA NCEI to uniquely identify the station (hopefully to be replaced with WIGOS Station identifiers in collaboration with the World Meteorological Organization in the medium term) (basic metadata) Quality flag Not applicable Integer flag system to reflect quality assessment of the observation. See Land User Guide or Common Data Model documentation for further particulars (basic metadata) Report type Not applicable The type of report e.g. synoptic, METAR etc. (optional metadata) Station name Not applicable Station primary name assigned (station may also have existed under other names) (basic metadata) Station type Not applicable Type of observing station (if known) (optional metadata) Units Not applicable unit of the measurement Value significance Not applicable Whether minimum, maximum, mean or sum (basic metadata) RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Data policy licence Not applicable Data policy licence (presently either open or WMO Resolution 40) (optional metadata) Data policy licence Not applicable Data policy licence (presently either open or WMO Resolution 40) (optional metadata) Date time Not applicable Timestamp for observation (basic metadata) specified as YYYY-MM-DD HH:MM:00+00 Date time Not applicable Timestamp for observation (basic metadata) specified as YYYY-MM-DD HH:MM:00+00 Date time meaning Not applicable Whether the date and time of observation given denotes start, middle or end of period of observation (optional metadata) Date time meaning Not applicable Whether the date and time of observation given denotes start, middle or end of period of observation (optional metadata) Height above surface m Altitude of the station as reported (if known) (basic metadata) Height above surface m Altitude of the station as reported (if known) (basic metadata) Latitude Degrees north Observation latitude, bounded between -90 and 90 (basic metadata) Latitude Degrees north Observation latitude, bounded between -90 and 90 (basic metadata) Longitude Degrees east Observation longitude bounded between -180 and 180 (basic metadata) Longitude Degrees east Observation longitude bounded between -180 and 180 (basic metadata) Observation duration Seconds The period over which the observation was taken (basic metadata) Observation duration Seconds The period over which the observation was taken (basic metadata) Observation id Not applicable Unique identifier associated with each individual observation consisting of the station identifier, station configuration and date time stamp (basic metadata) Observation id Not applicable Unique identifier associated with each individual observation consisting of the station identifier, station configuration and date time stamp (basic metadata) Observation value As specified in the units column Value of measurement Observation value As specified in the units column Value of measurement Observed variable Not applicable The variable being observed / measured (basic metadata) Observed variable Not applicable The variable being observed / measured (basic metadata) Platform type Not applicable The generic type of observing system (optional metadata) Platform type Not applicable The generic type of observing system (optional metadata) Primary station id Not applicable Identifier used within C3S and by NOAA NCEI to uniquely identify the station (hopefully to be replaced with WIGOS Station identifiers in collaboration with the World Meteorological Organization in the medium term) (basic metadata) Primary station id Not applicable Identifier used within C3S and by NOAA NCEI to uniquely identify the station (hopefully to be replaced with WIGOS Station identifiers in collaboration with the World Meteorological Organization in the medium term) (basic metadata) Quality flag Not applicable Integer flag system to reflect quality assessment of the observation. See Land User Guide or Common Data Model documentation for further particulars (basic metadata) Quality flag Not applicable Integer flag system to reflect quality assessment of the observation. See Land User Guide or Common Data Model documentation for further particulars (basic metadata) Report type Not applicable The type of report e.g. synoptic, METAR etc. (optional metadata) Report type Not applicable The type of report e.g. synoptic, METAR etc. (optional metadata) Station name Not applicable Station primary name assigned (station may also have existed under other names) (basic metadata) Station name Not applicable Station primary name assigned (station may also have existed under other names) (basic metadata) Station type Not applicable Type of observing station (if known) (optional metadata) Station type Not applicable Type of observing station (if known) (optional metadata) Units Not applicable unit of the measurement Units Not applicable unit of the measurement Value significance Not applicable Whether minimum, maximum, mean or sum (basic metadata) Value significance Not applicable Whether minimum, maximum, mean or sum (basic metadata) 468 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-arctic-and-antarctic-sea-ice-concentration http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001 Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF) Short description: For the Global - Arctic and Antarctic - Ocean. The OSI SAF delivers three global sea ice products in operational mode: sea ice concentration, sea ice edge, sea ice type (OSI-401 OSI-402 and OSI-403). These products are delivered daily at 10km resolution in a polar stereographic projection covering the Northern Hemisphere and the Southern Hemisphere. It is the Sea Ice operational nominal product for the Global Ocean. In addition, a sea ice drift product is delivered at 60km resolution in a polar stereographic projection covering the Northern and Southern Hemispheres. The sea ice motion vectors have a time-span of 2 days. DOI (product) :https://doi.org/10.48670/moi-00134 https://doi.org/10.48670/moi-00134 469 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/high-resolution-vegetation-phenology-and-productivity-2 https://www.wekeo.eu/data?view=viewer&t=1562219742857&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMSIsImxheWVySWQiOiJFTzpFRUE6REFUOkNMTVNfSFJWUFBfU1QvX19ERUZBVUxUX18vQ0xNU19IUlZQUF9TVF9RRkxBR18xME0iLCJ6SW5kZXgiOjEwfV0%3D High Resolution Vegetation Phenology and Productivity: Seasonal Trajectories Quality Flag (raster 10m) version 1 revision 1, Sep. 2021 The Quality Flag of the PPI Seasonal Trajectories is one of the products of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Plant Phenology Index (PPI) is a physically based vegetation index for improved monitoring of plant phenology, that is developed from a simplified solution to the radiative transfer equation by Jin and Eklundh (2014) and that has a linear relationship with green leaf area index. The PPI Seasonal Trajectories (ST) product is derived from a TIMESAT-based function fitting of the time series of the PPI vegetation index and thus provides a filtered time series of Plant Phenology Index (PPI), with regular 10-day time step. The Quality Flag indicates the quality of the PPI seasonal trajectory computation, in the form of a confidence level. The QFLAG dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for the period from 2017 until today. It is updated in the first quarter of each year. 470 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/n2k-change-2012-2018-vector-europe-6-yearly-jul-2021 https://land.copernicus.eu/local/natura/n2k-change-2012-2018?tab=download N2K Change 2012-2018 (vector), Europe, 6-yearly, Jul. 2021 This metadata refers to the CLMS N2K Change 2012-2018 product, the Copernicus Land Cover/Land Use (LC/LU) change map tailored to the needs of biodiversity monitoring in selected Natura2000 sites: 4790 sites of natural and semi-natural grassland formations listed in Annex I of the Habitats Directive, including a 2km buffer zone surrounding the sites. The change mapping exercise between the reference years 2012 and 2018, was based on the comparison of VHR satellite data and occurred over an area of 631.800 km² across Europe (i.e. EU27, the United Kingdom and Switzerland). The change dataset only shows the areas that have changed between 2012 and 2018 and covers an area of 1.199.652 ha. LC/LU is extracted from VHR satellite data and other available data. Change detection is based on the analysis of VHR satellite data from the reference years 2012 ±2 years and 2018 ±1 year. The production of N2K updates was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 471 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-precipitation-microwave https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-precipitation-microwave satellite-precipitation-microwave This dataset provides global estimates of daily accumulated and monthly means of precipitation. The precipitation estimates are based on a merge of passive microwave observations from two different radiometer classes operating on multiple Low Earth Orbit (LEO) satellites. Spaceborne passive microwave (MW) provides the most effective measurements for the remote sensing of precipitation because the MW upwelling radiation is directly responsive to the cloud microphysical structure and, in particular, to the emission and scattering properties of precipitation-size hydrometeors (solid and liquid). However, they are available at low spatial and temporal resolution, due to the limited number of passes per day (depending on latitude and number of platforms) at each location. On the other hand, infrared (IR) sensors, available also on geostationary platforms, provide measurements that mostly respond to upper-level cloud structure, but at much higher temporal and spatial resolution. Since precipitation is not directly sensed in the infrared, these observations are often merged with microwave-based precipitation estimates and rain gauges. A precipitation product merging IR and MW is also available on the Climate Data Store: GPCP precipitation dataset. GPCP precipitation dataset The two different radiometer classes used in the present Copernicus micrOwave-based gloBal pRecipitAtion (COBRA) dataset are: i) Conically scanning MW imagers; observations obtained by applying methodologies of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) in the Satellite Application Facility on Climate Monitoring (CM SAF). ii) Cross-track scanning MW sounders; observations obtained through the dedicated Passive microwave Neural network Precipitation Retrieval for Climate Applications (PNPR-CLIM) algorithm. This datset is independent of IR imagery and rain-gauge observations. A pure passive MW-based precipitation dataset overcomes the challenges and limitations of precipitation estimates based on IR observations, and the issues related to the inadequacy of the rain gauge networks in some regions and their almost complete absence over the ocean. The main limitations, however, are linked to the varying (in time and space) revisiting time of the LEO satellites and low temporal sampling compared to geostanionary IR observations. This dataset is produced by the Copernicus Climate Change Service (C3S). DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 1.0°x1.0° Vertical coverage Surface Vertical resolution Single level Temporal coverage January 2000 to December 2017 Temporal resolution Monthly and daily Temporal gaps No gaps File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Update frequency No expected updates DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 1.0°x1.0° Horizontal resolution 1.0°x1.0° Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage January 2000 to December 2017 Temporal coverage January 2000 to December 2017 Temporal resolution Monthly and daily Temporal resolution Monthly and daily Temporal gaps No gaps Temporal gaps No gaps File format NetCDF4 File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Versions 1.0 Update frequency No expected updates Update frequency No expected updates MAIN VARIABLES Name Units Description Precipitation mm day-1 This variable represents the water-equivalent volume rate per area and per day of atmospheric water in liquid or solid phase reaching the Earth's surface MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Precipitation mm day-1 This variable represents the water-equivalent volume rate per area and per day of atmospheric water in liquid or solid phase reaching the Earth's surface Precipitation mm day-1 This variable represents the water-equivalent volume rate per area and per day of atmospheric water in liquid or solid phase reaching the Earth's surface RELATED VARIABLES The daily and monthly precipitation rates of the COBRA dataset are accompanied by a set of respective ancillary data: standard deviation of precipitation rates, data quality flag based on PNPR-CLIM output, platforms and sensors, on which the data is based, number of observations per grid cell, number of hours within a day/month covered by measurements. RELATED VARIABLES RELATED VARIABLES The daily and monthly precipitation rates of the COBRA dataset are accompanied by a set of respective ancillary data: standard deviation of precipitation rates, data quality flag based on PNPR-CLIM output, platforms and sensors, on which the data is based, number of observations per grid cell, number of hours within a day/month covered by measurements. The daily and monthly precipitation rates of the COBRA dataset are accompanied by a set of respective ancillary data: standard deviation of precipitation rates, data quality flag based on PNPR-CLIM output, platforms and sensors, on which the data is based, number of observations per grid cell, number of hours within a day/month covered by measurements. 472 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-seabaltic-sea-sea-surface-temperature-analysis-l3s http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_BAL_SST_L3S_NRT_OBSERVATIONS_010_032 North Sea/Baltic Sea - Sea Surface Temperature Analysis L3S Short description: For the Baltic Sea- The DMI Sea Surface Temperature L3S aims at providing daily multi-sensor supercollated data at 0.03deg. x 0.03deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR. DOI (product) :https://doi.org/10.48670/moi-00154 https://doi.org/10.48670/moi-00154 473 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-sea-surface-temperature-ensemble-product https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-surface-temperature-ensemble-product satellite-sea-surface-temperature-ensemble-product This dataset provides global daily sea surface temperature (SST) data from the Group for High Resolution Sea Surface Temperature (GHRSST) multi-product ensemble (GMPE) produced by the European Space Agency SST Climate Change Initiative (ESA SST CCI). The GMPE system was designed to allow users to compare the outputs from different SST analysis systems and understand their similarities and differences. Although originally intended for comparison of near real time data, it has also been used to compare long historical datasets. Note that the dataset provided here is the climate version of the GMPE dataset. An operational version, with different input products and time coverage, also exists but is not distributed by the CDS. sea surface temperature climate The SST analyses ingested into the GMPE system come from the following seven SST products and providers: ESA SST CCI Analysis version 2.0 ESA SST CCI Analysis version 1.1 Copernicus Marine Environment Monitoring Service (CMEMS) Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Reprocessing National Centers for Environmental Information (NCEI) Advanced Very High Resolution Radiometer (AVHRR) Optimal Interpolation (OI) Global Blended SST Analysis Canada Meteorological Center (CMC) 0.2-degree Global Foundation SST Analysis Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) Analysis version 2.2.0.0 Japan Meteorological Agency (JMA) Merged satellite and in-situ Data Global Daily SST (MGDSST) Analysis ESA SST CCI Analysis version 2.0 ESA SST CCI Analysis version 1.1 Copernicus Marine Environment Monitoring Service (CMEMS) Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Reprocessing National Centers for Environmental Information (NCEI) Advanced Very High Resolution Radiometer (AVHRR) Optimal Interpolation (OI) Global Blended SST Analysis Canada Meteorological Center (CMC) 0.2-degree Global Foundation SST Analysis Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) Analysis version 2.2.0.0 Japan Meteorological Agency (JMA) Merged satellite and in-situ Data Global Daily SST (MGDSST) Analysis These products are all spatially complete (through use of infilling or reconstruction techniques) but were originally produced for different purposes and with different user requirements in mind. Therefore, each producer has made different choices on aspects of data production such as which input observations to use and what type of SST to represent. For example, the CMEMS OSTIA, CMC, and MGDSST analyses attempt to represent the foundation SST (water temperature free of diurnal temperature variability) while the ESA SST CCI and HadISST analyses estimate the SST at a standard depth of 20 cm. The AVHRR OI product, on the other hand, is bias-corrected to in situ observations and hence will be representative of their depths. The GMPE dataset provides the median and standard deviation of the input SST products, the differences between each input product and the median, and the horizontal gradients in each of the input SST products as well as the final ensemble product. The HadISST product consists of 10 different realisations, therefore the median and standard deviation are calculated for an ensemble of 16 input fields. All fields are provided on a common 0.25 degree regular latitude-longitude grid and extend from 1 September 1981 to 31 December 2016, although some of the individual input products cover shorter periods. The dataset will not be extended beyond 2016. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 0.25°x0.25° Temporal coverage From September 1981 to December 2016 Temporal resolution Daily File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.4, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.25°x0.25° Horizontal resolution 0.25°x0.25° Temporal coverage From September 1981 to December 2016 Temporal coverage From September 1981 to December 2016 Temporal resolution Daily Temporal resolution Daily File format NetCDF4 File format NetCDF4 Conventions Climate and Forecast (CF) Metadata Convention v1.4, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.4, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Analysed SST K Median of the 16 input SST analyses (one field). Gradient fields K m-1 Horizontal SST gradient for the 16 individual input analyses and the analysed product (17 fields). SST anomaly K Difference between each input SST analysis and the median (16 fields). SST standard deviation K Standard deviation of the SST across the 16 input analyses (one field). MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Analysed SST K Median of the 16 input SST analyses (one field). Analysed SST K Median of the 16 input SST analyses (one field). Gradient fields K m-1 Horizontal SST gradient for the 16 individual input analyses and the analysed product (17 fields). Gradient fields K m-1 Horizontal SST gradient for the 16 individual input analyses and the analysed product (17 fields). SST anomaly K Difference between each input SST analysis and the median (16 fields). SST anomaly K Difference between each input SST analysis and the median (16 fields). SST standard deviation K Standard deviation of the SST across the 16 input analyses (one field). SST standard deviation K Standard deviation of the SST across the 16 input analyses (one field). 474 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/soil-water-index-2015-present-raster-1-km-europe-daily https://land.copernicus.eu/global/products/swi Soil Water Index 2015-present (raster 1 km), Europe, daily - version 1 The Soil Water Index (SWI) quantifies the moisture condition at various depths in the soil. It is mainly driven by the precipitation via the process of infiltration. Soil moisture is a very heterogeneous variable and varies on small scales with soil properties and drainage patterns. Satellite measurements integrate over relative large-scale areas, with the presence of vegetation adding complexity to the interpretation. Soil moisture is a key parameter in numerous environmental studies including hydrology, meteorology and agriculture, and is recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). The SWI product provides daily information about moisture conditions in different soil layers. It includes a quality flag (QFLAG) indicating the availability of SSM measurements for SWI calculations, and a Surface State Flag (SSF) indicating frozen or snow covered soils. 475 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-physics-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BLKSEA_ANALYSISFORECAST_PHY_007_001 Black Sea Physics Analysis and Forecast Short description: The BLKSEA_ANALYSISFORECAST_PHY_007_001 is produced with a hydrodynamic model implemented over the whole Black Sea basin, including the Bosporus Strait and a portion of the Marmara Sea for the optimal interface with the Mediterranean Sea through lateral open boundary conditions. The model horizontal grid resolution is 1/40° in zonal and 1/40° in meridional direction (ca. 121 km) and has 121 unevenly spaced vertical levels. The product provides analysis and forecast for 3D potential temperature, salinity, horizontal and vertical currents. Together with the 2D variables sea surface height, bottom potential temperature and mixed layer thickness. Product Citation: Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (Product):https://doi.org/10.25423/cmcc/blksea_analysisforecast_phy_007_001_eas5 https://doi.org/10.25423/cmcc/blksea_analysisforecast_phy_007_001_eas5 476 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/high-resolution-vegetation-phenology-and-productivity-vpp https://www.wekeo.eu/data?view=viewer&t=1577751120000&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpFRUE6REFUOkNMTVNfSFJWUFBfVlBQL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVlBQX1FGTEFHX1NFQVNPTjFfMTBNIiwiekluZGV4IjoxMH1d&initial=1 High Resolution Vegetation Phenology and Productivity: VPP quality flag (raster 10m) version 1 revision 1, Sept. 2021 The Quality Flag (QFLAG), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Seasonal Productivity is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-m… https://land.copernicus.eu/user-corner/technical-library/product-user-m… The Quality Flag (QFLAG) is a quality indicator for the above set of 13 Vegetation Phenology and Productivity (VPP) parameters and provides a confidence level, that is described in table 4 of the same manual. The QFLAG dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year. 477 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-colour-copernicus-globcolour-bio-geo http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_GLO_BGC_L3_NRT_009_101 Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time) Short description: For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the ""multi"" products, and S3A & S3B only for the ""olci"" products. * Variables: Chlorophyll-a (CHL), Primary Production (PP), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Particulate Backscattering (BBP), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Absorption Coef. (ADG440/CDM440) and Reflectance (RRS). * Temporal resolutions: daily, monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a ""cloud free"" product. * Spatial resolutions: 4 km (global), 1 km (Atlantic ; 46°W-13°E , 20°N-66°N), and a finer resolution based on olci 300 meters inputs (Atlantic and global coastal). * Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY). To find the Copernicus-GlobColour products in the catalogue, use the search keyword ""GlobColour"". DOI (product) :https://doi.org/10.48670/moi-00278 https://doi.org/10.48670/moi-00278 478 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-iberian-biscay-irish-ocean-physics-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=IBI_MULTIYEAR_PHY_005_002 Atlantic-Iberian Biscay Irish- Ocean Physics Reanalysis Short description: The IBI-MFC provides a ocean physical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user’s format by Nologin with the support of CESGA in terms of supercomputing resources. The IBI model numerical core is based on the NEMO v3.6 ocean general circulation model run at 1/12° horizontal resolution. Altimeter data, in situ temperature and salinity vertical profiles and satellite sea surface temperature are assimilated. The product offers 3D daily, monthly and yearly ocean fields, as well as hourly mean fields for surface variables. Daily, monthly and yearly averages of 3D Temperature, 3D Salinity, 3D Zonal and Meridional Velocity components, Mix Layer Depth, Sea Bottom Temperature and Sea Surface Height are provided. Additionally, hourly means of surface fields for variables such as Sea Surface Height, Mix Layer Depth, Surface Temperature and Currents, together with Barotropic Velocities are distributed. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered. Product Citation: Please refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag…] http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (Product):https://doi.org/10.48670/moi-00028 https://doi.org/10.48670/moi-00028 479 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-colour-copernicus-globcolour-bio-geo-1 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_GLO_BGC_L4_NRT_009_102 Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time) Short description: For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the ""multi"" products, and S3A & S3B only for the ""olci"" products. * Variables: Chlorophyll-a (CHL), Primary Production (PP), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Particulate Backscattering (BBP), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Absorption Coef. (ADG440/CDM440) and Reflectance (RRS). * Temporal resolutions: daily, monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a ""cloud free"" product. * Spatial resolutions: 4 km (global), 1 km (Atlantic ; 46°W-13°E , 20°N-66°N), and a finer resolution based on olci 300 meters inputs (Atlantic and global coastal). * Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY). To find the Copernicus-GlobColour products in the catalogue, use the search keyword ""GlobColour"". DOI (product) :https://doi.org/10.48670/moi-00279 https://doi.org/10.48670/moi-00279 480 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/fraction-vegetation-cover-2014-present-raster-300-m http://land.copernicus.eu/global/products/fcover Fraction of Vegetation Cover 2014-present (raster 300 m), global, 10-daily - version 1 The Fraction of Vegetation Cover (FCover) corresponds to the fraction of ground covered by green vegetation. Practically, it quantifies the spatial extent of the vegetation. Because it is independent from the illumination direction and it is sensitive to the vegetation amount, FCover is a very good candidate for the replacement of classical vegetation indices for the monitoring of ecosystems. The product at 333m resolution is provided in Near Real Time and consolidated in the next six periods. 481 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-l3-significant-wave-height-nrt-satellite http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=WAVE_GLO_PHY_SWH_L3_NRT_014_001 GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS Short description: Near-Real-Time mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle…). All the missions are homogenized with respect to a reference mission (Jason-3 until April 2022, Sentinel-6A afterwards) and calibrated on in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise. As a support of information to the significant wave height, wind speed measured by the altimeters is also processed and included in the files. Wind speed values are provided by upstream products (L2) for each mission and are based on different algorithms. Only valid data are included and all the missions are homogenized with respect to the reference mission. This product is processed by the WAVE-TAC multi-mission altimeter data processing system. It serves in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes operational data (OGDR and NRT, produced in near-real-time) from the following altimeter missions: Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Cryosat-2, SARAL/AltiKa, CFOSAT ; and interim data (IGDR, 1 to 2 days delay) from Hai Yang-2B mission. One file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH), the unfiltered SWH (VAVH_UNFILTERED) and the wind speed (wind_speed). DOI (product) :https://doi.org/10.48670/moi-00179 https://doi.org/10.48670/moi-00179 482 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-ice-concentration-extent-and-classification http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_BAL_PHY_L4_MY_011_019 Baltic Sea ice concentration, extent, and classification time series Gridded sea ice concentration, sea ice extent and classification based on the digitized Baltic ice charts produced by the FMI/SMHI ice analysts. It is produced daily in the afternoon, describing the ice situation daily at 14:00 EET. The nominal resolution is about 1km. The temporal coverage is from the beginning of the season 1980-1981 until today. DOI (product) :https://doi.org/10.48670/moi-00131 https://doi.org/10.48670/moi-00131 483 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/leaf-area-index-2014-present-raster-300-m-global-10-daily http://land.copernicus.eu/global/products/lai Leaf Area Index 2014-present (raster 300 m), global, 10-daily - version 1 LAI was defined by CEOS as half the developed area of the convex hull wrapping the green canopy elements per unit horizontal ground. This definition allows accounting for elements which are not flat such as needles or stems. LAI is strongly non linearly related to reflectance. Therefore, its estimation from remote sensing observations will be scale dependant over heterogeneous landscapes. When observing a canopy made of different layers of vegetation, it is therefore mandatory to consider all the green layers. This is particularly important for forest canopies where the understory may represent a very significant contribution to the total canopy LAI. The derived LAI corresponds therefore to the total green LAI, including the contribution of the green elements of the understory. The product at 333m resolution is provided in Near Real Time and consolidated in the next six periods. 484 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-colour-copernicus-globcolour-bio-geo-2 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_GLO_BGC_L3_MY_009_103 Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997-ongoing) Short description: For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the ""multi"" products, and S3A & S3B only for the ""olci"" products. * Variables: Chlorophyll-a (CHL), Primary Production (PP), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Particulate Backscattering (BBP), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Absorption Coef. (ADG440/CDM440) and Reflectance (RRS). * Temporal resolutions: daily, monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a ""cloud free"" product. * Spatial resolutions: 4 km (global), 1 km (Atlantic ; 46°W-13°E , 20°N-66°N), and a finer resolution based on olci 300 meters inputs (Atlantic and global coastal). * Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY). To find the Copernicus-GlobColour products in the catalogue, use the search keyword ""GlobColour"". DOI (product) :https://doi.org/10.48670/moi-00280 https://doi.org/10.48670/moi-00280 485 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-ocean-colour-copernicus-globcolour-bio-geo http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_ATL_BGC_L4_NRT_009_116 Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (Near Real Time) Short description: For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the ""multi"" products, and S3A & S3B only for the ""olci"" products. * Variables: Chlorophyll-a (CHL), Primary Production (PP), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Particulate Backscattering (BBP), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Absorption Coef. (ADG440/CDM440) and Reflectance (RRS). * Temporal resolutions: daily, monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a ""cloud free"" product. * Spatial resolutions: 4 km (global), 1 km (Atlantic ; 46°W-13°E , 20°N-66°N), and a finer resolution based on olci 300 meters inputs (Atlantic and global coastal). * Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY). To find the Copernicus-GlobColour products in the catalogue, use the search keyword ""GlobColour"". DOI (product) :https://doi.org/10.48670/moi-00288 https://doi.org/10.48670/moi-00288 486 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-atlantic-ocean-colour-plankton-reflectance-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_ATL_BGC_L3_NRT_009_111 North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations Short description: For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the ""multi"" products, and S3A & S3B only for the ""olci"" products. * Variables: Chlorophyll-a (CHL), Primary Production (PP), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Particulate Backscattering (BBP), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Absorption Coef. (ADG440/CDM440) and Reflectance (RRS). * Temporal resolutions: daily, monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a ""cloud free"" product. * Spatial resolutions: 4 km (global), 1 km (Atlantic ; 46°W-13°E , 20°N-66°N), and a finer resolution based on olci 300 meters inputs (Atlantic and global coastal). * Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY). To find the Copernicus-GlobColour products in the catalogue, use the search keyword ""GlobColour"". DOI (product) :https://doi.org/10.48670/moi-00284 https://doi.org/10.48670/moi-00284 487 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-atlantic-ocean-colour-plankton-reflectance http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_ATL_BGC_L3_MY_009_113 North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations Short description: For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the ""multi"" products, and S3A & S3B only for the ""olci"" products. * Variables: Chlorophyll-a (CHL), Primary Production (PP), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Particulate Backscattering (BBP), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Absorption Coef. (ADG440/CDM440) and Reflectance (RRS). * Temporal resolutions: daily, monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a ""cloud free"" product. * Spatial resolutions: 4 km (global), 1 km (Atlantic ; 46°W-13°E , 20°N-66°N), and a finer resolution based on olci 300 meters inputs (Atlantic and global coastal). * Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY). To find the Copernicus-GlobColour products in the catalogue, use the search keyword ""GlobColour"". DOI (product) :https://doi.org/10.48670/moi-00286 https://doi.org/10.48670/moi-00286 488 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-surface-carbon http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008 Global Ocean Surface Carbon Short description: This product corresponds to a REP L4 time series of monthly global reconstructed surface ocean pCO2, air-sea fluxes of CO2, pH, total alkalinity, dissolved inorganic carbon, saturation state with respect to calcite and aragonite, and associated uncertainties on a 1° x 1° regular grid. The product is obtained from an ensemble-based forward feed neural network approach mapping situ data for surface ocean fugacity (SOCAT data base, Bakker et al. 2016, https://www.socat.info/) and sea surface salinity, temperature, sea surface height, chlorophyll a, mixed layer depth and atmospheric CO2 mole fraction. Sea-air flux fields are computed from the air-sea gradient of pCO2 and the dependence on wind speed of Wanninkhof (2014). Surface ocean pH on total scale, dissolved inorganic carbon, and saturation states are then computed from surface ocean pCO2 and reconstructed surface ocean alkalinity using the CO2sys speciation software. https://www.socat.info/ *Product Citation: Please refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag…. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (product) :https://doi.org/10.48670/moi-00047 https://doi.org/10.48670/moi-00047 489 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-colour-copernicus-globcolour-bio-geo-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_GLO_BGC_L4_MY_009_104 Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing) Short description: For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the ""multi"" products, and S3A & S3B only for the ""olci"" products. * Variables: Chlorophyll-a (CHL), Primary Production (PP), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Particulate Backscattering (BBP), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Absorption Coef. (ADG440/CDM440) and Reflectance (RRS). * Temporal resolutions: daily, monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a ""cloud free"" product. * Spatial resolutions: 4 km (global), 1 km (Atlantic ; 46°W-13°E , 20°N-66°N), and a finer resolution based on olci 300 meters inputs (Atlantic and global coastal). * Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY). To find the Copernicus-GlobColour products in the catalogue, use the search keyword ""GlobColour"". DOI (product) :https://doi.org/10.48670/moi-00281 https://doi.org/10.48670/moi-00281 490 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-ocean-colour-copernicus-globcolour-bio-geo-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_ATL_BGC_L4_MY_009_118 Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing) Short description: For the Global Ocean Satellite Observations, ACRI-ST company (Sophia Antipolis, France) is providing Bio-Geo-Chemical (BGC) products based on the Copernicus-GlobColour processor. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the ""multi"" products, and S3A & S3B only for the ""olci"" products. * Variables: Chlorophyll-a (CHL), Primary Production (PP), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Particulate Backscattering (BBP), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490), Absorption Coef. (ADG440/CDM440) and Reflectance (RRS). * Temporal resolutions: daily, monthly plus, for some variables, daily gap-free based on a space-time interpolation to provide a ""cloud free"" product. * Spatial resolutions: 4 km (global), 1 km (Atlantic ; 46°W-13°E , 20°N-66°N), and a finer resolution based on olci 300 meters inputs (Atlantic and global coastal). * Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY). To find the Copernicus-GlobColour products in the catalogue, use the search keyword ""GlobColour"". DOI (product) :https://doi.org/10.48670/moi-00289 https://doi.org/10.48670/moi-00289 491 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-multiyear-ocean-colour-plankton-reflectances http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_BAL_BGC_L3_MY_009_133 Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations Short description: For the Baltic Sea Ocean Satellite Observations, the Italian National Research Council (CNR – Rome, Italy), is providing multi-years Bio-Geo_Chemical (BGC) regional datasets: * ''plankton'' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm * ''reflectance'' with the spectral Remote Sensing Reflectance (RRS) * ''transparency'' with the diffuse attenuation coefficient of light at 490 nm (KD490) Upstreams: SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv5) for the ""multi"" products, and OLCI-S3A & S3B for the ""olci"" products Temporal resolution: daily Spatial resolution: 1 km for ""multi"" and 300 meters for ""olci"" To find this product in the catalogue, use the search keyword ""OCEANCOLOUR_BAL_BGC_L3_MY"". DOI (product) :https://doi.org/10.48670/moi-00296 https://doi.org/10.48670/moi-00296 492 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-chlorophyll-time-series-and-trend-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_HEALTH_CHL_BALTIC_OCEANCOLOUR_area_averaged_mean Baltic Sea Chlorophyll-a time series and trend from Observations Reprocessing DEFINITION The time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithm over remote sensing reflectances (Rrs) provided by the Plymouth Marine Laboratory using an ad-hoc configuration for CMEMS of the ESA OC-CCI processor version 6 (OC-CCIv6) to merge at 1km resolution (rather than at 4km as for OC-CCI) MERIS, MODIS-AQUA, SeaWiFS, NPP-VIIRS and OLCI-A data. The chlorophyll product is derived from a Multi-Layer Perceptron neural-net (MLP) developed on field measurements collected within the BiOMaP program of JRC/EC (Zibordi et al., 2011). The algorithm is an ensemble of different MLPs that use Rrs at different wavelengths as input. The processing chain and the techniques used to develop the algorithm are detailed in Brando et al. (2021a; 2021b). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens’s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend. CONTEXT Phytoplankton and chlorophyll concentration as a proxy for phytoplankton respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Gregg and Rousseaux, 2014). The character of the response in the Baltic Sea depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Kahru and Elmgren 2014). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, in the Baltic Sea phytoplankton is known to respond to the variations of SST in the basin associated with climate variability (Kabel et al. 2012). CMEMS KEY FINDINGS Baltic Sea shows a positive trend in the time interval 1997-2021 with a slope of 0.67±0.46% per year. Due to the change in chlorophyll algorithm, this trend estimate cannot be compared directly to those previously reported. Maxima and minima values are quite similar year-by-year. Absolute maximum is clear in 2008 while absolute minimum is achieved during 2004. Since 2019 chlorophyll seems to increase weakly. DOI (product):https://doi.org/10.48670/moi-00197 https://doi.org/10.48670/moi-00197 493 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-biogeochemistry-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTICSEA_MULTIYEAR_BGC_003_012 Baltic Sea Biogeochemistry Reanalysis Short description: This Baltic Sea Biogeochemical Reanalysis product provides a biogeochemical reanalysis for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus 1-1.5 year compared to real time. The product is produced by using the biogeochemical model ERGOM one-way online-coupled with the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include nitrate, phosphate, ammonium, dissolved oxygen, ph, chlorophyll-a, secchi depth, surface partial co2 pressure and net primary production. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers). DOI (product) : https://doi.org/10.48670/moi-00012 https://doi.org/10.48670/moi-00012 494 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/dominant-leaf-type-2018-raster-10-m-europe-3-yearly-sep https://land.copernicus.eu/pan-european/high-resolution-layers/forests/dominant-leaf-type/status-maps/dominant-leaf-type-2018 Dominant Leaf Type 2018 (raster 10 m), Europe, 3-yearly, Sep. 2020 This metadata refers to the HRL Forest 2018 primary status layer Dominant Leaf Type (DLT). The DLT raster product provides a basic land cover classification with 3 thematic classes (all non-tree covered areas, broadleaved and coniferous) at 10m spatial resolution and covers the full of EEA38 area and the United Kingdom. The production of the High Resolution Forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. The HRL Forest product consists of 3 types of (status) products and additional change products. The status products are available for 2012, 2015, and 2018 reference years: 1. Tree cover density (TCD) (level of tree cover density in a range from 0-100%) 2. Dominant leaf type (DLT) (broadleaved or coniferous majority) 3. Forest type product (FTY). The forest type product allows to get as close as possible to the FAO forest definition. In its original (10m (2018) / 20m (2012, 2015)) resolution it consists of two products: a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps (now only available on demand), based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and imperviousness 2009 data). For the final 100 m product trees under agricultural use and urban context from the support layer are removed. NEW for 2018: the 10m 2018 reference year FTY product now also has the agricultural/urban trees removed. In the past this was done only for the 100m product, now it is consistently applied for both the 10m and the 100m FTY products. This dataset is provided as 10 meter rasters (fully conformant with the EEA reference grid) in 100 x 100 km tiles grouped according to the EEA38 countries and the United Kingdom. 495 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/medium-resolution-vegetation-phenology-and-10 https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum_d1-0.pdf Medium Resolution Vegetation Phenology and Productivity: Rate of decrease at the end of the season (raster 500m), Oct. 2022 The decrease rate, one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European Medium Resolution Vegetation Phenology and Productivity (MR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The decrease rate at the end of the season (decrease rate) expresses the rate of change in the values of the Plant Phenology Index (PPI) at the day when the vegetation growing season ends. It is calculated as the absolute value of the ratio of the difference between the right 20 % and 80% levels and the corresponding time difference. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The decrease rate at the end of the season (decrease rate) is one of the 13 parameters. The full list is available in the Product User Manual: https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… The decrease rate at the end of the season (decrease rate) time series dataset is made available as raster files with 500x 500m resolution, in ETRS89-LAEA projection corresponding to the MCD43 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2000 onwards. It is updated in the first quarter of each year. The full on-line access to open and free data for this resource will be made available by the end of 2022. Until then the data will be made available 'on-demand' by filling in the form at: https://land.copernicus.eu/contact-form https://land.copernicus.eu/contact-form 496 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-chlorophyll-time-series-and-trend http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_HEALTH_CHL_MEDSEA_OCEANCOLOUR_area_averaged_mean Mediterranean Sea Chlorophyll-a time series and trend from Observations Reprocessing DEFINITION The time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3A-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2021). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens’s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend. CONTEXT Phytoplankton and chlorophyll concentration as a proxy for phytoplankton respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Mediterranean Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) and El Niño Southern Oscillation (ENSO) (Basterretxea et al. 2018, Colella et al. 2016). CMEMS KEY FINDINGS In the Mediterranean Sea, the trend average for the 1997-2021 period is slightly negative (-0.62±0.56% per year). This result is in contrast with the analysis of Sathyendranath et al. (2018) that reveals an increasing trend in chlorophyll concentration in all the European Seas. The observations time series (in grey) shows minima values have been quite constant until 2015 and then there is a little decrease up to 2020, when an absolute minimum occurs with values lower than 0.04 mg m-3. Throughout the time series, maxima are variable year by year (with absolute maximum in 2015, >0.14 mg m-3), showing an evident reduction since 2016. In the last years of the series, the decrease of chlorophyll concentrations is also observed in the deseasonalized timeseries (in green) with a marked step in 2020-2021. This attenuation of chlorophyll values in the last years, results in an overall negative trend for the Mediterranean Sea. DOI (product):https://doi.org/10.48670/moi-00259 https://doi.org/10.48670/moi-00259 497 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cems-glofas-forecast https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-forecast cems-glofas-forecast This dataset provides a modelled time series of gridded river discharge. It is a product of the Global Flood Awareness System (GloFAS) and offers a consistent representation of a key hydrological variable across the global domain. This dataset is accompanied by two ancillary files for interpretation, one containing upstream area data and the other elevation data (see the table of related variables and the associated link in the documentation). This dataset was produced by forcing the open-source LISFLOOD hydrological model with input from the European Centre for Medium-range Weather Forecasts (ECMWF) ensemble forecast combined with the ECMWF extended-range ensemble forecast up to 30 days. Companion datasets, also available through the CDS, are historical simulations which can be used to derive the hydrological climatology and for verification; reforecasts for research, local skill assessment and post-processing; and seasonal forecasts and reforecasts for users looking for longer leadtime forecasts. For users looking specifically for European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All the GloFAS and EFAS datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS), which is managed, technically implemented and developed by the European Commission’s Joint Research Centre. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal resolution 0.05° x 0.05° for version 4.0, 0.1° x 0.1° for version 3.1 and older Vertical resolution Surface level for river discharge Temporal coverage 5 November 2019 to near real time for operational, and various dates for legacy versions Temporal resolution Daily data File format GRIB2 Conventions WMO standards for GRIB2 Versions Current version - GloFAS v4.0 released 2023-07-26. For more information on versions we refer to the documentation Update frequency Updated daily DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E) Horizontal resolution 0.05° x 0.05° for version 4.0, 0.1° x 0.1° for version 3.1 and older Horizontal resolution 0.05° x 0.05° for version 4.0, 0.1° x 0.1° for version 3.1 and older Vertical resolution Surface level for river discharge Vertical resolution Surface level for river discharge Temporal coverage 5 November 2019 to near real time for operational, and various dates for legacy versions Temporal coverage 5 November 2019 to near real time for operational, and various dates for legacy versions Temporal resolution Daily data Temporal resolution Daily data File format GRIB2 File format GRIB2 Conventions WMO standards for GRIB2 Conventions WMO standards for GRIB2 Versions Current version - GloFAS v4.0 released 2023-07-26. For more information on versions we refer to the documentation Versions Current version - GloFAS v4.0 released 2023-07-26. For more information on versions we refer to the documentation Update frequency Updated daily Update frequency Updated daily MAIN VARIABLES Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over a 24-hour period. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over a 24-hour period. River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over a 24-hour period. RELATED VARIABLES Name Units Description Elevation m The mean height elevation above sea level for each pixel in the GloFAS domain. Accessible via the link in the Documentation tab. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Accessible via the link in the Documentation tab. RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Elevation m The mean height elevation above sea level for each pixel in the GloFAS domain. Accessible via the link in the Documentation tab. Elevation m The mean height elevation above sea level for each pixel in the GloFAS domain. Accessible via the link in the Documentation tab. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Accessible via the link in the Documentation tab. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Accessible via the link in the Documentation tab. 498 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-agriculture-agera5-explorer-data-extractor https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-agriculture-agera5-explorer-data-extractor app-agriculture-agera5-explorer-data-extractor The application is an explorer for the Agrometeorological indicators from 1979 to present derived from reanalysis (AgERA5) dataset with a facility to download data for a selectable point location. The easy-to-use interface provides access to a wealth of data that could be used as input for most agriculture and agro-ecological models. Agrometeorological indicators from 1979 to present derived from reanalysis The AgERA5 dataset is based on bias-adjusted ERA5 data. It includes daily aggregates of agronomic relevant elements, tuned to local day definitions and adapted to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES operational model. The elements cover temperature, precipitation, snow depth, humidity, cloud cover and radiation. These variables match the input needs of most agriculture and agro-ecological models. The interactive map displays global maps with a layer for each selected variable and optional layers for cities, lakes, state/province borders and country borders to assist in navigation. Users can also use the snapshot tool to take an image of the visible area of the map. Clicking at any location on the map with visible data or searching a city in the search bar will produce a time-series for the selected variables. The time-series data can be downloaded in comma separated variables (.csv) format if the user agrees to the Copernicus licence. The resolution of the point selection is set to 0.1° which matches the spatial resolution of the underlying data. In the case of city selection, the 0.1° grid cell closest to the city coooridinates is extracted. Copernicus licence User-selectable parameters User-selectable parameters Year: 1979 to Present Variable: See input variables table below Year: 1979 to Present Year Variable: See input variables table below Variable INPUT VARIABLES The units listed here are the units provided in the source data, these may be different to what is used in the application which has been optimised for visualisation purposes. Name Units Description Source 2m temperature K Air temperature at a height of 2 metres above the surface. Agrometeorological indicators 2m relative humidity % Relative humidity at 06h, 09h, 12h. 15h, 18h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. Agrometeorological indicators 2m dewpoint temperature K Mean dewpoint temperature at a height of 2 metres above the surface over the period 00h-24h local time. The dew point is the temperature to which air must be cooled to become saturated with water vapor. In combination with the air temperature it is used to assess relative humidity. Agrometeorological indicators Precipitation flux mm day-1 Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per day. Agrometeorological indicators Cloud cover Dimensionless The number of hours with clouds over the period 00h-24h local time divided by 24 hours. Agrometeorological indicators 10m wind speed m s-1 Mean wind speed at a height of 10 metres above the surface over the period 00h-24h local time. Agrometeorological indicators Vapour pressure hPa Contribution to the total atmospheric pressure provided by the water vapour over the period 00-24h local time per unit of time. Agrometeorological indicators Solar radiation flux J m-2 day-1 Total amount of energy provided by solar radiation at the surface over the period 00-24h local time per unit area and time. Agrometeorological indicators Snow thickness cm Mean snow depth over the period 00h-24h local time measured as volume of snow (cm3) per unit area (cm2). Agrometeorological indicators Snow thickness LWE cm Mean snow depth liquid water equivalent (LWE) over the period 00h-24h local time measured as volume of snow (cm3) per unit area (cm2) if all the snow had melted and had not penetrated the soil, runoff, or evaporated. Agrometeorological indicators Liquid precipitation fraction Dimensionless The number of hours with precipitation over the period 00h-24h local time divided by 24 hours and per unit of area. Liquid precipitation is equivalent to the height of the layer of water that would have formed from precipitation had the water not penetrated the soil, run off, or evaporated. Agrometeorological indicators Solid precipitation fraction Dimensionless The number of hours with solid precipitation (freezing rain, snow, wet snow, mixture of rain and snow, and ice pellets) over the period 00h-24h local time divided by 24 hours and per unit of area. Agrometeorological indicators INPUT VARIABLES INPUT VARIABLES The units listed here are the units provided in the source data, these may be different to what is used in the application which has been optimised for visualisation purposes. The units listed here are the units provided in the source data, these may be different to what is used in the application which has been optimised for visualisation purposes. Name Units Description Source Name Units Description Source 2m temperature K Air temperature at a height of 2 metres above the surface. Agrometeorological indicators 2m temperature K Air temperature at a height of 2 metres above the surface. Agrometeorological indicators Agrometeorological indicators 2m relative humidity % Relative humidity at 06h, 09h, 12h. 15h, 18h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. Agrometeorological indicators 2m relative humidity % Relative humidity at 06h, 09h, 12h. 15h, 18h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. Agrometeorological indicators Agrometeorological indicators 2m dewpoint temperature K Mean dewpoint temperature at a height of 2 metres above the surface over the period 00h-24h local time. The dew point is the temperature to which air must be cooled to become saturated with water vapor. In combination with the air temperature it is used to assess relative humidity. Agrometeorological indicators 2m dewpoint temperature K Mean dewpoint temperature at a height of 2 metres above the surface over the period 00h-24h local time. The dew point is the temperature to which air must be cooled to become saturated with water vapor. In combination with the air temperature it is used to assess relative humidity. Agrometeorological indicators Agrometeorological indicators Precipitation flux mm day-1 Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per day. Agrometeorological indicators Precipitation flux mm day-1 Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per day. Agrometeorological indicators Agrometeorological indicators Cloud cover Dimensionless The number of hours with clouds over the period 00h-24h local time divided by 24 hours. Agrometeorological indicators Cloud cover Dimensionless The number of hours with clouds over the period 00h-24h local time divided by 24 hours. Agrometeorological indicators Agrometeorological indicators 10m wind speed m s-1 Mean wind speed at a height of 10 metres above the surface over the period 00h-24h local time. Agrometeorological indicators 10m wind speed m s-1 Mean wind speed at a height of 10 metres above the surface over the period 00h-24h local time. Agrometeorological indicators Agrometeorological indicators Vapour pressure hPa Contribution to the total atmospheric pressure provided by the water vapour over the period 00-24h local time per unit of time. Agrometeorological indicators Vapour pressure hPa Contribution to the total atmospheric pressure provided by the water vapour over the period 00-24h local time per unit of time. Agrometeorological indicators Agrometeorological indicators Solar radiation flux J m-2 day-1 Total amount of energy provided by solar radiation at the surface over the period 00-24h local time per unit area and time. Agrometeorological indicators Solar radiation flux J m-2 day-1 Total amount of energy provided by solar radiation at the surface over the period 00-24h local time per unit area and time. Agrometeorological indicators Agrometeorological indicators Snow thickness cm Mean snow depth over the period 00h-24h local time measured as volume of snow (cm3) per unit area (cm2). Agrometeorological indicators Snow thickness cm Mean snow depth over the period 00h-24h local time measured as volume of snow (cm3) per unit area (cm2). Agrometeorological indicators Agrometeorological indicators Snow thickness LWE cm Mean snow depth liquid water equivalent (LWE) over the period 00h-24h local time measured as volume of snow (cm3) per unit area (cm2) if all the snow had melted and had not penetrated the soil, runoff, or evaporated. Agrometeorological indicators Snow thickness LWE cm Mean snow depth liquid water equivalent (LWE) over the period 00h-24h local time measured as volume of snow (cm3) per unit area (cm2) if all the snow had melted and had not penetrated the soil, runoff, or evaporated. Agrometeorological indicators Agrometeorological indicators Liquid precipitation fraction Dimensionless The number of hours with precipitation over the period 00h-24h local time divided by 24 hours and per unit of area. Liquid precipitation is equivalent to the height of the layer of water that would have formed from precipitation had the water not penetrated the soil, run off, or evaporated. Agrometeorological indicators Liquid precipitation fraction Dimensionless The number of hours with precipitation over the period 00h-24h local time divided by 24 hours and per unit of area. Liquid precipitation is equivalent to the height of the layer of water that would have formed from precipitation had the water not penetrated the soil, run off, or evaporated. Agrometeorological indicators Agrometeorological indicators Solid precipitation fraction Dimensionless The number of hours with solid precipitation (freezing rain, snow, wet snow, mixture of rain and snow, and ice pellets) over the period 00h-24h local time divided by 24 hours and per unit of area. Agrometeorological indicators Solid precipitation fraction Dimensionless The number of hours with solid precipitation (freezing rain, snow, wet snow, mixture of rain and snow, and ice pellets) over the period 00h-24h local time divided by 24 hours and per unit of area. Agrometeorological indicators Agrometeorological indicators 499 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-cotton-explorer https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-cotton-explorer app-cotton-explorer This application explores how climate change might affect the production of cotton in cotton-producing regions around the world. Cotton is the most widely used natural fibre on the planet, with over 22 million tons of raw material produced annually. As an arable crop, cotton grows in specific environments with temperature requirements and sufficient water supply needs to achieve high-quality production. Based on the agroclimatic indicators from 1951 to 2099 derived from climate projections dataset, this application focuses on the water and temperature requirements that cotton needs to grow to maturity. High quality cotton production requires a very specific environment and this application explores changes in these growing conditions based on the ERA-interim reanalysis and bias-corrected climate datasets. Cotton supply chain stakeholders can use these indicators to compare past and future time periods to improved their understanding of the potential effects of climate changes. The application interface allows users to select a country and explore agroclimatic indicators relating to cotton for cotton-growing regions within that country. Available countries are: India, China, USA, Brazil, Pakistan, Australia, Turkey, Uzbekistan, Mexico, Argentina, Benin and Greece. User-selectable parameters User-selectable parameters Country of interest: the country for which to explore cotton-growing regions. Climate model origin: the origin of the climate model from which agroclimatic indicators are derived. RCP scenario: the climate change scenario for future climate projections. Analysis: the variable to analyse which relates to the production and cultivation of cotton. Country of interest: the country for which to explore cotton-growing regions. Climate model origin: the origin of the climate model from which agroclimatic indicators are derived. RCP scenario: the climate change scenario for future climate projections. Analysis: the variable to analyse which relates to the production and cultivation of cotton. INPUT VARIABLES Name Units Description Source Growing season length day Number of days between the first occurrence after 1st January (1st July in southern hemisphere) of at least 6 consecutive days with TG > 5°C and the first occurrence after 1st July (1st January in southern hemisphere) of at least 6 consecutive days with TG < 5°C, where TG is the daily mean temperature. This indicator provides an indication whether or not a crop, or a combination of crops, can be sown and subsequently reach maturity within a certain time frame. Agroclimatic indicators Heavy precipitation days day Number of days per 10 days when RR > 10mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Agroclimatic indicators Maximum number of consecutive dry days day Longest period of consecutive days when RR < 1mm, where RR is the daily precipitation sum. This indicator is used for drought monitoring. Agroclimatic indicators Maximum number of consecutive frost days day Longest period of consecutive days when TN < 0°C, where TN is the daily minimum temperature. This indicator is used as a general frost damage indicator. Agroclimatic indicators Maximum number of consecutive wet days day Longest period of consecutive days when RR > 1mm, where RR is the daily precipitation sum. This indicator provides information on drought, oxygen stress and crop growth (i.e. less radiation interception during rainy days). Agroclimatic indicators Maximum of daily maximum temperature K Maximum value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Mean of daily mean temperature K Mean value of TG over 10 days, where TG is the daily mean temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Minimum of daily minimum temperature K Minimum value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Very heavy precipitation days day Number of days per 10 days when RR > 20mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Agroclimatic indicators Wet days day Number of days per 10 days when RR > 1mm, where RR is the daily precipitation sum. This indicator provides information on intercepted reduction. Agroclimatic indicators INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Growing season length day Number of days between the first occurrence after 1st January (1st July in southern hemisphere) of at least 6 consecutive days with TG > 5°C and the first occurrence after 1st July (1st January in southern hemisphere) of at least 6 consecutive days with TG < 5°C, where TG is the daily mean temperature. This indicator provides an indication whether or not a crop, or a combination of crops, can be sown and subsequently reach maturity within a certain time frame. Agroclimatic indicators Growing season length day Number of days between the first occurrence after 1st January (1st July in southern hemisphere) of at least 6 consecutive days with TG > 5°C and the first occurrence after 1st July (1st January in southern hemisphere) of at least 6 consecutive days with TG < 5°C, where TG is the daily mean temperature. This indicator provides an indication whether or not a crop, or a combination of crops, can be sown and subsequently reach maturity within a certain time frame. Agroclimatic indicators Agroclimatic indicators Heavy precipitation days day Number of days per 10 days when RR > 10mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Agroclimatic indicators Heavy precipitation days day Number of days per 10 days when RR > 10mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Agroclimatic indicators Agroclimatic indicators Maximum number of consecutive dry days day Longest period of consecutive days when RR < 1mm, where RR is the daily precipitation sum. This indicator is used for drought monitoring. Agroclimatic indicators Maximum number of consecutive dry days day Longest period of consecutive days when RR < 1mm, where RR is the daily precipitation sum. This indicator is used for drought monitoring. Agroclimatic indicators Agroclimatic indicators Maximum number of consecutive frost days day Longest period of consecutive days when TN < 0°C, where TN is the daily minimum temperature. This indicator is used as a general frost damage indicator. Agroclimatic indicators Maximum number of consecutive frost days day Longest period of consecutive days when TN < 0°C, where TN is the daily minimum temperature. This indicator is used as a general frost damage indicator. Agroclimatic indicators Agroclimatic indicators Maximum number of consecutive wet days day Longest period of consecutive days when RR > 1mm, where RR is the daily precipitation sum. This indicator provides information on drought, oxygen stress and crop growth (i.e. less radiation interception during rainy days). Agroclimatic indicators Maximum number of consecutive wet days day Longest period of consecutive days when RR > 1mm, where RR is the daily precipitation sum. This indicator provides information on drought, oxygen stress and crop growth (i.e. less radiation interception during rainy days). Agroclimatic indicators Agroclimatic indicators Maximum of daily maximum temperature K Maximum value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Maximum of daily maximum temperature K Maximum value of TX over 10 days, where TX is the daily maximum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Agroclimatic indicators Mean of daily mean temperature K Mean value of TG over 10 days, where TG is the daily mean temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Mean of daily mean temperature K Mean value of TG over 10 days, where TG is the daily mean temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Agroclimatic indicators Minimum of daily minimum temperature K Minimum value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Minimum of daily minimum temperature K Minimum value of TN over 10 days, where TN is the daily minimum temperature. This indicator provides information on long-term climate variability and change. Agroclimatic indicators Agroclimatic indicators Very heavy precipitation days day Number of days per 10 days when RR > 20mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Agroclimatic indicators Very heavy precipitation days day Number of days per 10 days when RR > 20mm, where RR is the daily precipitation sum. This indicator provides information on crop damage and runoff losses. Agroclimatic indicators Agroclimatic indicators Wet days day Number of days per 10 days when RR > 1mm, where RR is the daily precipitation sum. This indicator provides information on intercepted reduction. Agroclimatic indicators Wet days day Number of days per 10 days when RR > 1mm, where RR is the daily precipitation sum. This indicator provides information on intercepted reduction. Agroclimatic indicators Agroclimatic indicators OUTPUT VARIABLES Name Units Description Number of consecutive cotton growing days day The maximum number of consecutive days in one year which are viable for growing cotton. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Number of consecutive cotton growing days day The maximum number of consecutive days in one year which are viable for growing cotton. Number of consecutive cotton growing days day The maximum number of consecutive days in one year which are viable for growing cotton. 500 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/medium-resolution-vegetation-phenology-and-productivity-4 https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum_d1-0.pdf Medium Resolution Vegetation Phenology and Productivity: Rate of increase at the start of the season (raster 500m), Oct. 2022 The increase rate, one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European Medium Resolution Vegetation Phenology and Productivity (MR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The increase rate at the end of the season (decrease rate) expresses the rate of change in the values of the Plant Phenology Index (PPI) at the day when the vegetation growing season starts. It is calculated as the ratio of the difference between the left 20 % and 80 % levels and the corresponding time difference. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The increase rate is one of the 13 parameters. The full list is available in the Product User Manual: https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… The increase rate time series dataset is made available as raster files with 500x 500m resolution, in ETRS89-LAEA projection corresponding to the MCD43 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2000 onwards. It is updated in the first quarter of each year. The full on-line access to open and free data for this resource will be made available by the end of 2022. Until then the data will be made available 'on-demand' by filling in the form at: https://land.copernicus.eu/contact-form https://land.copernicus.eu/contact-form 501 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/leaf-area-index-1999-2020-raster-1-km-global-10-daily-0 http://land.copernicus.eu/global/products/lai Leaf Area Index 1999-2020 (raster 1 km), global, 10-daily - version 2 LAI was defined by CEOS as half the developed area of the convex hull wrapping the green canopy elements per unit horizontal ground. This definition allows accounting for elements which are not flat such as needles or stems. LAI is strongly non linearly related to reflectance. Therefore, its estimation from remote sensing observations will be scale dependant over heterogeneous landscapes. When observing a canopy made of different layers of vegetation, it is therefore mandatory to consider all the green layers. This is particularly important for forest canopies where the understory may represent a very significant contribution to the total canopy LAI. The derived LAI corresponds therefore to the total green LAI, including the contribution of the green elements of the understory. The resulting GEOV1 LAI products are relatively consistent with the actual LAI for low LAI values and ?non-forest? surfaces; while for forests, particularly for needle leaf types, significant departures with the true LAI are expected. 502 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-waves-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_MULTIYEAR_WAV_001_032 Global Ocean Waves Reanalysis Short description: GLOBAL_REANALYSIS_WAV_001_032 for the global wave reanalysis describing past sea states since years 1993. This product also bears the name of WAVERYS within the GLO-HR MFC. for correspondence to other global multi-year products like GLORYS. BIORYS. etc. The core of WAVERYS is based on the MFWAM model. a third generation wave model that calculates the wave spectrum. i.e. the distribution of sea state energy in frequency and direction on a 1/5° irregular grid. Average wave quantities derived from this wave spectrum. such as the SWH (significant wave height) or the average wave period. are delivered on a regular 1/5° grid with a 3h time step. The wave spectrum is discretized into 30 frequencies obtained from a geometric sequence of first member 0.035 Hz and a reason 7.5. WAVERYS takes into account oceanic currents from the GLORYS12 physical ocean reanalysis and assimilates significant wave height observed from historical altimetry missions and directional wave spectra from Sentinel 1 SAR from 2017 onwards. DOI (product):https://doi.org/10.48670/moi-00022 https://doi.org/10.48670/moi-00022 503 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/water-bodies-1998-2020-raster-1-km-africa-tiles-10-daily http://land.copernicus.eu/global/products/wb Water Bodies 1998-2020 (raster 1 km), Africa tiles, 10-daily - version 1 WB or Water bodies is a 10-day synthesis product that detects water bodies over Africa. The product is a fusion of the former VGT4Africa Small Water Bodies and seasonality (SWB), the Global Water Watch (GWW) and a Permanent Water mask (PW) derived from Shuttle Radar Topography Mission (SRTM) and Modis water mask (MOD44W). The WB products are produced by the VEGETATION image processing center (CTIV) at VITO. 504 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/water-bodies-1998-2020-raster-1-km-africa-continent-10 http://land.copernicus.eu/global/products/wb Water Bodies 1998-2020 (raster 1 km), Africa continent, 10-daily - version 1 WB or Water bodies is a 10-day synthesis product that detects water bodies over Africa. The product is a fusion of the former VGT4Africa Small Water Bodies and seasonality (SWB), the Global Water Watch (GWW) and a Permanent Water mask (PW) derived from Shuttle Radar Topography Mission (SRTM) and Modis water mask (MOD44W). The WB products are produced by the VEGETATION image processing center (CTIV) at VITO. 505 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-atlantic-ocean-chlorophyll-time-series-and-trend http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_HEALTH_CHL_ATLANTIC_OCEANCOLOUR_area_averaged_mean North Atlantic Ocean Chlorophyll-a time series and trend from Observations Reprocessing DEFINITION The time series are derived from the regional chlorophyll reprocessed (REP) products as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithms over remote sensing reflectances (Rrs) provided by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020). Daily regional mean values are calculated by performing the average (weighted by pixel area) over the region of interest. A fixed annual cycle is extracted from the original signal, using the Census-I method as described in Vantrepotte et al. (2009). The deseasonalised time series is derived by subtracting the mean seasonal cycle from the original time series, and then fitted to a linear regression to, finally, obtain the linear trend. CONTEXT Phytoplankton – and chlorophyll concentration as a proxy for phytoplankton – respond rapidly to changes in environmental conditions, such as temperature, light and nutrients availability, and mixing. The response in the North Atlantic ranges from cyclical to decadal oscillations (Henson et al., 2009); it is therefore of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the North Atlantic are known to respond to climate variability associated with the North Atlantic Oscillation (NAO), with the initiation of the spring bloom showing a nominal correlation with sea surface temperature and the NAO index (Zhai et al., 2013). CMEMS KEY FINDINGS While the overall trend average for the 1997-2021 period in the North Atlantic Ocean is slightly positive (0.16 ± 0.12 % per year), an underlying low frequency harmonic signal can be seen in the deseasonalised data. The annual average for the region in 2021 is 0.25 mg m-3. Though no appreciable changes in the timing of the spring and autumn blooms have been observed during 2021, a lower peak chlorophyll concentration is observed in the timeseries extension. This decrease in peak concentration with respect to the previous year is contributing to the reduction trend. DOI (product):https://doi.org/10.48670/moi-00194 https://doi.org/10.48670/moi-00194 506 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-carbon-dioxide https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-carbon-dioxide satellite-carbon-dioxide This dataset provides observations of atmospheric carbon dioxide (CO2) amounts obtained from observations collected by several current and historical satellite instruments. Carbon dioxide is a naturally occurring Greenhouse Gas (GHG), but one whose abundance has been increased substantially above its pre-industrial value of some 280 ppm by human activities, primarily because of emissions from combustion of fossil fuels, deforestation and other land-use change. The annual cycle (especially in the northern hemisphere) is primarily due to seasonal uptake and release of atmospheric CO2 by terrestrial vegetation. Atmospheric carbon dioxide abundance is indirectly observed by various satellite instruments. These instruments measure spectrally resolved near-infrared and/or infrared radiation reflected or emitted by the Earth and its atmosphere. In the measured signal, molecular absorption signatures from carbon dioxide and other constituent gasses can be identified. It is through analysis of those absorption lines in these radiance observations that the averaged carbon dioxide abundance in the sampled atmospheric column can be determined. The software used to analyse the absorption lines and determine the carbon dioxide concentration in the sampled atmospheric column is referred to as the retrieval algorithm. For this dataset, carbon dioxide abundances have been determined by applying several algorithms to different satellite instruments. Typically, different algorithms have different strengths and weaknesses and therefore, which product to use for a given application typically depends on the application. The data set consists of 2 types of products: column-averaged mixing ratios of CO2, denoted XCO2 mid-tropospheric CO2 columns. column-averaged mixing ratios of CO2, denoted XCO2 mid-tropospheric CO2 columns. The XCO2 products have been retrieved from SCIAMACHY/ENVISAT, TANSO-FTS/GOSAT, TANSO-FTS2/GOSAT2 and OCO-2. The mid-tropospheric CO2 product has been retrieved from the IASI instruments on-board the Metop satellite series and from AIRS. The XCO2 products are available as Level 2 (L2) products (satellite orbit tracks) and as Level 3 (L3) product (gridded). The L2 products are available as individual sensor products (SCIAMACHY: BESD and WFMD algorithms; GOSAT: OCFP and SRFP algorithms) and as a multi-sensor merged product (EMMA algorithm). The L3 XCO2 product is provided in OBS4MIPS format. The IASI and AIRS products are available as L2 products generated with the NLIS algorithm. This data set is updated on a yearly basis, with each update cycle adding (if required) a new data version for the entire period, up to one year behind real time. This dataset is produced on behalf of C3S with the exception of the SCIAMACHY and AIRS L2 products that were generated in the framework of the GHG-CCI project of the European Space Agency (ESA) Climate Change Initiative (CCI). DATA DESCRIPTION Data type Level 2 (L2): Along satellite orbit tracks Level 3 (L3): Gridded Projection Regular latitude-longitude grid Horizontal coverage Between approximately 70°N and 70°S Horizontal resolution SCIAMACHY (L2): 30x60 km2 TANSO and TANSO2 (L2): 10 km (diameter) IASI (L2): 12 km (diameter) L3 products: 5° x 5° Vertical coverage SCIAMACHY, TANSO, TANSO2 and L3 products: Total atmospheric column AIRS and IASI: Mid-troposphere Vertical resolution Single layer Temporal coverage SCIAMACHY (L2): October 2002 until April 2012 IASI (L2): July 2007 until December 2021 TANSO (L2): April 2009 until December 2021 TANSO2 (L2): February 2019 until December 2021 L3 products: August 2003 until March 2021 Temporal resolution L3 products: Monthly L2 products are provided as observation footprints along satellite orbits at the sampling frequency of the sensor. Please refer to the documentation for more details. File format NetCDF4 Conventions Climate and Forecast Metadata Convention v1.6 (CF-1.6) Versions Multiple algorithm and file versions are available for several sensor/algorithm combinations. Users are advised to use the latest version available. Update frequency Yearly DATA DESCRIPTION DATA DESCRIPTION Data type Level 2 (L2): Along satellite orbit tracks Level 3 (L3): Gridded Data type Level 2 (L2): Along satellite orbit tracks Level 3 (L3): Gridded Level 2 (L2): Along satellite orbit tracks Level 3 (L3): Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Between approximately 70°N and 70°S Horizontal coverage Between approximately 70°N and 70°S Horizontal resolution SCIAMACHY (L2): 30x60 km2 TANSO and TANSO2 (L2): 10 km (diameter) IASI (L2): 12 km (diameter) L3 products: 5° x 5° Horizontal resolution SCIAMACHY (L2): 30x60 km2 TANSO and TANSO2 (L2): 10 km (diameter) IASI (L2): 12 km (diameter) L3 products: 5° x 5° SCIAMACHY (L2): 30x60 km2 TANSO and TANSO2 (L2): 10 km (diameter) IASI (L2): 12 km (diameter) L3 products: 5° x 5° Vertical coverage SCIAMACHY, TANSO, TANSO2 and L3 products: Total atmospheric column AIRS and IASI: Mid-troposphere Vertical coverage SCIAMACHY, TANSO, TANSO2 and L3 products: Total atmospheric column AIRS and IASI: Mid-troposphere SCIAMACHY, TANSO, TANSO2 and L3 products: Total atmospheric column AIRS and IASI: Mid-troposphere Vertical resolution Single layer Vertical resolution Single layer Temporal coverage SCIAMACHY (L2): October 2002 until April 2012 IASI (L2): July 2007 until December 2021 TANSO (L2): April 2009 until December 2021 TANSO2 (L2): February 2019 until December 2021 L3 products: August 2003 until March 2021 Temporal coverage SCIAMACHY (L2): October 2002 until April 2012 IASI (L2): July 2007 until December 2021 TANSO (L2): April 2009 until December 2021 TANSO2 (L2): February 2019 until December 2021 L3 products: August 2003 until March 2021 SCIAMACHY (L2): October 2002 until April 2012 IASI (L2): July 2007 until December 2021 TANSO (L2): April 2009 until December 2021 TANSO2 (L2): February 2019 until December 2021 L3 products: August 2003 until March 2021 Temporal resolution L3 products: Monthly L2 products are provided as observation footprints along satellite orbits at the sampling frequency of the sensor. Please refer to the documentation for more details. Temporal resolution L3 products: Monthly L2 products are provided as observation footprints along satellite orbits at the sampling frequency of the sensor. Please refer to the documentation for more details. L3 products: Monthly L2 products are provided as observation footprints along satellite orbits at the sampling frequency of the sensor. Please refer to the documentation for more details. File format NetCDF4 File format NetCDF4 Conventions Climate and Forecast Metadata Convention v1.6 (CF-1.6) Conventions Climate and Forecast Metadata Convention v1.6 (CF-1.6) Versions Multiple algorithm and file versions are available for several sensor/algorithm combinations. Users are advised to use the latest version available. Versions Multiple algorithm and file versions are available for several sensor/algorithm combinations. Users are advised to use the latest version available. Update frequency Yearly Update frequency Yearly MAIN VARIABLES Name Units Description Column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) ppm Average molar mixing ratio (or mole fraction in micro mole carbon dioxide (CO2) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CO2 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCO2 indicates that the reported quantity is a "mole fraction" Mid-tropospheric columns of atmospheric carbon dioxide (CO2) ppm Average CO2 mixing ratio of the mid-troposphere. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) ppm Average molar mixing ratio (or mole fraction in micro mole carbon dioxide (CO2) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CO2 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCO2 indicates that the reported quantity is a "mole fraction" Column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) ppm Average molar mixing ratio (or mole fraction in micro mole carbon dioxide (CO2) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CO2 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCO2 indicates that the reported quantity is a "mole fraction" Average molar mixing ratio (or mole fraction in micro mole carbon dioxide (CO2) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CO2 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCO2 indicates that the reported quantity is a "mole fraction" Mid-tropospheric columns of atmospheric carbon dioxide (CO2) ppm Average CO2 mixing ratio of the mid-troposphere. Mid-tropospheric columns of atmospheric carbon dioxide (CO2) ppm Average CO2 mixing ratio of the mid-troposphere. RELATED VARIABLES The optimal estimation inversion algorithms used to compute the column average CO2 are based on a number of atmospheric variables like pressure, temperature, water vapour, scattering by aerosols and clouds, spectral albedo, including initial a-priori values and averaging kernels as well as estimates of uncertainty on the values of XCO2. Depending on the sensor and algorithm, a number of these variables are also included in the files along the main variable XCO2. RELATED VARIABLES RELATED VARIABLES The optimal estimation inversion algorithms used to compute the column average CO2 are based on a number of atmospheric variables like pressure, temperature, water vapour, scattering by aerosols and clouds, spectral albedo, including initial a-priori values and averaging kernels as well as estimates of uncertainty on the values of XCO2. Depending on the sensor and algorithm, a number of these variables are also included in the files along the main variable XCO2. The optimal estimation inversion algorithms used to compute the column average CO2 are based on a number of atmospheric variables like pressure, temperature, water vapour, scattering by aerosols and clouds, spectral albedo, including initial a-priori values and averaging kernels as well as estimates of uncertainty on the values of XCO2. Depending on the sensor and algorithm, a number of these variables are also included in the files along the main variable XCO2. 507 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-european-north-west-shelf-ocean-physics-analysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=NWSHELF_ANALYSISFORECAST_PHY_LR_004_001 Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast Short description: The low resolution ocean physics analysis and forecast for the North-West European Shelf is produced using a forecasting ocean assimilation model, with tides, at 7 km horizontal resolution. The ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature, vertical profiles of temperature and salinity, and along track satellite sea level anomaly data. The model is forced by lateral boundary conditions from the UK Met Office North Atlantic Ocean forecast model and by the CMEMS Baltic forecast product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… BALTICSEA_ANALYSISFORECAST_PHY_003_006]. The atmospheric forcing is given by the operational UK Met Office Global Atmospheric model. The river discharge is from a daily climatology. Further details of the model, including the product validation are provided in the [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004… CMEMS-NWS-QUID-004-001]. Products are provided as hourly instantaneous and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated daily, providing a 6-day forecast and the previous 2-day assimilative hindcast. See [http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-0… CMEMS-NWS-PUM-004-001_002] for further details. https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004… http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-0… Associated products: This model is coupled with a biogeochemistry model (ERSEM) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NWSHELF_ANALYSISFORECAST_BGC_004_002] A reanalysis product is available from: [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NWSHELF_MULTIYEAR_PHY_004_009]. https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… DOI (product) :https://doi.org/10.48670/moi-00057 https://doi.org/10.48670/moi-00057 508 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/season-maximum-value-2017-present-raster-10-m-europe https://www.wekeo.eu/data?view=viewer&t=1577905116279&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1QSEVOT0xPR1ktQU5ELVBST0RVQ1RJVklUWS1QQVJBTUVURVJTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVlBQX01BWFZfU0VBU09OMV8xME0iLCJ6SW5kZXgiOjgwfV0%3D&initial=1 Season Maximum Value 2017-present (raster 10 m), Europe, yearly, Sept. 2021 The Season Maximum Value (MAXV), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Season Maximum Value (MAXV) provides the maximum (peak) value that the Plant Phenology Index (PPI) reaches during the vegetation growing season. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Season Maximum Value is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-m… https://land.copernicus.eu/user-corner/technical-library/product-user-m… A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The MAXV dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year. 509 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-mean-sea-level-time-series-and-trend http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTIC_OMI_SL_area_averaged_anomalies Baltic Sea Mean Sea Level time series and trend from Observations Reprocessing DEFINITION The sea level ocean monitoring indicator is derived from the DUACS delayed-time (DT-2021 version) altimeter gridded maps of sea level anomalies based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The mean sea level evolution estimated in the Baltic Sea is derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed (least scare fit of sinusoidal function) and the time series is low-pass filtered. The curve is corrected for the regional mean effect of the Glacial Isostatic Adjustment (GIA) using the ICE5G-VM2 GIA model (Peltier, 2004) During 1993-1998, the Global men sea level (hereafter GMSL) has been known to be affected by a TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018; Legeais et al., 2020). This drift led to overestimate the trend of the GMSL during the first 6 years of the altimetry record (about 0.04 mm/y at global scale over the whole altimeter period). A correction of the drift is proposed for the Global mean sea level (Legeais et al., 2020). Whereas this TOPEX-A instrumental drift should also affect the regional mean sea level (hereafter RMSL) trend estimation, this empirical correction is currently not applied to the altimeter sea level dataset and resulting estimates for RMSL. Indeed, the pertinence of the global correction applied at regional scale has not been demonstrated yet and there is no clear consensus achieved on the way to proceed at regional scale. Additionally, the estimate of such a correction at regional scale is not obvious, especially in areas where few accurate independent measurements (e.g., in situ) - necessary for this estimation - are available. The trend uncertainty is provided in a 90% confidence interval (Prandi et al., 2021). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not taken into account. CONTEXT The indicator on area averaged sea level is a crucial index of climate change, and individual components contribute to sea level rise, including expansion due to ocean warming and melting of glaciers and ice sheets (WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report, global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018 with a rate 25 of rise that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm yr-1 for the period 2006–2018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). At regional scale, sea level does not change homogenously, and RMSL rise can also be influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). Rising sea level can strongly affect population and infrastructures in coastal areas, increase their vulnerability and risks for food security, particularly in low lying areas and island states. Adverse impacts from floods, storms and tropical cyclones with related losses and damages have increased due to sea level rise, and increase their vulnerability and increase risks for food security, particularly in low lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). The Baltic Sea is a relatively small semi-enclosed basin with shallow bathymetry. Different forcings have been discussed to trigger sea level variations in the Baltic Sea at different time scales. In addition to steric effects, decadal and longer sea level variability in the basin can be induced by sea water exchange with the North Sea, and in response to atmospheric forcing and climate variability (e.g., the North Atlantic Oscillation; Gräwe et al., 2019). CMEMS KEY FINDINGS Over the [1993/01/01, 2021/08/02] period, the basin-wide RMSL in the Baltic Sea rises at a rate of 4.5  0.84 mm/year. DOI (product):https://doi.org/10.48670/moi-00202 https://doi.org/10.48670/moi-00202 510 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-biodiversity-suitability-hedgerows https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-biodiversity-suitability-hedgerows app-biodiversity-suitability-hedgerows This application explores climate suitability for typical multi-species hedgerows at six point locations over Europe, underpinned by CMIP5 climate projections (bias-adjusted to ERA5) for air temperature and precipitation in the 6-months growing season (April-September). Hedgerow suitability provides stakeholders within the agro-ecology sector with information about the climate impact on hedgerow ecosystems to support management decisions. The interactive map shows 20-year averages for air temperature and precipitation over three time frames (1981-2000 (historical), 2041-2060 (near future) and 2081-2100 (far future)) for two climate scenarios, i.e. the Representative Concentration Pathways RCP4.5 (moderate scenario) and RCP8.5 (pessimistic scenario) at high resolution (1km x 1km). Clicking on a map marker reveals a time series plot of the modelled climate suitability for a selection of the location’s typical hedgerows (based on species-specific seasonal temperature and precipitation envelopes of multiple species) at standard resolution (0.5° x 0.5° ~ 50km x 50km). User-selectable parameters User-selectable parameters Variable: The data visualised in the interactive map. Variable: The data visualised in the interactive map. INPUT VARIABLES Name Units Description Source Annual mean temperature (BIO01) K Annual mean of the daily mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Bioclimatic indicators Annual precipitation (BIO12) m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Bioclimatic indicators Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Bioclimatic indicators Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). The data is aggregated over the months. Bioclimatic indicators Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). The data is aggregated over the months. Bioclimatic indicators Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds. Bioclimatic indicators Monthly mean precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Bioclimatic indicators INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Annual mean temperature (BIO01) K Annual mean of the daily mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Bioclimatic indicators Annual mean temperature (BIO01) K Annual mean of the daily mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Bioclimatic indicators Bioclimatic indicators Annual precipitation (BIO12) m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Bioclimatic indicators Annual precipitation (BIO12) m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Bioclimatic indicators Bioclimatic indicators Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Bioclimatic indicators Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Bioclimatic indicators Bioclimatic indicators Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). The data is aggregated over the months. Bioclimatic indicators Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). The data is aggregated over the months. Bioclimatic indicators Bioclimatic indicators Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). The data is aggregated over the months. Bioclimatic indicators Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). The data is aggregated over the months. Bioclimatic indicators Bioclimatic indicators Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds. Bioclimatic indicators Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds. Bioclimatic indicators Bioclimatic indicators Monthly mean precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Bioclimatic indicators Monthly mean precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Bioclimatic indicators Bioclimatic indicators OUTPUT VARIABLES Name Units Description Climate suitability Dimensionless Climate suitability (0-1) for different grassland species in accordance to the above bioclimate indicators; calculation using the CMIP5-based bio-climatic indicators. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Climate suitability Dimensionless Climate suitability (0-1) for different grassland species in accordance to the above bioclimate indicators; calculation using the CMIP5-based bio-climatic indicators. Climate suitability Dimensionless Climate suitability (0-1) for different grassland species in accordance to the above bioclimate indicators; calculation using the CMIP5-based bio-climatic indicators. 511 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-european-risk-extreme-precipitation-indicators https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-risk-extreme-precipitation-indicators sis-european-risk-extreme-precipitation-indicators The dataset presents climate impact indicators related to extreme precipitation in Europe under current climate conditions. The suite of indicators include recent historic records, recurrence intervals, and other relevant statistical measures to evaluate the magnitude and frequency of extreme precipitation events. These are provided as gridded products, with one product covering the whole of Europe, and the other higher resolution product focused on 20 European cities that were identified as vulnerable to urban pluvial flooding based on stakeholder surveys. This dataset makes use of precipitation data available in the Climate Data Store (i.e. E-OBS gridded land-only observational dataset and ERA5 reanalysis) combined with additional datasets capable of improving the spatial and temporal resolution of the precipitation data, making it suitable for pluvial flood analysis at city scales. These are derived from i) the network of meteorological stations included in the European Climate Assessment & Dataset (ECA&D) programme and ii) dynamically downscaled ERA5 reanalysis at 2 km x 2 km (ERA5-2km) using the regional climate model COSMO-CLM and accounting for urban parameterization, specifically performed for the 20 European cities identified as vulnerable to urban pluvial flooding. At the European scale, E-OBS and ERA5 precipitation data are used to compute indicators at different temporal resolutions (i.e. daily, monthly, yearly, and 30-year) according to the type of indicator. The precipitation amounts at fixed return periods are also computed for point observations from meteorological stations using the ECA&D network and are then interpolated onto the E-OBS grid. At the city scale, a dynamically downscaled ERA5-2km precipitation data are instead used to derive daily indicators, allowing city stakeholders to detect and rank local extreme precipitation events and evaluate their magnitude. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage Europe Horizontal resolution ERA5: 0.25° x 0.25° E-OBS: 0.1° x 0.1° ERA5-2km: 0.02° x 0.02° ECA&D: 0.1° x 0.1° Vertical coverage Surface Vertical resolution Single level Temporal coverage ERA5: 1979-2020 E-OBS: 1950-2019 ERA5-2km: 1989-2018 ECA&D: 1989-2018 Temporal resolution Daily, monthly, yearly, and 30-year statistics File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions 1.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Europe Horizontal coverage Europe Horizontal resolution ERA5: 0.25° x 0.25° E-OBS: 0.1° x 0.1° ERA5-2km: 0.02° x 0.02° ECA&D: 0.1° x 0.1° Horizontal resolution ERA5: 0.25° x 0.25° E-OBS: 0.1° x 0.1° ERA5-2km: 0.02° x 0.02° ECA&D: 0.1° x 0.1° ERA5: 0.25° x 0.25° E-OBS: 0.1° x 0.1° ERA5-2km: 0.02° x 0.02° ECA&D: 0.1° x 0.1° Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage ERA5: 1979-2020 E-OBS: 1950-2019 ERA5-2km: 1989-2018 ECA&D: 1989-2018 Temporal coverage ERA5: 1979-2020 E-OBS: 1950-2019 ERA5-2km: 1989-2018 ECA&D: 1989-2018 ERA5: 1979-2020 E-OBS: 1950-2019 ERA5-2km: 1989-2018 ECA&D: 1989-2018 Temporal resolution Daily, monthly, yearly, and 30-year statistics Temporal resolution Daily, monthly, yearly, and 30-year statistics File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions 1.0 Versions 1.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Maximum 1-day precipitation kg m-2 The maximum amount of precipitation in one day for the user-selected month or year period. Maximum 5-day precipitation kg m-2 The maximum amount of precipitation in 5 consecutive days for the user-selected month or year period. Number of consecutive wet days Count The maximum number of consecutive days with the daily precipitation amount greater than 1 mm for the user-selected month or year period. Number of precipitation days exceeding 20mm Count The number of days with at least 20 mm of daily precipitation for the user-selected month or year period. Number of precipitation days exceeding fixed percentiles Count The number of days with daily precipitation exceeding the 90th, 95th, or 99th percentile of wet days (daily precipitation ≥ 1 mm) for the user-selected month or year. Number of wet days Count The number of days with the daily precipitation amount greater than 1 mm for the user-selected month or year period. Precipitation at fixed percentiles kg m-2 Total precipitation when daily precipitation amounts exceed the 90th, 95th, or 99th percentiles in wet days (daily precipitation ≥ 1 mm) computed over the 30-year period (1989-2018). Precipitation at fixed return periods kg m-2 Daily precipitation amount characterised by the 5, 10, 25, 50, or 100 year return period computed over the 30-year period (1989-2018). Standardised precipitation exceeding fixed percentiles Dimensionless Standardised daily precipitation amount over the grid point's 95th or 99th percentile of wet days (daily precipitation ≥ 1 mm). Values are decimal, ranging between 0-17 (95th percentile) or 0-10 (99th percentile). These values may be used to detect and rank extreme precipitation events. Total precipitation mm Total precipitation amount in one month or one year. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Maximum 1-day precipitation kg m-2 The maximum amount of precipitation in one day for the user-selected month or year period. Maximum 1-day precipitation kg m-2 The maximum amount of precipitation in one day for the user-selected month or year period. Maximum 5-day precipitation kg m-2 The maximum amount of precipitation in 5 consecutive days for the user-selected month or year period. Maximum 5-day precipitation kg m-2 The maximum amount of precipitation in 5 consecutive days for the user-selected month or year period. Number of consecutive wet days Count The maximum number of consecutive days with the daily precipitation amount greater than 1 mm for the user-selected month or year period. Number of consecutive wet days Count The maximum number of consecutive days with the daily precipitation amount greater than 1 mm for the user-selected month or year period. Number of precipitation days exceeding 20mm Count The number of days with at least 20 mm of daily precipitation for the user-selected month or year period. Number of precipitation days exceeding 20mm Count The number of days with at least 20 mm of daily precipitation for the user-selected month or year period. Number of precipitation days exceeding fixed percentiles Count The number of days with daily precipitation exceeding the 90th, 95th, or 99th percentile of wet days (daily precipitation ≥ 1 mm) for the user-selected month or year. Number of precipitation days exceeding fixed percentiles Count The number of days with daily precipitation exceeding the 90th, 95th, or 99th percentile of wet days (daily precipitation ≥ 1 mm) for the user-selected month or year. Number of wet days Count The number of days with the daily precipitation amount greater than 1 mm for the user-selected month or year period. Number of wet days Count The number of days with the daily precipitation amount greater than 1 mm for the user-selected month or year period. Precipitation at fixed percentiles kg m-2 Total precipitation when daily precipitation amounts exceed the 90th, 95th, or 99th percentiles in wet days (daily precipitation ≥ 1 mm) computed over the 30-year period (1989-2018). Precipitation at fixed percentiles kg m-2 Total precipitation when daily precipitation amounts exceed the 90th, 95th, or 99th percentiles in wet days (daily precipitation ≥ 1 mm) computed over the 30-year period (1989-2018). Precipitation at fixed return periods kg m-2 Daily precipitation amount characterised by the 5, 10, 25, 50, or 100 year return period computed over the 30-year period (1989-2018). Precipitation at fixed return periods kg m-2 Daily precipitation amount characterised by the 5, 10, 25, 50, or 100 year return period computed over the 30-year period (1989-2018). Standardised precipitation exceeding fixed percentiles Dimensionless Standardised daily precipitation amount over the grid point's 95th or 99th percentile of wet days (daily precipitation ≥ 1 mm). Values are decimal, ranging between 0-17 (95th percentile) or 0-10 (99th percentile). These values may be used to detect and rank extreme precipitation events. Standardised precipitation exceeding fixed percentiles Dimensionless Standardised daily precipitation amount over the grid point's 95th or 99th percentile of wet days (daily precipitation ≥ 1 mm). Values are decimal, ranging between 0-17 (95th percentile) or 0-10 (99th percentile). These values may be used to detect and rank extreme precipitation events. Total precipitation mm Total precipitation amount in one month or one year. Total precipitation mm Total precipitation amount in one month or one year. 512 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/high-resolution-snow-and-ice-monitoring-daily-cumulative https://cryo.land.copernicus.eu/finder/ High Resolution Snow and Ice Monitoring: Daily cumulative Gap-filled Fractional Snow Cover (raster 60m) The Copernicus Daily Cumulative Gap-filled Fractional Snow Cover (GFSC) product is generated in near real-time for the entire EEA38 and the United Kingdom, based on radar satellite data from the Sentinel-1 constellation and on optical data from the Sentinel-2 constellation. The product uses Copernicus FSC (Fractional Snow Cover), WDS (Wet/Dry Snow) and SWS (SAR Wet Snow) products along with DEM data as input to form a spatially complete composite of snow conditions, to reduce observational gaps due to clouds and lack of sensor coverage on a daily basis. The product provides the extent of the snow cover per pixel as a percentage (0% – 100%) with a spatial resolution of 60 m x 60 m. The GFSC product is distributed in raster files covering an area of 110 km by 110 km with a pixel size of 60 m by 60 m in UTM/WGS84 projection, which corresponds to the Sentinel-2 input L1C product tile. Each product is composed of four separate GeoTIFF files corresponding to the different layers of the product, and a metadata file. The GF layer provides the gap-filled fractional snow cover, while the QC layers (AT, QCFLAGS, QC) provide information on the quality of the GF layer at the pixel level . The GFSC is one of the products of the pan-European High-Resolution Snow & Ice service (HR-S&I), which are provided at high spatial resolution (20 m x 20 m and 60 m x 60 m), from the Sentinel-2 and Sentinel-1 constellations data from September 1, 2016 onwards. Visit https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… to get more information on the different HR-S&I products (Snow products : FSC, WDS, SWS, GFSC, and PSA. Ice products: RLIE and ARLIE). https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… 513 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-european-north-west-shelf-wave-physics http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=NWSHELF_REANALYSIS_WAV_004_015 Atlantic- European North West Shelf- Wave Physics Reanalysis Short description: This product provides long term hindcast outputs from a wave model for the North-West European Shelf. The wave model is WAVEWATCH III and the North-West Shelf configuration is based on a two-tier Spherical Multiple Cell grid mesh (3 and 1.5 km cells) derived from with the 1.5km grid used for [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013]. The model is forced by lateral boundary conditions from a Met Office Global wave hindcast. The atmospheric forcing is given by the [https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ECMWF ERA-5] Numerical Weather Prediction reanalysis. Model outputs comprise wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state and wind-sea and swell components. The data are delivered on a regular grid at approximately 1.5km resolution, consistent with physical ocean and wave analysis-forecast products. See [http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-0… CMEMS-NWS-PUM-004-015] for more information. Further details of the model, including source term physics, propagation schemes, forcing and boundary conditions, and validation, are provided in the [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004… CMEMS-NWS-QUID-004-015]. The product is updated biannually provinding six-month extension of the time series. https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-0… http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004… Associated products: [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014]. https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… DOI (product) :https://doi.org/10.48670/moi-00060 https://doi.org/10.48670/moi-00060 514 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-high-resolution-sar-sea-ice-drift http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006 Global Ocean - High Resolution SAR Sea Ice Drift Short description: DTU Space produces polar covering Near Real Time gridded ice displacement fields obtained by MCC processing of Sentinel-1 SAR, Envisat ASAR WSM swath data or RADARSAT ScanSAR Wide mode data . The nominal temporal span between processed swaths is 24hours, the nominal product grid resolution is a 10km. DOI (product) :https://doi.org/10.48670/moi-00135 https://doi.org/10.48670/moi-00135 515 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-l3s-sea-surface-temperature-reprocessed http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_BAL_PHY_L3S_MY_010_040 Baltic Sea - L3S Sea Surface Temperature Reprocessed Short description: For the Baltic Sea- the DMI Sea Surface Temperature reprocessed L3S aims at providing daily multi-sensor supercollated data at 0.02deg. x 0.02deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR DOI (product) :https://doi.org/10.48670/moi-00312 https://doi.org/10.48670/moi-00312 516 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-era5-pressure-levels-preliminary-back https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-preliminary-back-extension reanalysis-era5-pressure-levels-preliminary-back-extension This entry is a preliminary version of the ERA5 reanalysis back extension from 1950 to 1978. It has now been superseded by the ERA5 Climate Data Store entries from 1940 onwards and will be deprecated in due course. Therefore, users are advised to use the latter, final release, instead Although in many other respects the quality of this dataset is quite satisfactory (Bell et al., 2021), this preliminary data does suffer from tropical cyclones that are sometimes unrealistically intense. This is in contrast with the ERA5 product from 1959 onwards. For more details see the articles, ERA5 back extension 1950-1978 (Preliminary version): tropical cyclones are too intense and Changes in the ERA5 back extension compared to its preliminary version. (Bell et al., 2021) ERA5 back extension 1950-1978 (Preliminary version): tropical cyclones are too intense Changes in the ERA5 back extension compared to its preliminary version ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Currently, data is available from 1940, with superseded Climate Data Store entries for 1950-1978 (preliminary back extension, this page) and from 1940 onwards (final release plus timely updates). ERA5 replaces the ERA-Interim reanalysis. ERA5 Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. this article these guidelines Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on pressure levels from 1950 to 1978 (preliminary version)". DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution Reanalysis: 0.25°x0.25° Mean, spread and members: 0.5°x0.5° Vertical coverage 1000 hPa to 1 hPa Vertical resolution 37 pressure levels Temporal coverage 1950 to 1978 Temporal resolution Hourly File format GRIB DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution Reanalysis: 0.25°x0.25° Mean, spread and members: 0.5°x0.5° Horizontal resolution Reanalysis: 0.25°x0.25° Mean, spread and members: 0.5°x0.5° Reanalysis: 0.25°x0.25° Mean, spread and members: 0.5°x0.5° Vertical coverage 1000 hPa to 1 hPa Vertical coverage 1000 hPa to 1 hPa Vertical resolution 37 pressure levels Vertical resolution 37 pressure levels Temporal coverage 1950 to 1978 Temporal coverage 1950 to 1978 Temporal resolution Hourly Temporal resolution Hourly File format GRIB File format GRIB MAIN VARIABLES Name Units Description Divergence s-1 This parameter is the horizontal divergence of velocity. It is the rate at which air is spreading out horizontally from a point, per square metre. This parameter is positive for air that is spreading out, or diverging, and negative for the opposite, for air that is concentrating, or converging (convergence). Fraction of cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud (liquid or ice) and varies between zero and one. This parameter is available on multiple levels through the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography. Ozone mass mixing ratio kg kg-1 This parameter is the mass of ozone per kilogram of air. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. Most of the IFS chemical species are archived as mass mixing ratios [kg kg-1]. Potential vorticity K m2 kg-1 s-1 Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere. Relative humidity % This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function. Specific cloud ice water content kg kg-1 This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'. Specific cloud liquid water content kg kg-1 This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific humidity kg kg-1 This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. Specific rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Temperature K This parameter is the temperature in the atmosphere. It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere. U-component of wind m s-1 This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east. A negative sign indicates air moving towards the west. This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind. V-component of wind m s-1 This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north. A negative sign indicates air moving towards the south. This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind. Vertical velocity Pa s-1 This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion. Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values). Vorticity (relative) s-1 This parameter is a measure of the rotation of air in the horizontal, around a vertical axis, relative to a fixed point on the surface of the Earth. On the scale of weather systems, troughs (weather features that can include rain) are associated with anticlockwise rotation (in the northern hemisphere), and ridges (weather features that bring light or still winds) are associated with clockwise rotation. Adding the effect of rotation of the Earth, the Coriolis parameter, to the relative vorticity produces the absolute vorticity. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Divergence s-1 This parameter is the horizontal divergence of velocity. It is the rate at which air is spreading out horizontally from a point, per square metre. This parameter is positive for air that is spreading out, or diverging, and negative for the opposite, for air that is concentrating, or converging (convergence). Divergence s-1 This parameter is the horizontal divergence of velocity. It is the rate at which air is spreading out horizontally from a point, per square metre. This parameter is positive for air that is spreading out, or diverging, and negative for the opposite, for air that is concentrating, or converging (convergence). Fraction of cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud (liquid or ice) and varies between zero and one. This parameter is available on multiple levels through the atmosphere. Fraction of cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud (liquid or ice) and varies between zero and one. This parameter is available on multiple levels through the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography. Ozone mass mixing ratio kg kg-1 This parameter is the mass of ozone per kilogram of air. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. Most of the IFS chemical species are archived as mass mixing ratios [kg kg-1]. Ozone mass mixing ratio kg kg-1 This parameter is the mass of ozone per kilogram of air. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. Most of the IFS chemical species are archived as mass mixing ratios [kg kg-1]. Potential vorticity K m2 kg-1 s-1 Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere. Potential vorticity K m2 kg-1 s-1 Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere. Relative humidity % This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function. Relative humidity % This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function. Specific cloud ice water content kg kg-1 This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'. Specific cloud ice water content kg kg-1 This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'. Specific cloud liquid water content kg kg-1 This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud liquid water content kg kg-1 This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific humidity kg kg-1 This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. Specific humidity kg kg-1 This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. Specific rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Temperature K This parameter is the temperature in the atmosphere. It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere. Temperature K This parameter is the temperature in the atmosphere. It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere. U-component of wind m s-1 This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east. A negative sign indicates air moving towards the west. This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind. U-component of wind m s-1 This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east. A negative sign indicates air moving towards the west. This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind. V-component of wind m s-1 This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north. A negative sign indicates air moving towards the south. This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind. V-component of wind m s-1 This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north. A negative sign indicates air moving towards the south. This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind. Vertical velocity Pa s-1 This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion. Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values). Vertical velocity Pa s-1 This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion. Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values). Vorticity (relative) s-1 This parameter is a measure of the rotation of air in the horizontal, around a vertical axis, relative to a fixed point on the surface of the Earth. On the scale of weather systems, troughs (weather features that can include rain) are associated with anticlockwise rotation (in the northern hemisphere), and ridges (weather features that bring light or still winds) are associated with clockwise rotation. Adding the effect of rotation of the Earth, the Coriolis parameter, to the relative vorticity produces the absolute vorticity. Vorticity (relative) s-1 This parameter is a measure of the rotation of air in the horizontal, around a vertical axis, relative to a fixed point on the surface of the Earth. On the scale of weather systems, troughs (weather features that can include rain) are associated with anticlockwise rotation (in the northern hemisphere), and ridges (weather features that bring light or still winds) are associated with clockwise rotation. Adding the effect of rotation of the Earth, the Coriolis parameter, to the relative vorticity produces the absolute vorticity. 517 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-water-level-change-indicators-cmip6 https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-water-level-change-indicators-cmip6 sis-water-level-change-indicators-cmip6 This dataset provides statistical indicators of tides, storm surges and sea level that can be used to characterize global sea level in present-day conditions and also to assess changes under climate change. The indicators calculated include extreme-value indicators (e.g. return periods including confidence bounds for total water levels and surge levels), probability indicators (e.g. percentile for total water levels and surge levels). They provide a basis for studies aiming to evaluate sea level variability, coastal flooding, coastal erosion, and accessibility of ports at a global scale. The extreme value statistics for different return periods can be used to assess the frequency of an event and form the basis of risk assessments. The global coverage allows for world-wide assessments that are particularly useful for the data scarce regions where detailed modelling studies are currently lacking. The indicators are computed from time series data available in a related dataset in the Climate Data Store named Global sea level change time series from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections (see Related data), where further details of the modelling are provided. The indicators are produced for three different 30-year periods corresponding to historical, present, and future climate conditions (1951-1980, 1985-2014, and 2021-2050). The future period is based on global climate projections using the high-emission scenario SSP5-8.5. The dataset is based on climate forcing from ERA5 global reanalysis and 4 Global Climate Models (GCMs) of the high resolution Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate projection dataset from the High Resolution Model Intercomparison Project (HighResMIP) multi-model ensemble. An estimate of the uncertainties associated with the climate forcing has been obtained through the use of a multi-model ensemble. Each of the indicators provides ensemble statistics computed across the 4 members of the HighResMIP ensemble (e.g. median, mean, standard deviation, range). Absolute and relative changes for the future period (2015-2050) relative to the present-day (1985-2014) are provided to assess climate change impacts on water levels. Global sea level change time series from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded with variable grid step Projection Latitude-longitude grid Horizontal coverage Global Horizontal resolution Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Vertical coverage Surface Vertical resolution Single level Temporal coverage 1950-2050 Temporal resolution Annual and 30-year periods (1951-1980, 1985-2014 and 2021-2050) File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions Dataset version 1.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded with variable grid step Data type Gridded with variable grid step Projection Latitude-longitude grid Projection Latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Horizontal resolution Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage 1950-2050 Temporal coverage 1950-2050 Temporal resolution Annual and 30-year periods (1951-1980, 1985-2014 and 2021-2050) Temporal resolution Annual and 30-year periods (1951-1980, 1985-2014 and 2021-2050) File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions Dataset version 1.0 Versions Dataset version 1.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Annual mean of highest high water m Annual average of the highest high tide (MHHW) of each tidal day (25-hour window) including sea level rise. Storm surge caused by atmospheric forcing is not taken into account. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Annual mean of lowest low water m Annual average of the lowest low tide (MLLW) of each tidal day (25-hour window) including sea level rise. Storm surge caused by atmospheric forcing is not taken into account. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Highest astronomical tide m Highest Astronomical Tide (HAT) is the elevation of the highest predicted astronomical tide expected to occur at a specific location over the datum (i.e. MSL). HAT is calculated as the maximum over the 30-year simulation period. All tide variables are derived from a tide-only simulation with GTSM. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Lowest astronomical tide m Lowest Astronomical Tide (LAT) is the elevation of the lowest predicted astronomical tide expected to occur at a specific location over the datum (i.e. MSL). LAT is calculated as the minimum over the 30-year simulation period. All tide variables are derived from a tide-only simulation with GTSM. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Mean sea level m The average water level of a 30-year tide-only simulation (MSL). This includes the interaction effects with tides and sea level rise over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. The vertical reference level is mean sea level over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Surge level m Surge level are defined as the difference between the tide-only and the total water level simulations, and include (changes in) surge levels and interactions. Total water level and surge level simulations are forced by ERA5 reanalysis and the HighResMIP ensemble. The following percentiles are computed: 5th, 10th, 25th, 50th, 75th, 90th and 95th. The following return periods are also computed: 1, 2, 5, 10, 25, 50, 75, 100 year. The relative change is computed for the HighResMIP ensemble for 2021-2050 and 1951-1980, using 1985-2014 as the reference period. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Tidal range m Tidal range (TR) is the height difference between the mean higher high water and mean lower low water over the 30-year period simulated. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Total water level m Total water levels include (changes in) tidal levels, surge levels and interactions, but with sea level rise removed. Total water level and surge level simulations are forced by ERA5 reanalysis and the HighResMIP ensemble. The following percentiles are computed: 5th, 10th, 25th, 50th, 75th, 90th and 95th. The following return periods are also computed: 1, 2, 5, 10, 25, 50, 75, 100 year. The relative change is computed for the HighResMIP ensemble for 2021-2050 and 1951-1980, using 1985-2014 as the reference period. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Annual mean of highest high water m Annual average of the highest high tide (MHHW) of each tidal day (25-hour window) including sea level rise. Storm surge caused by atmospheric forcing is not taken into account. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Annual mean of highest high water m Annual average of the highest high tide (MHHW) of each tidal day (25-hour window) including sea level rise. Storm surge caused by atmospheric forcing is not taken into account. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Annual mean of lowest low water m Annual average of the lowest low tide (MLLW) of each tidal day (25-hour window) including sea level rise. Storm surge caused by atmospheric forcing is not taken into account. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Annual mean of lowest low water m Annual average of the lowest low tide (MLLW) of each tidal day (25-hour window) including sea level rise. Storm surge caused by atmospheric forcing is not taken into account. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Highest astronomical tide m Highest Astronomical Tide (HAT) is the elevation of the highest predicted astronomical tide expected to occur at a specific location over the datum (i.e. MSL). HAT is calculated as the maximum over the 30-year simulation period. All tide variables are derived from a tide-only simulation with GTSM. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Highest astronomical tide m Highest Astronomical Tide (HAT) is the elevation of the highest predicted astronomical tide expected to occur at a specific location over the datum (i.e. MSL). HAT is calculated as the maximum over the 30-year simulation period. All tide variables are derived from a tide-only simulation with GTSM. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Lowest astronomical tide m Lowest Astronomical Tide (LAT) is the elevation of the lowest predicted astronomical tide expected to occur at a specific location over the datum (i.e. MSL). LAT is calculated as the minimum over the 30-year simulation period. All tide variables are derived from a tide-only simulation with GTSM. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Lowest astronomical tide m Lowest Astronomical Tide (LAT) is the elevation of the lowest predicted astronomical tide expected to occur at a specific location over the datum (i.e. MSL). LAT is calculated as the minimum over the 30-year simulation period. All tide variables are derived from a tide-only simulation with GTSM. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Mean sea level m The average water level of a 30-year tide-only simulation (MSL). This includes the interaction effects with tides and sea level rise over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. The vertical reference level is mean sea level over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Mean sea level m The average water level of a 30-year tide-only simulation (MSL). This includes the interaction effects with tides and sea level rise over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. The vertical reference level is mean sea level over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Surge level m Surge level are defined as the difference between the tide-only and the total water level simulations, and include (changes in) surge levels and interactions. Total water level and surge level simulations are forced by ERA5 reanalysis and the HighResMIP ensemble. The following percentiles are computed: 5th, 10th, 25th, 50th, 75th, 90th and 95th. The following return periods are also computed: 1, 2, 5, 10, 25, 50, 75, 100 year. The relative change is computed for the HighResMIP ensemble for 2021-2050 and 1951-1980, using 1985-2014 as the reference period. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Surge level m Surge level are defined as the difference between the tide-only and the total water level simulations, and include (changes in) surge levels and interactions. Total water level and surge level simulations are forced by ERA5 reanalysis and the HighResMIP ensemble. The following percentiles are computed: 5th, 10th, 25th, 50th, 75th, 90th and 95th. The following return periods are also computed: 1, 2, 5, 10, 25, 50, 75, 100 year. The relative change is computed for the HighResMIP ensemble for 2021-2050 and 1951-1980, using 1985-2014 as the reference period. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Tidal range m Tidal range (TR) is the height difference between the mean higher high water and mean lower low water over the 30-year period simulated. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Tidal range m Tidal range (TR) is the height difference between the mean higher high water and mean lower low water over the 30-year period simulated. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Total water level m Total water levels include (changes in) tidal levels, surge levels and interactions, but with sea level rise removed. Total water level and surge level simulations are forced by ERA5 reanalysis and the HighResMIP ensemble. The following percentiles are computed: 5th, 10th, 25th, 50th, 75th, 90th and 95th. The following return periods are also computed: 1, 2, 5, 10, 25, 50, 75, 100 year. The relative change is computed for the HighResMIP ensemble for 2021-2050 and 1951-1980, using 1985-2014 as the reference period. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. Total water level m Total water levels include (changes in) tidal levels, surge levels and interactions, but with sea level rise removed. Total water level and surge level simulations are forced by ERA5 reanalysis and the HighResMIP ensemble. The following percentiles are computed: 5th, 10th, 25th, 50th, 75th, 90th and 95th. The following return periods are also computed: 1, 2, 5, 10, 25, 50, 75, 100 year. The relative change is computed for the HighResMIP ensemble for 2021-2050 and 1951-1980, using 1985-2014 as the reference period. The vertical reference level is mean sea level (MSL) over the 1986-2005 reference period. Please refer to the appendix in the user documentation for details on the vertical reference level. RELATED VARIABLES The longitude and latitude coordinates for the stations are provided in the variables named as station_x_coordinates and station_y_coordinates respectively. RELATED VARIABLES RELATED VARIABLES The longitude and latitude coordinates for the stations are provided in the variables named as station_x_coordinates and station_y_coordinates respectively. The longitude and latitude coordinates for the stations are provided in the variables named as station_x_coordinates and station_y_coordinates respectively. 518 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/european-seas-along-track-l3-sea-surface-heights http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_EUR_PHY_L3_MY_008_061 EUROPEAN SEAS ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION Short description: Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications. This product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details) http://duacs.cls.fr http://duacs.cls.fr “’Associated products”’ A time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_NOIS… describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document. https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_NOIS… DOI (product):https://doi.org/10.48670/moi-00139 https://doi.org/10.48670/moi-00139 519 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-freshwater-content-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=ARCTIC_OMI_TEMPSAL_FWC Arctic Freshwater Content from Reanalysis DEFINITION Estimates of Arctic liquid Freshwater Content (FWC in meters) are obtained from integrated differences of the measured salinity and a reference salinity (set to 34.8) from the surface to the bottom per unit area in the Arctic region with a water depth greater than 500m as function of salinity (S), the vertical cell thickness of the dataset (dz) and the salinity reference (Sref). Waters saltier than the 34.8 reference are not included in the estimation. The regional FWC values from 1993 up to real time are then averaged aiming to: * obtain the mean FWC as expressed in cubic km (km3) * monitor the large-scale variability and change of liquid freshwater stored in the Arctic Ocean (i.e. the change of FWC in time). CONTEXT The Arctic region is warming twice as fast as the global mean and its climate is undergoing unprecedented and drastic changes, affecting all the components of the Arctic system. Many of these changes affect the hydrological cycle. Monitoring the storage of freshwater in the Arctic region is essential for understanding the contemporary Earth system state and variability. Variations in Arctic freshwater can induce changes in ocean stratification. Exported southward downstream, these waters have potential future implications for global circulation and heat transport. CMEMS KEY FINDINGS Since 1993, the Arctic Ocean freshwater has experienced a significant increase of 423 ± 39 km3/year. The year 2016 witnessed the highest freshwater content in the Artic since the last 24 years. Second half of 2016 and first half of 2017 show a substantial decrease of the FW storage. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00193 https://doi.org/10.48670/moi-00193 520 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-lai-fapar https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-lai-fapar satellite-lai-fapar Fraction of absorbed photosynthetically active radiation: FAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the canopy. The FAPAR value results directly from the radiative transfer in the canopy which is instantaneous. It depends on canopy structure, vegetation element optical properties and illumination conditions. FAPAR is very useful as input to a number of primary productivity models based on simple efficiency considerations. FAPAR is relatively linearly related to reflectance values and is little sensitive to scaling issues. Note also that the FAPAR refers only to the green parts of the canopy. Leaf area Index: LAI is defined as half the developed area of photosynthetically active elements of the vegetation per unit horizontal ground area. It determines the size of the interface for exchange of energy (including radiation) and mass between the canopy and the atmosphere. This is an intrinsic canopy primary variable that should not depend on observation conditions. LAI is strongly non-linearly related to reflectance. Therefore, its estimation from remote sensing observations is scale dependent. Note that vegetation LAI as estimated from remote sensing includes all the green contributors such as the understory when existing under forests canopies. However, except when using directional observations, LAI is not directly accessible from remote sensing observations due to the possible heterogeneity in leaf distribution within the canopy volume. Therefore, remote sensing observations are rather sensitive to the ‘effective’ leaf area index, i.e. the value that provides the same diffuse gap fraction while assuming a random distribution of leaves. The difference between the actual LAI and the effective LAI may be quantified by the clumping index that roughly varies between 0.5 (very clumped canopies) and 1.0 (randomly distributed leaves). Note that similarly to the other variables, the retrieved LAI is mainly corresponding to the green elements: the correct term to be used would be GAI (Green Area Index) although we propose to still use LAI for the sake of simplicity. The following product versions are available: Version v0.0 is a brokered dataset from the Copernicus Global Land Service (CGLS). Version v1.0 is produced by the C3S service. In this version, the theoretical framework for the CDR LAI and FAPAR algorithm capitalizes on the past experience gained in the Copernicus Global Land products (Baret et al. 2013). It capitalizes itself on the development and validation of already existing products: CYCLOPES Version 3.1 and MODIS Collection 5, and the use of neural networks. Verger et al. (2008) demonstrated that this approach could be used efficiently to estimate LAI and FAPAR from existing products provided that a strong link exists between the inputs and the outputs in the training dataset. Error propagation was included in the version v1.0 (compared to v0.0). Version v2.0 is produced by the C3S service. This version is retrieved by applying the Two-stream Inversion Package (TIP) to visible (VIS) and near-infrared (NIR) broadband albedos. TIP is based on the Two-stream Model developed by Pinty et al. 2006, which implements the two-stream approximation of radiative transfer for a homogeneous canopy (“1D-canopy”). Version v3.0 is produced by the C3S service. This version builds on version 2 and adds a multi-sensor aspect to the LAI/FAPAR products delivered so far. The inversion of the BRDF coefficients was improved. A climatological BRDF based on SPOT-VGT is introduced as a priori information used for the inversion of the albedos which is the input for the TIP model. Version v4.0 is produced by the C3S service and builds on version 3 using the Sentinel3 A/B SLSTR and OLCI dataset. Version v0.0 is a brokered dataset from the Copernicus Global Land Service (CGLS). Version v1.0 is produced by the C3S service. In this version, the theoretical framework for the CDR LAI and FAPAR algorithm capitalizes on the past experience gained in the Copernicus Global Land products (Baret et al. 2013). It capitalizes itself on the development and validation of already existing products: CYCLOPES Version 3.1 and MODIS Collection 5, and the use of neural networks. Verger et al. (2008) demonstrated that this approach could be used efficiently to estimate LAI and FAPAR from existing products provided that a strong link exists between the inputs and the outputs in the training dataset. Error propagation was included in the version v1.0 (compared to v0.0). Version v2.0 is produced by the C3S service. This version is retrieved by applying the Two-stream Inversion Package (TIP) to visible (VIS) and near-infrared (NIR) broadband albedos. TIP is based on the Two-stream Model developed by Pinty et al. 2006, which implements the two-stream approximation of radiative transfer for a homogeneous canopy (“1D-canopy”). Version v3.0 is produced by the C3S service. This version builds on version 2 and adds a multi-sensor aspect to the LAI/FAPAR products delivered so far. The inversion of the BRDF coefficients was improved. A climatological BRDF based on SPOT-VGT is introduced as a priori information used for the inversion of the albedos which is the input for the TIP model. Version v4.0 is produced by the C3S service and builds on version 3 using the Sentinel3 A/B SLSTR and OLCI dataset. Please note that LAI version v0.0 and v1.0 represent the actual LAI. Starting from version v2.0, the LAI produced by the TIP (Two Inversion Package) model represents the "effective" LAI. The difference between the actual LAI and the effective LAI may be quantified by the clumping index that roughly varies between 0.5 (very clumped canopies) and 1.0 (randomly distributed leaves). DATA DESCRIPTION Data type Gridded Projection Plate Carrée Horizontal coverage Global land surface Horizontal resolution AVHRR: 1/30° (~4 km) VGT: 1/112° (~1 km) Sentinel-3: 1/336° (~300 m) Vertical coverage Top of the canopy Temporal coverage AVHRR: September 1981 to December 2005 SPOT-VGT: April 1998 to May 2014 PROBA-VGT: October 2013 to June 2020 Sentinel-3: July 2018 to April 2019 Temporal resolution 10 days File format NetCDF Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions There are 5 product versions available, please refer to the overview text and/or documentation for further details on the differences between these versions. Update frequency Monthly updates DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Plate Carrée Projection Plate Carrée Horizontal coverage Global land surface Horizontal coverage Global land surface Horizontal resolution AVHRR: 1/30° (~4 km) VGT: 1/112° (~1 km) Sentinel-3: 1/336° (~300 m) Horizontal resolution AVHRR: 1/30° (~4 km) VGT: 1/112° (~1 km) Sentinel-3: 1/336° (~300 m) AVHRR: 1/30° (~4 km) VGT: 1/112° (~1 km) Sentinel-3: 1/336° (~300 m) Vertical coverage Top of the canopy Vertical coverage Top of the canopy Temporal coverage AVHRR: September 1981 to December 2005 SPOT-VGT: April 1998 to May 2014 PROBA-VGT: October 2013 to June 2020 Sentinel-3: July 2018 to April 2019 Temporal coverage AVHRR: September 1981 to December 2005 SPOT-VGT: April 1998 to May 2014 PROBA-VGT: October 2013 to June 2020 Sentinel-3: July 2018 to April 2019 AVHRR: September 1981 to December 2005 SPOT-VGT: April 1998 to May 2014 PROBA-VGT: October 2013 to June 2020 Sentinel-3: July 2018 to April 2019 Temporal resolution 10 days Temporal resolution 10 days File format NetCDF File format NetCDF Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions There are 5 product versions available, please refer to the overview text and/or documentation for further details on the differences between these versions. Versions There are 5 product versions available, please refer to the overview text and/or documentation for further details on the differences between these versions. Update frequency Monthly updates Update frequency Monthly updates MAIN VARIABLES Name Units Description Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Dimensionless It is a dimensionless quantity varying from 0 (over deserts) to 1 (for large, deep, homogeneous canopy layers observed by medium- to low-resolution sensors) quantifying the aumount of solar radiation in the spectral range 400–700 nm, known as photosynthetically active radiation (PAR), absorbed by the plants relatively to the total amount of energy available at that spectral range. The maximum value is never witnessed in practice because some of the incoming light is always reflected back by the canopy or the underlying ground. Leaf Area Index (LAI) Dimensionless Leaf Area Index of a plant canopy or ecosystem, is defined as one half of the total green leaf area per unit horizontal ground surface area. It measures the area of leaf material present in the specified environment. This dimensionless variable (sometimes expressed in terms of square metres of leaf material per square metre of ground) varies between 0 and values of about 10 or so, depending on local conditions. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Dimensionless It is a dimensionless quantity varying from 0 (over deserts) to 1 (for large, deep, homogeneous canopy layers observed by medium- to low-resolution sensors) quantifying the aumount of solar radiation in the spectral range 400–700 nm, known as photosynthetically active radiation (PAR), absorbed by the plants relatively to the total amount of energy available at that spectral range. The maximum value is never witnessed in practice because some of the incoming light is always reflected back by the canopy or the underlying ground. Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Dimensionless It is a dimensionless quantity varying from 0 (over deserts) to 1 (for large, deep, homogeneous canopy layers observed by medium- to low-resolution sensors) quantifying the aumount of solar radiation in the spectral range 400–700 nm, known as photosynthetically active radiation (PAR), absorbed by the plants relatively to the total amount of energy available at that spectral range. The maximum value is never witnessed in practice because some of the incoming light is always reflected back by the canopy or the underlying ground. Leaf Area Index (LAI) Dimensionless Leaf Area Index of a plant canopy or ecosystem, is defined as one half of the total green leaf area per unit horizontal ground surface area. It measures the area of leaf material present in the specified environment. This dimensionless variable (sometimes expressed in terms of square metres of leaf material per square metre of ground) varies between 0 and values of about 10 or so, depending on local conditions. Leaf Area Index (LAI) Dimensionless Leaf Area Index of a plant canopy or ecosystem, is defined as one half of the total green leaf area per unit horizontal ground surface area. It measures the area of leaf material present in the specified environment. This dimensionless variable (sometimes expressed in terms of square metres of leaf material per square metre of ground) varies between 0 and values of about 10 or so, depending on local conditions. RELATED VARIABLES Along with the main variables the following variables are included in V0 and V1: - Error uncertainty on every band in the variable; - QFLAG: the quality flag of the product; - NMOD: the number of valid observations during the 20-day synthesis period that are used to calculate. In V2 the following variables are included: - Error: uncertainty on every band in the variable; - In FAPAR product: unc_correl: uncertainty correlation is the correlation of LAI_ERR and fAPAR _ERR. It can be used to compute the full variance-covariance matrix of LAI and FAPAR, as it would ideally be used in a joint assimilation. RELATED VARIABLES RELATED VARIABLES Along with the main variables the following variables are included in V0 and V1: - Error uncertainty on every band in the variable; - QFLAG: the quality flag of the product; - NMOD: the number of valid observations during the 20-day synthesis period that are used to calculate. In V2 the following variables are included: - Error: uncertainty on every band in the variable; - In FAPAR product: unc_correl: uncertainty correlation is the correlation of LAI_ERR and fAPAR _ERR. It can be used to compute the full variance-covariance matrix of LAI and FAPAR, as it would ideally be used in a joint assimilation. Along with the main variables the following variables are included in V0 and V1: - Error uncertainty on every band in the variable; - QFLAG: the quality flag of the product; - NMOD: the number of valid observations during the 20-day synthesis period that are used to calculate. In V2 the following variables are included: - Error: uncertainty on every band in the variable; - In FAPAR product: unc_correl: uncertainty correlation is the correlation of LAI_ERR and fAPAR _ERR. It can be used to compute the full variance-covariance matrix of LAI and FAPAR, as it would ideally be used in a joint assimilation. 521 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-l3-spectral-parameters-reprocessed-satellite http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=WAVE_GLO_PHY_SPC_L3_MY_014_006 GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM REPROCESSED SATELLITE MEASUREMENTS Short description: Multi-Year mono-mission satellite-based integral parameters derived from the directional wave spectra. Using linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered. The dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land). The integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.This product is processed by the WAVE-TAC multi-mission SAR data processing system. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B.One file is produced for each mission and is available in two formats: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land. DOI (product) :https://doi.org/10.48670/moi-00174 https://doi.org/10.48670/moi-00174 522 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-coastal-indicators-waves-projections https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-coastal-indicators-waves-projections app-coastal-indicators-waves-projections The application presents a range of European coastal indicators, including water levels, tidal ranges and ocean surface wave parameters under the impacts associated with climate change up the 2100. The indicators are useful for various coastal sectors and studies, for example assessing coastal flooding, coastal erosion, infrastructure planning and adaption studies. This application is underpinned by the C3S datasets ‘Water level change indicators for the European coast from 1977 to 2100 derived from climate projections’. The oceanic wave indicators are calculated with ECMWF’s Stand-Alone Wave model (SAW) and the water-level indicators are calculated with Deltares Global Tide and Surge Model (GTSMv3.0) hydrodynamic model. These models were forced with meteorological data from ERA5 reanalysis (2001 to 2017) and a EURO-CORDEX model output for a historical period (1977 to 2005) and two future periods for different representative concentration pathways (2041 to 2070 for RCP 8.5 and 2070 to 2100 for RCP 4.5), termed ‘Reanalysis' & ‘Historical’, ‘RCP4.5’, ‘RCP8.5’ within the application. The projections of these climate scenarios are based on a single combination of the regional and global climate models, users of these data should take in consideration that there is an inevitable underestimation of the uncertainty associated with this dataset. The application consists of an interactive map centred on the European coastal domain with 4 selectable layers for given time-slices and scenarios. The map presents the tidal, sea-level and wave indicators spatially data aggregated onto marine regions or projected on to a regular latitude/longitude grid (a course and fine resolution, i.e., 1 degree and 0.1 degree). A “number of points” layer is included to give an indication of the density of data points when projected on to a regular grid. These points are called ‘station data’ and the density of these points are higher in coastal areas, versus open ocean, please refer to the dataset documentation for further details. When spatial aggregation is set to "Marine regions", users can click on a marine region produce the focused view. The boxplot of all data points within that region for the 4 time-slices and scenarios. The marine regions are sourced from the "Marine Ecoregions of the World" openly available from the world-wildlife-fund. When the spatial aggregation is set to "Coarse grid" or "Fine grid" users can click on a point on the interactive map to produce a focused view of that location. The focussed view contains a box plot of all the grid points within a given radius (default = 1.0°) of that point and a line plot presents the value of the closest station (non-interpolated data point). Users can select the radius using the input field. This focussed view can also be opened by searching a coastal city in the search field in the top right of the interactive map. User-selectable parameters User-selectable parameters Indicator: One of the 14 indicators listed in the input variables table Spatial aggregation: Course grid, fine grid or marine regions. Statistic: Multi-model ensemble statistic or return period. Only available for a subset of the indicators, please refer to the input variables table for more details Indicator: One of the 14 indicators listed in the input variables table Indicator Spatial aggregation: Course grid, fine grid or marine regions. Spatial aggregation Statistic: Multi-model ensemble statistic or return period. Only available for a subset of the indicators, please refer to the input variables table for more details Statistic INPUT VARIABLES Name Units Description Source Annual highest high water m Annual highest high tide including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Annual lowest low water m Annual lowest low tide including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Annual mean highest high water m Annual average of the highest high tide of each tidal day (25 hour window) including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Annual mean lowest low water m Annual average of the lowest low tide of each tidal day (25 hour window) including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Epoch-mean highest high water m Highest high tide of each tidal day (25 hour window), including mean sea level and sea level rise, averaged over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Epoch-mean lowest low water m Lowest low tide of each tidal day (25 hour window), including mean sea level and sea level rise, averaged over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Highest astronomical tide m Highest astronomical tide over the 30-year period simulated. Water level change indicators Lowest astronomical tide m Lowest astronomical tide over the 30-year period simulated. Water level change indicators Mean sea level m Mean sea level including sea level rise observed over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Peak wave period s The period of the most energetic ocean waves generated by local winds and associated with swell. Statistic options are available for: Percentiles: 90th, 95th, 99th and 90th-100th percentile average Return periods: 100 year Ocean surface wave indicators Significant wave height m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. Statistic options are available for: Percentiles: 90th, 95th, 99th and 90th-100th percentile average Return periods: 100 year Ocean surface wave indicators Surge level m Storm surge level, defined as the difference between the pure tide and the total water level simulations. Statistic options are available for: Percentiles: 10th, 25th, 50th, 75th and 90th Return periods: 2, 5, 10, 25, 50 and 100 years. Water level change indicators Tidal range m Average tidal range observed over the 30-year period simulated. Water level change indicators Total water level m Total water level, including tide, surge level and taking future sea level rise into account. Statistic options are available for: Percentiles: 10th, 25th, 50th, 75th and 90th Return periods: 2, 5, 10, 25, 50 and 100 years. Water level change indicators INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Annual highest high water m Annual highest high tide including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Annual highest high water m Annual highest high tide including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Water level change indicators Annual lowest low water m Annual lowest low tide including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Annual lowest low water m Annual lowest low tide including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Water level change indicators Annual mean highest high water m Annual average of the highest high tide of each tidal day (25 hour window) including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Annual mean highest high water m Annual average of the highest high tide of each tidal day (25 hour window) including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Water level change indicators Annual mean lowest low water m Annual average of the lowest low tide of each tidal day (25 hour window) including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Annual mean lowest low water m Annual average of the lowest low tide of each tidal day (25 hour window) including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Water level change indicators Epoch-mean highest high water m Highest high tide of each tidal day (25 hour window), including mean sea level and sea level rise, averaged over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Epoch-mean highest high water m Highest high tide of each tidal day (25 hour window), including mean sea level and sea level rise, averaged over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Water level change indicators Epoch-mean lowest low water m Lowest low tide of each tidal day (25 hour window), including mean sea level and sea level rise, averaged over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Epoch-mean lowest low water m Lowest low tide of each tidal day (25 hour window), including mean sea level and sea level rise, averaged over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Water level change indicators Highest astronomical tide m Highest astronomical tide over the 30-year period simulated. Water level change indicators Highest astronomical tide m Highest astronomical tide over the 30-year period simulated. Water level change indicators Water level change indicators Lowest astronomical tide m Lowest astronomical tide over the 30-year period simulated. Water level change indicators Lowest astronomical tide m Lowest astronomical tide over the 30-year period simulated. Water level change indicators Water level change indicators Mean sea level m Mean sea level including sea level rise observed over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Mean sea level m Mean sea level including sea level rise observed over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Water level change indicators Water level change indicators Peak wave period s The period of the most energetic ocean waves generated by local winds and associated with swell. Statistic options are available for: Percentiles: 90th, 95th, 99th and 90th-100th percentile average Return periods: 100 year Ocean surface wave indicators Peak wave period s The period of the most energetic ocean waves generated by local winds and associated with swell. Statistic options are available for: Percentiles: 90th, 95th, 99th and 90th-100th percentile average Return periods: 100 year The period of the most energetic ocean waves generated by local winds and associated with swell. Statistic options are available for: Percentiles: 90th, 95th, 99th and 90th-100th percentile average Return periods: 100 year Ocean surface wave indicators Ocean surface wave indicators Significant wave height m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. Statistic options are available for: Percentiles: 90th, 95th, 99th and 90th-100th percentile average Return periods: 100 year Ocean surface wave indicators Significant wave height m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. Statistic options are available for: Percentiles: 90th, 95th, 99th and 90th-100th percentile average Return periods: 100 year This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. Statistic options are available for: Percentiles: 90th, 95th, 99th and 90th-100th percentile average Return periods: 100 year Ocean surface wave indicators Ocean surface wave indicators Surge level m Storm surge level, defined as the difference between the pure tide and the total water level simulations. Statistic options are available for: Percentiles: 10th, 25th, 50th, 75th and 90th Return periods: 2, 5, 10, 25, 50 and 100 years. Water level change indicators Surge level m Storm surge level, defined as the difference between the pure tide and the total water level simulations. Statistic options are available for: Percentiles: 10th, 25th, 50th, 75th and 90th Return periods: 2, 5, 10, 25, 50 and 100 years. Storm surge level, defined as the difference between the pure tide and the total water level simulations. Statistic options are available for: Percentiles: 10th, 25th, 50th, 75th and 90th Return periods: 2, 5, 10, 25, 50 and 100 years. Water level change indicators Water level change indicators Tidal range m Average tidal range observed over the 30-year period simulated. Water level change indicators Tidal range m Average tidal range observed over the 30-year period simulated. Water level change indicators Water level change indicators Total water level m Total water level, including tide, surge level and taking future sea level rise into account. Statistic options are available for: Percentiles: 10th, 25th, 50th, 75th and 90th Return periods: 2, 5, 10, 25, 50 and 100 years. Water level change indicators Total water level m Total water level, including tide, surge level and taking future sea level rise into account. Statistic options are available for: Percentiles: 10th, 25th, 50th, 75th and 90th Return periods: 2, 5, 10, 25, 50 and 100 years. Total water level, including tide, surge level and taking future sea level rise into account. Statistic options are available for: Percentiles: 10th, 25th, 50th, 75th and 90th Return periods: 2, 5, 10, 25, 50 and 100 years. Water level change indicators Water level change indicators 523 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-along-track-l3-sea-surface-heights http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_L3_MY_008_062 GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS REPROCESSED (1993-ONGOING) TAILORED FOR DATA ASSIMILATION Short description: Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications. This product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc.). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details) http://duacs.cls.fr http://duacs.cls.fr “’Associated products”’ A time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_NOIS… describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document. https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_NOIS… DOI (product):https://doi.org/10.48670/moi-00146 https://doi.org/10.48670/moi-00146 524 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/pan-european-high-resolution-image-mosaic-2018-false https://land.copernicus.eu/imagery-in-situ/european-image-mosaics/high-resolution/high-resolution-image-mosaics-2018/high-resolution-image-mosaic-2018-false-colour-10m Pan-European High Resolution Image Mosaic 2018 - False Colour, Coverage 1 (10 m), Sept. 2019 The pan-European High Resolution (HR) Image Mosaic 2018 provides cloud-free high resolution false colour imagery for EEA39 countries. The mosaic has been produced using Sentinel-2 data in 10 meter resolution, at a Sentinel 2 tile level, and consists of 1079 Sentinel-2 tiles. The imagery for each state is acquired within a predefined window corresponding to the vegetation season in 2018. The false colour composite consists of a three band stack and includes the following bands: Band 8 – NIR (0.842 μm) Band 4 – Red (0.665 μm) Band 3 – Green (0.560 μm) The mosaic primarily is used as input data in the production of various Copernicus Land Monitoring Service (CLMS) datasets and services, such as land cover maps and high resolution layers on land cover characteristic and can be also useful for CLMS users for visualizations and classifications on land. Since the pan-European High Resolution Mosaic 2018 is created exclusively from Sentinel-2 data, the imagery can be downloaded from The Copernicus Open Access Hub (mission ongoing since 2015) at https://scihub.copernicus.eu/. Global Sentinel-2 Image Mosaics Hub at https://land.copernicus.eu/imagery-in-situ/global-image-mosaics/ can be also used to automatically create mosaics over the area of interest. https://scihub.copernicus.eu/ https://land.copernicus.eu/imagery-in-situ/global-image-mosaics/ 525 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sea-ice-thickness-derived-merging-cryosat-2-and-smos-ice http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_ARC_PHY_L4_NRT_011_014 Sea Ice Thickness derived from merging CryoSat-2 and SMOS ice thickness Short description: Arctic sea ice thickness from merged SMOS and Cryosat-2 (CS2) observations during freezing season between October and April. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. On the other hand, CS2 uses radar altimetry to measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. DOI (product) :https://doi.org/10.48670/moi-00125 https://doi.org/10.48670/moi-00125 526 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-high-resolution-and-ultra-high http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_MED_SST_L3S_NRT_OBSERVATIONS_010_012 Mediterranean Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature Short description: For the Mediterranean Sea (MED), the CNR MED Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Mediterranean Sea at high (1/16°) and Ultra High (0.01°) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. DOI (product) :https://doi.org/10.48670/moi-00171 https://doi.org/10.48670/moi-00171 527 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-iberian-biscay-irish-ocean-wave-analysis-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=IBI_ANALYSIS_FORECAST_WAV_005_005 Atlantic-Iberian Biscay Irish- Ocean Wave Analysis and Forecast Short description: The IBI-MFC provides a high-resolution wave analysis and forecast product (run twice a day by Nologin with the support of CESGA in terms of supercomputing resources), covering the European waters, and more specifically the Iberia–Biscay–Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as hourly instantaneous forecasts with a horizon of up to 10 days (updated on a daily basis) are available on the catalogue. The IBI wave model system is based on the MFWAM model and runs on a grid of 5 km of horizontal resolution forced with the ECMWF hourly wind data. The system assimilates significant wave height (SWH) altimeter data and CFOSAT wave spectral data (supplied by Météo-France), and it is forced by currents provided by the IBI ocean circulation system. The product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum and fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra. Additionally, the IBI wave system is set up to provide internally some key parameters adequate to be used as forcing in the IBI NEMO ocean model forecast run. Product Citation: Please refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag…] http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (Product):https://doi.org/10.48670/moi-00025 https://doi.org/10.48670/moi-00025 528 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/insitu-observations-gruan-reference-network https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-observations-gruan-reference-network insitu-observations-gruan-reference-network The Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) is an international reference observing network, established in 2006, of sites measuring essential climate variables above Earth's surface, designed to fill an important gap in the current global observing system. GRUAN measurements are providing high-quality climate data records from the surface, through the troposphere, and into the stratosphere. These are being used to determine trends, constrain and calibrate data from more spatially comprehensive observing systems (including satellites and current radiosonde networks), and provide appropriate data for studying atmospheric processes. GRUAN is envisaged as a global network of eventually 30-40 sites that, to the extent possible, builds on existing observational networks and capabilities. GRUAN observation processing is designed to adjust systematic errors in radiosonde measurements of pressure, temperature, humidity, and wind, and to quantify how the uncertainties related to these error sources are derived. This characterization ensures the traceability to SI units or accepted standards, providing extensive metadata and full documentation of measurements and algorithms. This allows the full reprocessing from the raw data. The Climate Data Store provides access to the GRUAN data version 2 (V2). Currently, only two GRUAN data products (GDP), for Vaisala RS92 and for Meisei RS-11G sondes, are available. In due time (expected early 2021) GRUAN data products for the new Vaisala RS41 sondes will be added. Specific GRUAN data products for other sonde types are also under development. The current dataset provides radiosoundings from 17 stations. The GRUAN dataset provides the vertical aggregation of 12 quantities as listed below in the main-variables table. Attributes are described in the related-variables tables. Radiosoundings can be downloaded as comma-separated values (CSV) files organised in two different ways: CSV one row per level: for each observed height all variables are provided as individual columns in one row. Each main variable has its dedicated columns for related uncertainty and bias correction, where applies (referenced as csv-lev in the variable lists below). CSV one row per observation: no aggregation; each row provides information about one variable at one level only. All main variable are grouped together in the following columns: one for the observed variable (indicates variable in question), one for observation value, one column for each of the three flavours of uncertainty and one for bias correction (referenced as csv-obs in the variable lists below). CSV one row per level: for each observed height all variables are provided as individual columns in one row. Each main variable has its dedicated columns for related uncertainty and bias correction, where applies (referenced as csv-lev in the variable lists below). CSV one row per observation: no aggregation; each row provides information about one variable at one level only. All main variable are grouped together in the following columns: one for the observed variable (indicates variable in question), one for observation value, one column for each of the three flavours of uncertainty and one for bias correction (referenced as csv-obs in the variable lists below). More details about the the GRUAN dataset are given in the Documentation section, and general information and background can be obtained from the GRUAN website (www.gruan.org). www.gruan.org DATA DESCRIPTION Data type Point data Horizontal coverage 17 stations, distributed around the globe Vertical coverage Up to about 40km, varying from sonder launch Vertical resolution Varying per station, typically 5-10 m Temporal coverage 2006 to March 2020. Each station has its own start date Temporal resolution Sub-daily File format CSV Versions GRUAN algorithm Version 2 Update frequency Irregular, next version expected by early 2021 DATA DESCRIPTION DATA DESCRIPTION Data type Point data Data type Point data Horizontal coverage 17 stations, distributed around the globe Horizontal coverage 17 stations, distributed around the globe Vertical coverage Up to about 40km, varying from sonder launch Vertical coverage Up to about 40km, varying from sonder launch Vertical resolution Varying per station, typically 5-10 m Vertical resolution Varying per station, typically 5-10 m Temporal coverage 2006 to March 2020. Each station has its own start date Temporal coverage 2006 to March 2020. Each station has its own start date Temporal resolution Sub-daily Temporal resolution Sub-daily File format CSV File format CSV Versions GRUAN algorithm Version 2 Versions GRUAN algorithm Version 2 Update frequency Irregular, next version expected by early 2021 Update frequency Irregular, next version expected by early 2021 MAIN VARIABLES Name Units Description Air temperature K Temperature in the atmosphere at the observed height. Temperature measured in Kelvin can be converted to degrees Celsius by subtracting 273.15. Its uncertainty is estimated with a GRUAN correction scheme. Air temperature post processing radiation correction K Bias correction applied to air temperature by a GRUAN correction scheme (csv-lev only). Air temperature random uncertainty K Statistical standard deviation (k=1) of air temperature (csv-obs only). Air temperature systematic uncertainty K Correlated uncertainty for air temperature of systematic uncertainty sources estimated from calibration, calibration correction, radiation correction, time-lag (csv-lev only). Air temperature total uncertainty K Standard uncertainty (k=1) of air temperature (csv-obs only). Altitude m Geometric altitude above sea level calculated from air pressure and GNSS altitude. Altitude total uncertainty m Standard uncertainty (k=1) of altitude (csv-obs only). Eastward wind component m s-1 Wind component towards the east. Frost point temperature K Temperature, below 0° C, at which moisture in the air will condense as a layer of frost on any exposed surface. The frost point temperature is calculated from relative humidity using the water vapor pressure formula of HylandWexler, corrected by a GRUAN correction scheme. Geopotential height m A measure of the height of a point in the atmosphere in relation to its potential energy. It is calculated by dividing the geopotential by the Earth's mean gravitational acceleration, g (=9.80665 m s-2 ). The geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. Geopotential is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. Northward wind component m s-1 Wind component towards the north. Relative humidity % The water vapour pressure as a percentage of the value at which the air becomes saturated. For GRUAN data it is collated from U1 and U2 based on the water vapor pressure formula of HylandWexler, corrected by a GRUAN correction scheme. Relative humidity effective vertical resolution % The water vapour pressure as a percentage of the value at which the air becomes saturated. For GRUAN data it is collated from U1 and U2 based on the water vapor pressure formula of HylandWexler, corrected by a GRUAN correction scheme. Relative humidity post processing radiation correction Bias correction applied to relative humidity by a GRUAN correction scheme (csv-lev only). Relative humidity random uncertainty % Statistical standard deviation (k=1) of relative humidity (csv-obs only). Relative humidity systematic uncertainty % Correlated uncertainty for relative humidity of systematic uncertainty sources estimated from calibration, calibration correction, radiation correction, time-lag (csv-lev only). Relative humidity total uncertainty % Standard uncertainty (k=1) of relative humidity (csv-obs only). Shortwave radiation W m-2 Short-wave radiation field (actinic flux) derived from a model for given sun elevation (mean between a cloudy and cloud-free case). Shortwave radiation total uncertainty W m-2 Standard uncertainty (k=1) of shortwave radiation (csv-obs only). Vertical speed of radiosonde m s-1 Speed at which the radiosonde rises. Water vapor volume mixing ratio mol mol-1 Volume mixing ratio (mol/mol) of water vapor calculated from relative humidity using the vapor pressure formula of HylandWexler and corrected by a GRUAN correction scheme. Wind from direction Degrees from north Meteorological wind direction is defined as the direction from which the wind originates. Wind from direction total uncertainty Degrees from north Standard uncertainty (k=1) of wind from direction (csv-obs only). Wind speed m s-1 Horizontal speed of the wind, or movement of air, at the height of the observation. Wind speed total uncertainty m s-1 Standard uncertainty (k=1) of wind speed (csv-obs only). MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Air temperature K Temperature in the atmosphere at the observed height. Temperature measured in Kelvin can be converted to degrees Celsius by subtracting 273.15. Its uncertainty is estimated with a GRUAN correction scheme. Air temperature K Temperature in the atmosphere at the observed height. Temperature measured in Kelvin can be converted to degrees Celsius by subtracting 273.15. Its uncertainty is estimated with a GRUAN correction scheme. Air temperature post processing radiation correction K Bias correction applied to air temperature by a GRUAN correction scheme (csv-lev only). Air temperature post processing radiation correction K Bias correction applied to air temperature by a GRUAN correction scheme (csv-lev only). Air temperature random uncertainty K Statistical standard deviation (k=1) of air temperature (csv-obs only). Air temperature random uncertainty K Statistical standard deviation (k=1) of air temperature (csv-obs only). Air temperature systematic uncertainty K Correlated uncertainty for air temperature of systematic uncertainty sources estimated from calibration, calibration correction, radiation correction, time-lag (csv-lev only). Air temperature systematic uncertainty K Correlated uncertainty for air temperature of systematic uncertainty sources estimated from calibration, calibration correction, radiation correction, time-lag (csv-lev only). Air temperature total uncertainty K Standard uncertainty (k=1) of air temperature (csv-obs only). Air temperature total uncertainty K Standard uncertainty (k=1) of air temperature (csv-obs only). Altitude m Geometric altitude above sea level calculated from air pressure and GNSS altitude. Altitude m Geometric altitude above sea level calculated from air pressure and GNSS altitude. Altitude total uncertainty m Standard uncertainty (k=1) of altitude (csv-obs only). Altitude total uncertainty m Standard uncertainty (k=1) of altitude (csv-obs only). Eastward wind component m s-1 Wind component towards the east. Eastward wind component m s-1 Wind component towards the east. Frost point temperature K Temperature, below 0° C, at which moisture in the air will condense as a layer of frost on any exposed surface. The frost point temperature is calculated from relative humidity using the water vapor pressure formula of HylandWexler, corrected by a GRUAN correction scheme. Frost point temperature K Temperature, below 0° C, at which moisture in the air will condense as a layer of frost on any exposed surface. The frost point temperature is calculated from relative humidity using the water vapor pressure formula of HylandWexler, corrected by a GRUAN correction scheme. Geopotential height m A measure of the height of a point in the atmosphere in relation to its potential energy. It is calculated by dividing the geopotential by the Earth's mean gravitational acceleration, g (=9.80665 m s-2 ). The geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. Geopotential is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. Geopotential height m A measure of the height of a point in the atmosphere in relation to its potential energy. It is calculated by dividing the geopotential by the Earth's mean gravitational acceleration, g (=9.80665 m s-2 ). The geopotential is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. Geopotential is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. Northward wind component m s-1 Wind component towards the north. Northward wind component m s-1 Wind component towards the north. Relative humidity % The water vapour pressure as a percentage of the value at which the air becomes saturated. For GRUAN data it is collated from U1 and U2 based on the water vapor pressure formula of HylandWexler, corrected by a GRUAN correction scheme. Relative humidity % The water vapour pressure as a percentage of the value at which the air becomes saturated. For GRUAN data it is collated from U1 and U2 based on the water vapor pressure formula of HylandWexler, corrected by a GRUAN correction scheme. Relative humidity effective vertical resolution % The water vapour pressure as a percentage of the value at which the air becomes saturated. For GRUAN data it is collated from U1 and U2 based on the water vapor pressure formula of HylandWexler, corrected by a GRUAN correction scheme. Relative humidity effective vertical resolution % The water vapour pressure as a percentage of the value at which the air becomes saturated. For GRUAN data it is collated from U1 and U2 based on the water vapor pressure formula of HylandWexler, corrected by a GRUAN correction scheme. Relative humidity post processing radiation correction Bias correction applied to relative humidity by a GRUAN correction scheme (csv-lev only). Relative humidity post processing radiation correction Bias correction applied to relative humidity by a GRUAN correction scheme (csv-lev only). Relative humidity random uncertainty % Statistical standard deviation (k=1) of relative humidity (csv-obs only). Relative humidity random uncertainty % Statistical standard deviation (k=1) of relative humidity (csv-obs only). Relative humidity systematic uncertainty % Correlated uncertainty for relative humidity of systematic uncertainty sources estimated from calibration, calibration correction, radiation correction, time-lag (csv-lev only). Relative humidity systematic uncertainty % Correlated uncertainty for relative humidity of systematic uncertainty sources estimated from calibration, calibration correction, radiation correction, time-lag (csv-lev only). Relative humidity total uncertainty % Standard uncertainty (k=1) of relative humidity (csv-obs only). Relative humidity total uncertainty % Standard uncertainty (k=1) of relative humidity (csv-obs only). Shortwave radiation W m-2 Short-wave radiation field (actinic flux) derived from a model for given sun elevation (mean between a cloudy and cloud-free case). Shortwave radiation W m-2 Short-wave radiation field (actinic flux) derived from a model for given sun elevation (mean between a cloudy and cloud-free case). Shortwave radiation total uncertainty W m-2 Standard uncertainty (k=1) of shortwave radiation (csv-obs only). Shortwave radiation total uncertainty W m-2 Standard uncertainty (k=1) of shortwave radiation (csv-obs only). Vertical speed of radiosonde m s-1 Speed at which the radiosonde rises. Vertical speed of radiosonde m s-1 Speed at which the radiosonde rises. Water vapor volume mixing ratio mol mol-1 Volume mixing ratio (mol/mol) of water vapor calculated from relative humidity using the vapor pressure formula of HylandWexler and corrected by a GRUAN correction scheme. Water vapor volume mixing ratio mol mol-1 Volume mixing ratio (mol/mol) of water vapor calculated from relative humidity using the vapor pressure formula of HylandWexler and corrected by a GRUAN correction scheme. Wind from direction Degrees from north Meteorological wind direction is defined as the direction from which the wind originates. Wind from direction Degrees from north Meteorological wind direction is defined as the direction from which the wind originates. Wind from direction total uncertainty Degrees from north Standard uncertainty (k=1) of wind from direction (csv-obs only). Wind from direction total uncertainty Degrees from north Standard uncertainty (k=1) of wind from direction (csv-obs only). Wind speed m s-1 Horizontal speed of the wind, or movement of air, at the height of the observation. Wind speed m s-1 Horizontal speed of the wind, or movement of air, at the height of the observation. Wind speed total uncertainty m s-1 Standard uncertainty (k=1) of wind speed (csv-obs only). Wind speed total uncertainty m s-1 Standard uncertainty (k=1) of wind speed (csv-obs only). RELATED VARIABLES Name Units Description Air pressure Pa Barometric air pressure using a silicon sensor up to 18.6 km, and derived from GPS-altitude above. Height of station above sea level m Altitude of the launch site. Latitude Degrees Observation latitude, (-90,90). Longitude Degrees Observation longitude (-180,180). Observation value According to the unit as specified in the main variables table. Measurement value of the variable in question (csv-obs only). Observed variable None Specification of the measurand (csv-obs only). Post processing radiation correction As for variable Bias correction applied to the variable in question by a GRUAN correction scheme (csv-obs only). Random uncertainty As for variable Statistical standard deviation (k=1) of the variable in question (csv-obs only). Record number None 6-digit integer GRUAN unique identifier for particular radiosonde ascent. Relative humidity effective vertical resolution s Resolution (defined by 1 / cut-off frequency) of the relative humidity in terms of time. Report time stamp None Date and time of observation specified as YYYY-MM-DD HH:MM:00+00. Station name None 3-character GRUAN station identifier. Systematic uncertainty As for variable Correlated uncertainty for variable in question of systematic uncertainty sources estimated from calibration, calibration correction, radiation correction, time-lag (csv-obs only). Time since launch s Time elapsed after the radiosounding launch. Total uncertainty As for variable Standard uncertainty (k=1) of the variable in question (csv-obs only). RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Air pressure Pa Barometric air pressure using a silicon sensor up to 18.6 km, and derived from GPS-altitude above. Air pressure Pa Barometric air pressure using a silicon sensor up to 18.6 km, and derived from GPS-altitude above. Height of station above sea level m Altitude of the launch site. Height of station above sea level m Altitude of the launch site. Latitude Degrees Observation latitude, (-90,90). Latitude Degrees Observation latitude, (-90,90). Longitude Degrees Observation longitude (-180,180). Longitude Degrees Observation longitude (-180,180). Observation value According to the unit as specified in the main variables table. Measurement value of the variable in question (csv-obs only). Observation value According to the unit as specified in the main variables table. Measurement value of the variable in question (csv-obs only). Observed variable None Specification of the measurand (csv-obs only). Observed variable None Specification of the measurand (csv-obs only). Post processing radiation correction As for variable Bias correction applied to the variable in question by a GRUAN correction scheme (csv-obs only). Post processing radiation correction As for variable Bias correction applied to the variable in question by a GRUAN correction scheme (csv-obs only). Random uncertainty As for variable Statistical standard deviation (k=1) of the variable in question (csv-obs only). Random uncertainty As for variable Statistical standard deviation (k=1) of the variable in question (csv-obs only). Record number None 6-digit integer GRUAN unique identifier for particular radiosonde ascent. Record number None 6-digit integer GRUAN unique identifier for particular radiosonde ascent. Relative humidity effective vertical resolution s Resolution (defined by 1 / cut-off frequency) of the relative humidity in terms of time. Relative humidity effective vertical resolution s Resolution (defined by 1 / cut-off frequency) of the relative humidity in terms of time. Report time stamp None Date and time of observation specified as YYYY-MM-DD HH:MM:00+00. Report time stamp None Date and time of observation specified as YYYY-MM-DD HH:MM:00+00. Station name None 3-character GRUAN station identifier. Station name None 3-character GRUAN station identifier. Systematic uncertainty As for variable Correlated uncertainty for variable in question of systematic uncertainty sources estimated from calibration, calibration correction, radiation correction, time-lag (csv-obs only). Systematic uncertainty As for variable Correlated uncertainty for variable in question of systematic uncertainty sources estimated from calibration, calibration correction, radiation correction, time-lag (csv-obs only). Time since launch s Time elapsed after the radiosounding launch. Time since launch s Time elapsed after the radiosounding launch. Total uncertainty As for variable Standard uncertainty (k=1) of the variable in question (csv-obs only). Total uncertainty As for variable Standard uncertainty (k=1) of the variable in question (csv-obs only). 529 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-water-mass-formation-rates-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_VAR_EXTREME_WMF_MEDSEA_area_averaged_mean Mediterranean Water Mass Formation Rates from Reanalysis DEFINITION The Mediterranean water mass formation rates are evaluated in 4 areas as defined in the Ocean State Report issue 2 section 3.4 (Simoncelli and Pinardi, 2018) as shown in Figure 2: (1) the Gulf of Lions for the Western Mediterranean Deep Waters (WMDW); (2) the Southern Adriatic Sea Pit for the Eastern Mediterranean Deep Waters (EMDW); (3) the Cretan Sea for Cretan Intermediate Waters (CIW) and Cretan Deep Waters (CDW); (4) the Rhodes Gyre, the area of formation of the so-called Levantine Intermediate Waters (LIW) and Levantine Deep Waters (LDW). Annual water mass formation rates have been computed using daily mixed layer depth estimates (density criteria Δσ = 0.01 kg/m3, 10 m reference level) considering the annual maximum volume of water above mixed layer depth with potential density within or higher the specific thresholds specified in Table 1 then divided by seconds per year. Annual mean values are provided using the Mediterranean 1/24o eddy resolving reanalysis (Escudier et al., 2020, 2021). CONTEXT The formation of intermediate and deep water masses is one of the most important processes occurring in the Mediterranean Sea, being a component of its general overturning circulation. This circulation varies at interannual and multidecadal time scales and it is composed of an upper zonal cell (Zonal Overturning Circulation) and two main meridional cells in the western and eastern Mediterranean (Pinardi and Masetti, 2000). The objective is to monitor the main water mass formation events using the eddy resolving Mediterranean Sea Reanalysis (Escudier et al., 2020, 2021) and considering Pinardi et al. (2015) and Simoncelli and Pinardi (2018) as references for the methodology. The Mediterranean Sea Reanalysis can reproduce both Eastern Mediterranean Transient and Western Mediterranean Transition phenomena and catches the principal water mass formation events reported in the literature. This will permit constant monitoring of the open ocean deep convection process in the Mediterranean Sea and a better understanding of the multiple drivers of the general overturning circulation at interannual and multidecadal time scales. Deep and intermediate water formation events reveal themselves by a deep mixed layer depth distribution in four Mediterranean areas (Table 1 and Figure 2): Gulf of Lions, Southern Adriatic Sea Pit, Cretan Sea and Rhodes Gyre. CMEMS KEY FINDINGS The Western Mediterranean Deep Water (WMDW) formation events in the Gulf of Lion appear to be larger after 1999 consistently with Schroeder et al. (2006, 2008) related to the Eastern Mediterranean Transient event. This modification of WMDW after 2005 has been called Western Mediterranean Transition. WMDW formation events are consistent with Somot et al. (2016) and the event in 2009 is also reported in Houpert et al. (2016). The Eastern Mediterranean Deep Water (EMDW) formation in the Southern Adriatic Pit region displays a period of water mass formation between 1988 and 1993, in agreement with Pinardi et al. (2015), in 1996, 1999 and 2000 as documented by Manca et al. (2002). Weak deep water formation in winter 2006 is confirmed by observations in Vilibić and Šantić (2008). An intense deep water formation event is detected in 2012-2013 (Gačić et al., 2014). Last years are characterized by large events starting from 2017 (Mihanovic et al., 2021). Cretan Intermediate Water formation rates present larger peaks between 1989 and 1993 with the ones in 1992 and 1993 composing the Eastern Mediterranean Transient phenomena. The Cretan Deep Water formed in 1992 and 1993 is characterized by the highest densities of the entire period in accordance with Velaoras et al. (2014). The Levantine Deep Water formation rate in the Rhode Gyre region presents the largest values between 1992 and 1993 in agreement with Kontoyiannis et al. (1999). DOI (product):https://doi.org/10.48670/mds-00318 https://doi.org/10.48670/mds-00318 530 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/fraction-absorbed-photosynthetically-active-radiation-0 http://land.copernicus.eu/global/products/fapar Fraction of Absorbed Photosynthetically Active Radiation 1999-2020 (raster 1 km), global, 10-daily - version 1 The FAPAR quantifies the fraction of the solar radiation absorbed by plants for photosynthesis. It refers only to the green and living elements of the canopy. The FAPAR depends on the canopy structure, vegetation element optical properties, atmospheric conditions and angular configuration. To overcome this latter dependency, a daily integrated FAPAR value is assessed. FAPAR is very useful as input to a number of primary productivity models and is recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). 531 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/start-season-value-2017-present-raster-10-m-europe-yearly https://www.wekeo.eu/data?view=viewer&t=1577905116279&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1QSEVOT0xPR1ktQU5ELVBST0RVQ1RJVklUWS1QQVJBTUVURVJTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVlBQX1NPU1ZfU0VBU09OMV8xME0iLCJ6SW5kZXgiOjgwfV0%3D&initial=1 Start-of-season Value 2017-present (raster 10 m), Europe, yearly, Sept. 2021 The Start-of-Season Value (SOSV), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Start-of-Season Value (SOSV) provides the value of the Plant Phenology Index (PPI) at the start of the vegetation growing season. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Start-of-Season Value is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-m… https://land.copernicus.eu/user-corner/technical-library/product-user-m… A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The SOSV dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year. 532 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-chlorophyll-trend-map-observations-reprocessing http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_trend Black Sea Chlorophyll-a trend map from Observations Reprocessing DEFINITION This product includes the Black Sea satellite chlorophyll trend map based on regional chlorophyll reprocessed (MY) product as distributed by CMEMS OC-TAC. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3A-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of two different regional algorithms developed with the BiOMaP data set (Zibordi et al., 2011): a band-ratio algorithm (B/R) (Zibordi et al., 2015) and a Multilayer Perceptron (MLP) neural net algorithm based on Rrs values at three individual wavelengths (490, 510 and 555 nm) (Kajiyama et al., 2018). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2021). The trend map is obtained by applying Colella et al. (2016) methodology, where the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens’s method (Sen, 1968) are applied on deseasonalized monthly time series, as obtained from the X-11 technique (see e. g. Pezzulli et al. 2005), to estimate, trend magnitude and its significance. The trend is expressed in % per year that represents the relative changes (i.e., percentage) corresponding to the dimensional trend [mg m-3 y-1] with respect to the reference climatology (1997-2014). Only significant trends (p < 0.05) are included. CONTEXT Phytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration - as a proxy for phytoplankton - respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Black Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) (Oguz et al .2003). Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series. CMEMS KEY FINDINGS The average Black Sea trend for the 1997-2021 period is about -1.4% per year. The trend is negative overall the basin, with weaker values in the central area, up to no significant trend percentages. The western side of the basin highlights markable negative trend. Negative values are shown in the Azov Sea with a strong inversion offshore the Don River. The overall negative trend in the map is in accordance with the results of Bengil and Mavruk (2018), that revealed a decreasing trend of chlorophyll during the post-eutrophication phase in the years 1997-2017. DOI (product):https://doi.org/10.48670/moi-00212 https://doi.org/10.48670/moi-00212 533 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-era5-pressure-levels-monthly-means-preliminary https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly-means-preliminary-back-extension reanalysis-era5-pressure-levels-monthly-means-preliminary-back-extension This entry is a preliminary version of the ERA5 reanalysis back extension from 1950 to 1978. It has now been superseded by the ERA5 Climate Data Store entries from 1940 onwards and will be deprecated in due course. Therefore, users are advised to use the latter, final release, instead. Although in many other respects the quality of this dataset is quite satisfactory (Bell et al., 2021), this preliminary data does suffer from tropical cyclones that are sometimes unrealistically intense. This is in contrast with the ERA5 product from 1959 onwards. For more details see the articles, ERA5 back extension 1950-1978 (Preliminary version): tropical cyclones are too intense and Changes in the ERA5 back extension compared to its preliminary version. (Bell et al., 2021) ERA5 back extension 1950-1978 (Preliminary version): tropical cyclones are too intense Changes in the ERA5 back extension compared to its preliminary version ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Currently, data is available from 1940, with superseded Climate Data Store entries for 1950-1978 (preliminary back extension, this page) and from 1940 onwards (final release plus timely updates). ERA5 replaces the ERA-Interim reanalysis. ERA5 Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. this article these guidelines Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly averaged data on pressure levels from 1950 to 1978 (preliminary version)". DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution Reanalysis: 0.25°x0.25° Ensemble members: 0.5°x0.5° Vertical coverage 1000 hPa to 1 hPa Vertical resolution 37 pressure levels Temporal coverage 1950 to 1978 Temporal resolution Monthly File format GRIB DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution Reanalysis: 0.25°x0.25° Ensemble members: 0.5°x0.5° Horizontal resolution Reanalysis: 0.25°x0.25° Ensemble members: 0.5°x0.5° Vertical coverage 1000 hPa to 1 hPa Vertical coverage 1000 hPa to 1 hPa Vertical resolution 37 pressure levels Vertical resolution 37 pressure levels Temporal coverage 1950 to 1978 Temporal coverage 1950 to 1978 Temporal resolution Monthly Temporal resolution Monthly File format GRIB File format GRIB MAIN VARIABLES Name Units Description Divergence s-1 This parameter is the horizontal divergence of velocity. It is the rate at which air is spreading out horizontally from a point, per square metre. This parameter is positive for air that is spreading out, or diverging, and negative for the opposite, for air that is concentrating, or converging (convergence). Fraction of cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud (liquid or ice) and varies between zero and one. This parameter is available on multiple levels through the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography. Ozone mass mixing ratio kg kg-1 This parameter is the mass of ozone per kilogram of air. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. Most of the IFS chemical species are archived as mass mixing ratios [kg kg-1]. Potential vorticity K m2 kg-1 s-1 Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere. Relative humidity % This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function. Specific cloud ice water content kg kg-1 This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'. Specific cloud liquid water content kg kg-1 This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific humidity kg kg-1 This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. Specific rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Temperature K This parameter is the temperature in the atmosphere. It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere. U-component of wind m s-1 This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east. A negative sign indicates air moving towards the west. V-component of wind m s-1 This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north. A negative sign indicates air moving towards the south. Vertical velocity Pa s-1 This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion. Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values). Vorticity (relative) s-1 This parameter is a measure of the rotation of air in the horizontal, around a vertical axis, relative to a fixed point on the surface of the Earth. On the scale of weather systems, troughs (weather features that can include rain) are associated with anticlockwise rotation (in the northern hemisphere), and ridges (weather features that bring light or still winds) are associated with clockwise rotation. Adding the effect of rotation of the Earth, the Coriolis parameter, to the relative vorticity produces the absolute vorticity. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Divergence s-1 This parameter is the horizontal divergence of velocity. It is the rate at which air is spreading out horizontally from a point, per square metre. This parameter is positive for air that is spreading out, or diverging, and negative for the opposite, for air that is concentrating, or converging (convergence). Divergence s-1 This parameter is the horizontal divergence of velocity. It is the rate at which air is spreading out horizontally from a point, per square metre. This parameter is positive for air that is spreading out, or diverging, and negative for the opposite, for air that is concentrating, or converging (convergence). Fraction of cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud (liquid or ice) and varies between zero and one. This parameter is available on multiple levels through the atmosphere. Fraction of cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud (liquid or ice) and varies between zero and one. This parameter is available on multiple levels through the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography. Ozone mass mixing ratio kg kg-1 This parameter is the mass of ozone per kilogram of air. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. Most of the IFS chemical species are archived as mass mixing ratios [kg kg-1]. Ozone mass mixing ratio kg kg-1 This parameter is the mass of ozone per kilogram of air. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. Most of the IFS chemical species are archived as mass mixing ratios [kg kg-1]. Potential vorticity K m2 kg-1 s-1 Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere. Potential vorticity K m2 kg-1 s-1 Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere. Relative humidity % This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function. Relative humidity % This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function. Specific cloud ice water content kg kg-1 This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'. Specific cloud ice water content kg kg-1 This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'. Specific cloud liquid water content kg kg-1 This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud liquid water content kg kg-1 This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific humidity kg kg-1 This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. Specific humidity kg kg-1 This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. Specific rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Temperature K This parameter is the temperature in the atmosphere. It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere. Temperature K This parameter is the temperature in the atmosphere. It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere. U-component of wind m s-1 This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east. A negative sign indicates air moving towards the west. U-component of wind m s-1 This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east. A negative sign indicates air moving towards the west. V-component of wind m s-1 This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north. A negative sign indicates air moving towards the south. V-component of wind m s-1 This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north. A negative sign indicates air moving towards the south. Vertical velocity Pa s-1 This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion. Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values). Vertical velocity Pa s-1 This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion. Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values). Vorticity (relative) s-1 This parameter is a measure of the rotation of air in the horizontal, around a vertical axis, relative to a fixed point on the surface of the Earth. On the scale of weather systems, troughs (weather features that can include rain) are associated with anticlockwise rotation (in the northern hemisphere), and ridges (weather features that bring light or still winds) are associated with clockwise rotation. Adding the effect of rotation of the Earth, the Coriolis parameter, to the relative vorticity produces the absolute vorticity. Vorticity (relative) s-1 This parameter is a measure of the rotation of air in the horizontal, around a vertical axis, relative to a fixed point on the surface of the Earth. On the scale of weather systems, troughs (weather features that can include rain) are associated with anticlockwise rotation (in the northern hemisphere), and ridges (weather features that bring light or still winds) are associated with clockwise rotation. Adding the effect of rotation of the Earth, the Coriolis parameter, to the relative vorticity produces the absolute vorticity. 534 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/high-resolution-snow-and-ice-monitoring-river-and-lake https://cryo.land.copernicus.eu/finder/ High Resolution Snow and Ice Monitoring: River and Lake Ice Extent (raster 20m) The Copernicus River and Lake Ice Extent (RLIE) products provide pixel-based information about ice presence on rivers and lakes. There are several RLIE products available for the entire EEA38 and the United Kingdom, depending on their data source. RLIE S1 and RLIE S2 are generated in near real-time based on observations from the Sentinel-1 and the Sentinel-2 constellations respectively while RLIE S1+S2 is a delayed-time product derived from the previous products - an RLIE S2 for a given day is enriched with RLIE S1 of the same day. All RLIE products are distributed in raster files covering an area of 110 km by 110 km with a pixel size of 60 m by 60 m in UTM/WGS84 projection, which corresponds to the Sentinel-2 L1C product tile. They inform of the presence of snow-covered or snow-free ice on the various water bodies described by the EU-HYDRO river and lake network database. Each product is composed of three separate files corresponding to the different layers of the product, and another metadata file. The RLIE products are part of the products of the pan-European High-Resolution Snow & Ice service (HR-S&I), which are provided at high spatial resolution (20 m x 20 m and 60 m x 60 m), from the Sentinel-2 and Sentinel-1 constellations data from September 1, 2016 onwards. Visit https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… to get more information on the different HR-S&I products (Snow products : FSC, WDS, SWS, GFSC, and PSA. Ice products : RLIE and ARLIE). https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… 535 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/fraction-absorbed-photosynthetically-active-radiation http://land.copernicus.eu/global/products/fapar Fraction of Absorbed Photosynthetically Active Radiation 2014-present (raster 300 m), global, 10-daily - version 1 The FAPAR quantifies the fraction of the solar radiation absorbed by plants for photosynthesis. It refers only to the green and living elements of the canopy. The FAPAR depends on the canopy structure, vegetation element optical properties, atmospheric conditions and angular configuration. To overcome this latter dependency, a daily integrated FAPAR value is assessed. FAPAR is very useful as input to a number of primary productivity models and is recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). The product at 333m resolution is provided in Near Real Time and consolidated in the next six periods. 536 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/soil-water-index-static-layers-raster-125-km-global https://land.copernicus.eu/global/access Soil Water Index Static Layers (raster 12.5 km), global - version 3 The Soil Water Index (SWI) Static Layer collection provides static masks and ancillary files that aid in the analysis of the daily SWI, 10-daily SWI or SWI Time Series products. These include (i) a DGG file to find Discrete Global Grid cell corresponding to a given location, (ii) a tropical Forest Mask (FM), (iii) a Topographic Complexity (TC) mask, a (iv) Wetland Fraction (WF) mask and (iv) Correlation Information (CI) that shows the correlation with Global Land Data Assimilation (GLDAS) models. 537 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-european-north-west-shelf-ocean-wave-analysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014 Atlantic - European North West Shelf - Ocean Wave Analysis and Forecast Short description: This product provides analysis-forecast outputs from a wave model for the North-West European Shelf coupled with an ocean model. The wave model is WAVEWATCH III and the North-West Shelf configuration is based on a two-tier Spherical Multiple Cell grid mesh (3 and 1.5 km cells) derived from with the 1.5km grid used for [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013]. The model is forced by lateral boundary conditions from the Met Office Global wave forecast model. The atmospheric forcing is given by the operational ECMWF Numerical Weather Prediction model and surface current forcing is provided by the North-West Shelf ocean physics analysis and forecast model described in [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013]. Model outputs comprise wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state and wind-sea and swell components. The data are delivered on a regular grid at approximately 1.5km resolution, consistent with the physical ocean product. See [http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-0… CMEMS-NWS-PUM-004-013_014] for more information. Further details of the model, including source term physics, propagation schemes, forcing and boundary conditions, and validation, are provided in the [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004… CMEMS-NWS-QUID-004-014]. https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-0… http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004… Associated products: The analysis-forecast product from the ocean physics model is: [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013]. https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… DOI (product) :https://doi.org/10.48670/moi-00055 https://doi.org/10.48670/moi-00055 538 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-ice-extent-observations-reprocessing http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTIC_OMI_SI_extent Baltic Sea Ice Extent from Observations Reprocessing DEFINITION Sea ice extent is defined as the area covered by sea ice, that is the area of ocean having more than 15% sea ice concentration. Sea ice concentration is the fractional coverage of an ocean area covered with sea ice. Daily sea ice extent values are computed from the daily sea ice concentration maps. All sea ice covered Baltic Sea is included, except for lake ice. The data used to produce the charts are Synthetic Aperture Radar images as well as in situ observations from ice breakers (Uiboupin et al., 2010). The annual course of the sea ice extent has been calculated as daily mean ice extent for each day-of-year over the period October 1992 – September 2014. Weekly smoothed time series of the sea ice extent have been calculated from daily values using a 7-day moving average filter. CONTEXT Sea ice coverage has a vital role in the annual course of physical and ecological conditions in the Baltic Sea. Moreover, it is an important parameter for safe winter navigation. The presence of sea ice cover sets special requirements for navigation, both for the construction of the ships and their behaviour in ice, as in many cases, merchant ships need icebreaker assistance. Temporal trends of the sea ice extent could be a valuable indicator of the climate change signal in the Baltic Sea region. It has been estimated that a 1 °C increase in the average air temperature results in the retreat of ice-covered area in the Baltic Sea about 45,000 km2 (Granskog et al., 2006). Decrease in maximum ice extent may influence vertical stratification of the Baltic Sea (Hordoir and Meier, 2012) and affect the onset of the spring bloom (Eilola et al., 2013). In addition, statistical sea ice coverage information is crucial for planning of coastal and offshore construction. Therefore, the knowledge about ice conditions and their variability is required and monitored in Copernicus Marine Service. CMEMS KEY FINDINGS Sea ice coverage in the Baltic Sea is strongly seasonal. In general, sea ice starts to form in October and may last until June. The ice season 2020/21 had the moderate maximum ice extent in the Baltic Sea. Sea ice extent reached a maximum area of about 121 000 km2. The seasonal cycle of the sea ice extent showed strong asymmetry towards an earlier period of the ice season. The ice extent increased rapidly during the first half of February followed by a fast decrease of the sea ice extent during the second half of February. Maximum sea ice extent in 2020/21 reached the level of mean maximum sea ice extent. On average, maximum sea ice extent is reached at the beginning of March, but in 2021 maximum sea ice extent was observed in mid February. In a case of fully ice covered Baltic Sea the maximum ice extent is 422 000 km2, which was last observed during the 1940s (Vihma and Haapala, 2009). Thus, 29% of the Baltic Sea was covered by ice in 2020/21. Although there is a tendency of decreasing sea ice extent in the Baltic Sea over the period 1993-2021, the linear trend is not statistically significant. DOI (product):https://doi.org/10.48670/moi-00200 https://doi.org/10.48670/moi-00200 539 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-situ-near-real-time-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_ARC_PHYBGCWAV_DISCRETE_MYNRT_013_031 Arctic Ocean- In Situ Near Real Time Observations Short description: Arctic Oceans - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average DOI (product) :https://doi.org/10.48670/moi-00031 https://doi.org/10.48670/moi-00031 540 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-hydrology-variables-derived-seasonal-forecast https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-hydrology-variables-derived-seasonal-forecast sis-hydrology-variables-derived-seasonal-forecast This dataset provides hydrological seasonal forecasts of monthly mean river discharge across Europe. Two hydrological model ensembles are provided. The first is an E-HYPE multi-model system comprising eight model realisations using a catchment-based resolution. The second comprises the E-HYPEgrid, VIC-WUR and EFAS (LISFLOOD) hydrological models at a 5km gridded resolution. The initialisation of the hydrological seasonal forecast uses the European Flood Awareness System (EFAS) daily gridded meteorological observations (EFAS-Meteo) up until the start of the forecast, and the subsequent integration of the meteorological seasonal forecasts using all 51 members of the ECMWF seasonal forecast system 5 (SEAS5) meteorological forecasts. A bias adjustment step using quantile mapping for temperature and precipitation was used for the E-HYPE and ViC-WUR models to minimize drift in the forecasts caused by biases in SEAS5 compared to EFAS-Meteo. The final output is in the form of monthly mean river discharge for the coming seven months. The context of the forecasts is provided by upper and lower terciles of the historical EFAS-Meteo driven simulation for each month of the year. This dataset is produced by the Swedish Meteorological and Hydrological Institute on behalf of the Copernicus Climate Change Service. The operational production is performed by C3S in collaboration with Copernicus Emergency Management Service (CEMS). DATA DESCRIPTION Data type Gridded and catchments Projection Lambert azimuthal equal area grid for the 5km grid Horizontal coverage Europe Horizontal resolution 5km grid and catchments Vertical coverage Surface Vertical resolution Single level Temporal coverage January 2021 to present with a 7 month lead-time Temporal resolution Monthly Temporal gaps No gaps File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v.1.6 Versions The current version of the multimodel dataset is v1.0. The EFAS forecasts used are v4 released 2020-10-14. For more information on versions we refer to the documentation. Update frequency Monthly DATA DESCRIPTION DATA DESCRIPTION Data type Gridded and catchments Data type Gridded and catchments Projection Lambert azimuthal equal area grid for the 5km grid Projection Lambert azimuthal equal area grid for the 5km grid Horizontal coverage Europe Horizontal coverage Europe Horizontal resolution 5km grid and catchments Horizontal resolution 5km grid and catchments Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage January 2021 to present with a 7 month lead-time Temporal coverage January 2021 to present with a 7 month lead-time Temporal resolution Monthly Temporal resolution Monthly Temporal gaps No gaps Temporal gaps No gaps File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v.1.6 Conventions Climate and Forecast (CF) Metadata Convention v.1.6 Versions The current version of the multimodel dataset is v1.0. The EFAS forecasts used are v4 released 2020-10-14. For more information on versions we refer to the documentation. Versions The current version of the multimodel dataset is v1.0. The EFAS forecasts used are v4 released 2020-10-14. For more information on versions we refer to the documentation. Update frequency Monthly Update frequency Monthly MAIN VARIABLES Name Units Description Brier skill score above normal conditions Dimensionless Monthly skill metrics as fairBSSan (fair Brier skill score above the 66 percentile) against a climate reference over the reference period 1993-2016. The Brier skill score is a strictly proper score function that measures the accuracy of probabilistic forecasts. For more information on how the Brier skill score was calculated we refer to the documentation. Brier skill score below normal conditions Dimensionless Monthly skill metrics as fairBSSbn (fair Brier skill score below the 33 percentile) against a climate reference over the reference period 1993-2016. The Brier skill score is a strictly proper score function that measures the accuracy of probabilistic forecasts. For more information on how the Brier skill score was calculated we refer to the documentation. Continuous ranked probability skill score Dimensionless Monthly skill metrics as fairCRPSS (fair continuous ranked probability skill score) against a climate reference over the reference period 1993-2016. The fairCRPSS skill score is a proper score function that measures the performance of probabilistic forecasts. For more information on how the fairCRPSS was calculated we refer to the documentation. Fair ranked probability skill score Dimensionless Monthly skill metrics as fairRPSS (fair ranked probability skill score) against a climate reference over the reference period 1993-2016. The fairRPSS skill score is a proper score function that measures the performance of probabilistic forecasts. For more information on how the fairRPSS was calculated we refer to the documentation. Reference river discharge lower tercile m3 s-1 The lower tercile of the river discharge for the reference period. Reference river discharge upper tercile m3 s-1 The upper tercile of the river discharge for the reference period. River discharge m3 s-1 Volume rate of water flow, including sediments, chemical and biological material in the river channel averaged over a time step through a cross-section. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Brier skill score above normal conditions Dimensionless Monthly skill metrics as fairBSSan (fair Brier skill score above the 66 percentile) against a climate reference over the reference period 1993-2016. The Brier skill score is a strictly proper score function that measures the accuracy of probabilistic forecasts. For more information on how the Brier skill score was calculated we refer to the documentation. Brier skill score above normal conditions Dimensionless Monthly skill metrics as fairBSSan (fair Brier skill score above the 66 percentile) against a climate reference over the reference period 1993-2016. The Brier skill score is a strictly proper score function that measures the accuracy of probabilistic forecasts. For more information on how the Brier skill score was calculated we refer to the documentation. Brier skill score below normal conditions Dimensionless Monthly skill metrics as fairBSSbn (fair Brier skill score below the 33 percentile) against a climate reference over the reference period 1993-2016. The Brier skill score is a strictly proper score function that measures the accuracy of probabilistic forecasts. For more information on how the Brier skill score was calculated we refer to the documentation. Brier skill score below normal conditions Dimensionless Monthly skill metrics as fairBSSbn (fair Brier skill score below the 33 percentile) against a climate reference over the reference period 1993-2016. The Brier skill score is a strictly proper score function that measures the accuracy of probabilistic forecasts. For more information on how the Brier skill score was calculated we refer to the documentation. Continuous ranked probability skill score Dimensionless Monthly skill metrics as fairCRPSS (fair continuous ranked probability skill score) against a climate reference over the reference period 1993-2016. The fairCRPSS skill score is a proper score function that measures the performance of probabilistic forecasts. For more information on how the fairCRPSS was calculated we refer to the documentation. Continuous ranked probability skill score Dimensionless Monthly skill metrics as fairCRPSS (fair continuous ranked probability skill score) against a climate reference over the reference period 1993-2016. The fairCRPSS skill score is a proper score function that measures the performance of probabilistic forecasts. For more information on how the fairCRPSS was calculated we refer to the documentation. Fair ranked probability skill score Dimensionless Monthly skill metrics as fairRPSS (fair ranked probability skill score) against a climate reference over the reference period 1993-2016. The fairRPSS skill score is a proper score function that measures the performance of probabilistic forecasts. For more information on how the fairRPSS was calculated we refer to the documentation. Fair ranked probability skill score Dimensionless Monthly skill metrics as fairRPSS (fair ranked probability skill score) against a climate reference over the reference period 1993-2016. The fairRPSS skill score is a proper score function that measures the performance of probabilistic forecasts. For more information on how the fairRPSS was calculated we refer to the documentation. Reference river discharge lower tercile m3 s-1 The lower tercile of the river discharge for the reference period. Reference river discharge lower tercile m3 s-1 The lower tercile of the river discharge for the reference period. Reference river discharge upper tercile m3 s-1 The upper tercile of the river discharge for the reference period. Reference river discharge upper tercile m3 s-1 The upper tercile of the river discharge for the reference period. River discharge m3 s-1 Volume rate of water flow, including sediments, chemical and biological material in the river channel averaged over a time step through a cross-section. River discharge m3 s-1 Volume rate of water flow, including sediments, chemical and biological material in the river channel averaged over a time step through a cross-section. 541 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/medium-resolution-vegetation-phenology-and-productivity-1 https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum_d1-0.pdf Medium Resolution Vegetation Phenology and Productivity: Seasonal amplitude (raster 500m), Oct. 2022 The seasonal amplitude, one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European Medium Resolution Vegetation Phenology and Productivity (MR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The seasonal amplitude is the difference between the maximum and minimum Plant Phenology Index (PPI) values reached during the season. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The seasonal amplitude) is one of the 13 parameters. The full list is available in the Product User Manual: https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum… The seasonal amplitude time series dataset is made available as raster files with 500x 500m resolution, in ETRS89-LAEA projection corresponding to the MCD43 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2000 onwards. It is updated in the first quarter of each year. The full on-line access to open and free data for this resource will be made available by the end of 2022. Until then the data will be made available 'on-demand' by filling in the form at: https://land.copernicus.eu/contact-form https://land.copernicus.eu/contact-form 542 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/multi-observation-global-ocean-3d-temperature-salinity http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD Short description: You can find here the Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year reprocessing. It consists of 3D Temperature, Salinity, Heights, Geostrophic Currents and Mixed Layer Depth, available on a 1/4 degree regular grid and on 50 depth levels from the surface down to the bottom. The product includes 4 datasets: * dataset-armor-3d-nrt-weekly, which delivers near-real-time (NRT) weekly data * dataset-armor-3d-nrt-monthly, which delivers near-real-time (NRT) monthly data * dataset-armor-3d-rep-weekly, which delivers multi-year reprocessed (REP) weekly data * dataset-armor-3d-rep-monthly, which delivers multi-year reprocessed (REP) monthly data DOI (product) :https://doi.org/10.48670/moi-00052 https://doi.org/10.48670/moi-00052 Product Citation: Please refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag…. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… 543 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-fisheries-eutrophication https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-fisheries-eutrophication sis-fisheries-eutrophication The dataset includes ocean eutrophication indicators derived from climate projections and ocean colour remote-sensing satellite products. Eutrophication refers to the excessive nutrient input into water bodies that lead to increased primary production, algal blooms, and low-oxygenated waters, consequently causing a reduction to marine life and habitat degradation. These indicators are useful to the long-term planning and monitoring of water quality since coastal areas of Europe are subject to the adverse effects of eutrophication, i.e. the increased occurrence of harmful algal blooms. Ocean eutrophication may be determined by measuring the concentration of chlorophyll-a, a measure of the amount of primary production in the water column. This may be modelled using biogeochemical models or observed by its green colour in the ocean using satellite remote sensing. In this dataset, the indicators are derived from the Copernicus Marine Environment Monitoring Service (CMEMS) ocean colour satellite product for 2006-2016 and climate projections. To produce the climate projections, two marine hydrodynamic-biogeochemical models forced with climate simulations were used: the NEMO-ERSEM model run on a domain for the Northwest European Shelf, and the POLCOM-ERSEM model run on a domain covering the Northwest European Shelf and Mediterranean Sea. The indicators are based on comparisons between daily chlorophyll-a concentration values and the daily 90th percentile (P90) chlorophyll-a concentration from climatology. The P90 of chlorophyll-a concentration can itself be used to detect abnormal levels of chlorophyll-a concentration in an ecosystem and has been adopted as a measure of eutrophication in coastal waters by the Convention for the Protection of the Marine Environment of the Northeast Atlantic 2008 (OSPAR). It is considered to be a robust ecological indicator of trophic ecological status. This is developed upon in this dataset and the measure of eutrophication used is the proportion of days in each month which have unusually high chlorophyll values (greater than that day’s climatological P90 value). It has dimensionless units and its expected value in the absence of changes in eutrophication state is 0.1. The main eutrophication indicator in this dataset is the difference between this proportion and 0.1 and is referred to as the chlorophyll-a anomaly indicator. Additional derived indicators are also included that further measure changes in eutrophication status over different periods. For instance, we could ask whether a location has become generally more eutrophic up to August 2000, or whether Augusts have become more eutrophic up to 2000. The algorithms and metrics for eutrophication were developed and applied by Plymouth Marine Laboratory (UK). In order to assess the impact of climate change, simulations under two future climate scenarios based on different Representative Concentration Pathways (RCP) for future greenhouse gas concentration are conducted: the intermediate scenario, RCP4.5, in which greenhouse gas concentration peak around 2040 before declining mainly due to successful mitigation measures in place; and the more pessimistic scenario, RCP8.5, where greenhouse gas concentration continue to rise throughout the century. The hydrodynamic-biogeochemical models were driven by one Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate model (GCM) projection downscaled to a regional climate model (RCM). Note that by using only one of the many possible combinations of GCM-RCM pairs leads to an incomplete estimate of the true uncertainty in the outcome in a changing climate by, most likely, indicating a smaller spread of outcomes than if the estimate were based on a larger ensemble of such GCM-RCM combinations. This does not necessarily mean a reduction in the true uncertainty, but simply an incomplete estimate of it. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) CMEMS satellite product: Northwest European Shelf and Mediterranean Sea (46W to 37E, 11N to 65N) Horizontal resolution POLCOMS-ERSEM: 0.1° x 0.1° (approx. 11km) NEMO-ERSEM: 0.06° x 0.06° (approx. 7km) CMEMS satellite product: 1km x 1km Vertical coverage Single level Vertical resolution Variables are provided on a single level which may differ among variables Temporal coverage POLCOMS-ERSEM: 2006 up to 2100 NEMO-ERSEM: 2006 to 2049 CMEMS satellite product: 2006 – 2016 Temporal resolution Monthly File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.4 Versions v1.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) CMEMS satellite product: Northwest European Shelf and Mediterranean Sea (46W to 37E, 11N to 65N) Horizontal coverage POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) CMEMS satellite product: Northwest European Shelf and Mediterranean Sea (46W to 37E, 11N to 65N) POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) CMEMS satellite product: Northwest European Shelf and Mediterranean Sea (46W to 37E, 11N to 65N) Horizontal resolution POLCOMS-ERSEM: 0.1° x 0.1° (approx. 11km) NEMO-ERSEM: 0.06° x 0.06° (approx. 7km) CMEMS satellite product: 1km x 1km Horizontal resolution POLCOMS-ERSEM: 0.1° x 0.1° (approx. 11km) NEMO-ERSEM: 0.06° x 0.06° (approx. 7km) CMEMS satellite product: 1km x 1km POLCOMS-ERSEM: 0.1° x 0.1° (approx. 11km) NEMO-ERSEM: 0.06° x 0.06° (approx. 7km) CMEMS satellite product: 1km x 1km Vertical coverage Single level Vertical coverage Single level Vertical resolution Variables are provided on a single level which may differ among variables Vertical resolution Variables are provided on a single level which may differ among variables Temporal coverage POLCOMS-ERSEM: 2006 up to 2100 NEMO-ERSEM: 2006 to 2049 CMEMS satellite product: 2006 – 2016 Temporal coverage POLCOMS-ERSEM: 2006 up to 2100 NEMO-ERSEM: 2006 to 2049 CMEMS satellite product: 2006 – 2016 POLCOMS-ERSEM: 2006 up to 2100 NEMO-ERSEM: 2006 to 2049 CMEMS satellite product: 2006 – 2016 Temporal resolution Monthly Temporal resolution Monthly File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.4 Conventions Climate and Forecast (CF) Metadata Convention v1.4 Versions v1.0 Versions v1.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Chlorophyll-a anomaly dimensionless The anomaly is a measure of the extent to which the number of unusually high daily chlorophyll-a values is greater or less than expected. Unusually high is defined as exceeding P90 value for that day. At a given position, if the anomaly is zero and equal to the reference period we would expect the number of times the daily chlorophyll-a value exceeds P90 over a month to be 10% (0.1) of the total number of chlorophyll measurements. The monthly anomaly is therefore the difference between the actual proportion and 0.1, hence has a possible range of -0.1 (proportion=0) to 0.9 (proportion=1). Positive anomaly means we encounter more unusually high chlorophyll values than expected, negative anomaly means we encounter less. Chlorophyll-a anomaly P-value dimensionless The two-tailed P-value of the mean annual rate of change in chlorophyll-a anomaly. Low P-values indicate that the results are unlikely to have arisen by chance, and so we can be more confident that the trend is real. Standard practice is to treat P-values less than 0.05 as significant. Chlorophyll-a anomaly gradient year-1 The mean annual rate of change of the chlorophyll-a anomaly from the start of the time series to the given date, determined through simple linear regression. These data are available either as a 'standard' time series where data is presented with a monthly resolution, progressing January, February etc. through the year, or split out on a calendar month basis where only data for a named month is returned. This monthly data is useful for comparing year on year changes for a specific month or season. Cumulative chlorophyll-a anomaly dimensionless The cumulative sum of monthly anomalies from the start of the time series to the given date. This is a measure of consistent behaviour in the anomaly, e.g. if the anomaly is highly variable but slightly greater than zero on average, this might be hard to detect in the anomaly or slope data, but the cumulative anomaly will gradually increase. This can either be calculated using every month in the time series, or using only the calendar month corresponding to the date, e.g. the sum to August 2016 would use only August anomalies. For the data stored by month this is the cumulative difference between the monthly proportion exceeding climatological P90 and 0.1 in the given calendar month. Standard error of the cumulative chlorophyll-a anomaly dimensionless This is a measure of the statistical uncertainty of the cumulative anomaly. For instance, a cumulative anomaly of 1 would suggest a consistent anomaly greater than zero if its standard error were 0.1, but would be attributable to chance if it were 10. For the data stored by month this is the standard error of the cumulative difference in proportion in the given calendar month. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Chlorophyll-a anomaly dimensionless The anomaly is a measure of the extent to which the number of unusually high daily chlorophyll-a values is greater or less than expected. Unusually high is defined as exceeding P90 value for that day. At a given position, if the anomaly is zero and equal to the reference period we would expect the number of times the daily chlorophyll-a value exceeds P90 over a month to be 10% (0.1) of the total number of chlorophyll measurements. The monthly anomaly is therefore the difference between the actual proportion and 0.1, hence has a possible range of -0.1 (proportion=0) to 0.9 (proportion=1). Positive anomaly means we encounter more unusually high chlorophyll values than expected, negative anomaly means we encounter less. Chlorophyll-a anomaly dimensionless The anomaly is a measure of the extent to which the number of unusually high daily chlorophyll-a values is greater or less than expected. Unusually high is defined as exceeding P90 value for that day. At a given position, if the anomaly is zero and equal to the reference period we would expect the number of times the daily chlorophyll-a value exceeds P90 over a month to be 10% (0.1) of the total number of chlorophyll measurements. The monthly anomaly is therefore the difference between the actual proportion and 0.1, hence has a possible range of -0.1 (proportion=0) to 0.9 (proportion=1). Positive anomaly means we encounter more unusually high chlorophyll values than expected, negative anomaly means we encounter less. Chlorophyll-a anomaly P-value dimensionless The two-tailed P-value of the mean annual rate of change in chlorophyll-a anomaly. Low P-values indicate that the results are unlikely to have arisen by chance, and so we can be more confident that the trend is real. Standard practice is to treat P-values less than 0.05 as significant. Chlorophyll-a anomaly P-value dimensionless The two-tailed P-value of the mean annual rate of change in chlorophyll-a anomaly. Low P-values indicate that the results are unlikely to have arisen by chance, and so we can be more confident that the trend is real. Standard practice is to treat P-values less than 0.05 as significant. Chlorophyll-a anomaly gradient year-1 The mean annual rate of change of the chlorophyll-a anomaly from the start of the time series to the given date, determined through simple linear regression. These data are available either as a 'standard' time series where data is presented with a monthly resolution, progressing January, February etc. through the year, or split out on a calendar month basis where only data for a named month is returned. This monthly data is useful for comparing year on year changes for a specific month or season. Chlorophyll-a anomaly gradient year-1 The mean annual rate of change of the chlorophyll-a anomaly from the start of the time series to the given date, determined through simple linear regression. These data are available either as a 'standard' time series where data is presented with a monthly resolution, progressing January, February etc. through the year, or split out on a calendar month basis where only data for a named month is returned. This monthly data is useful for comparing year on year changes for a specific month or season. Cumulative chlorophyll-a anomaly dimensionless The cumulative sum of monthly anomalies from the start of the time series to the given date. This is a measure of consistent behaviour in the anomaly, e.g. if the anomaly is highly variable but slightly greater than zero on average, this might be hard to detect in the anomaly or slope data, but the cumulative anomaly will gradually increase. This can either be calculated using every month in the time series, or using only the calendar month corresponding to the date, e.g. the sum to August 2016 would use only August anomalies. For the data stored by month this is the cumulative difference between the monthly proportion exceeding climatological P90 and 0.1 in the given calendar month. Cumulative chlorophyll-a anomaly dimensionless The cumulative sum of monthly anomalies from the start of the time series to the given date. This is a measure of consistent behaviour in the anomaly, e.g. if the anomaly is highly variable but slightly greater than zero on average, this might be hard to detect in the anomaly or slope data, but the cumulative anomaly will gradually increase. This can either be calculated using every month in the time series, or using only the calendar month corresponding to the date, e.g. the sum to August 2016 would use only August anomalies. For the data stored by month this is the cumulative difference between the monthly proportion exceeding climatological P90 and 0.1 in the given calendar month. Standard error of the cumulative chlorophyll-a anomaly dimensionless This is a measure of the statistical uncertainty of the cumulative anomaly. For instance, a cumulative anomaly of 1 would suggest a consistent anomaly greater than zero if its standard error were 0.1, but would be attributable to chance if it were 10. For the data stored by month this is the standard error of the cumulative difference in proportion in the given calendar month. Standard error of the cumulative chlorophyll-a anomaly dimensionless This is a measure of the statistical uncertainty of the cumulative anomaly. For instance, a cumulative anomaly of 1 would suggest a consistent anomaly greater than zero if its standard error were 0.1, but would be attributable to chance if it were 10. For the data stored by month this is the standard error of the cumulative difference in proportion in the given calendar month. 544 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/antarctic-sea-ice-extent-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=ANTARCTIC_OMI_SI_extent Antarctic Sea Ice Extent from Reanalysis DEFINITION Estimates of Antarctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Southern Hemisphere (excluding ice lakes) and from 1993 up to real time aiming to: i) obtain the Antarctic sea ice extent as expressed in millions of km squared (106 km2) to monitor both the large-scale variability and mean state and change. ii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss/gain since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013)). For the Southern Hemisphere, these trends are calculated from the annual mean values. The Antarctic sea ice extent used here is based on the “multi-product” approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread. CONTEXT Sea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019). The sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018). CMEMS KEY FINDINGS With quasi regular highs and lows, the annual Antarctic sea ice extent shows large variability until several monthly record high in 2014 and record lows in 2017 and 2018. Since the year 1993, the Southern Hemisphere annual sea ice extent regularly alternates positive and negative trend. The period 1993-2018 have seen a slight decrease at a rate of -0.01*106km2 per decade. This represents a loss amount of 0.1% per decade of Southern Hemisphere sea ice extent during this period; with however large uncertainties. The last quarter of the year 2016 and years 2017 and 2018 experienced unusual losses of ice. 2019 is not a record year, but the summer of 2019 remains among the lowest since the 1990s. Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00186 https://doi.org/10.48670/moi-00186 545 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/end-season-date-2017-present-raster-10-m-europe-yearly https://www.wekeo.eu/data?view=viewer&t=1577905116279&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1QSEVOT0xPR1ktQU5ELVBST0RVQ1RJVklUWS1QQVJBTUVURVJTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVlBQX0VPU0RfU0VBU09OMV8xME0iLCJ6SW5kZXgiOjgwfV0%3D&initial=1 End-of-season Date 2017-present (raster 10 m), Europe, yearly, Sept. 2021 The End-of-Season Date (EOSD), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The End-of-Season Date (EOSD) marks the date when the vegetation growing season ends in the time profile of the Plant Phenology Index (PPI). The end-of-season occurs, by definition, when the PPI value reaches 15% of the season amplitude during the green-down period. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The End-of-Season Date is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-m… https://land.copernicus.eu/user-corner/technical-library/product-user-m… A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The EOSD dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year. 546 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/tree-cover-density-2018-raster-10-m-europe-3-yearly-sep https://land.copernicus.eu/pan-european/high-resolution-layers/forests/tree-cover-density/status-maps/tree-cover-density-2018 Tree Cover Density 2018 (raster 10 m), Europe, 3-yearly, Sep. 2020 This metadata refers to the HRL Forest 2018 primary status layer Tree Cover Density (TCD). The TCD raster product provides information on the proportional crown coverage per pixel at 10m spatial resolution and ranges from 0% (all non-tree covered areas) to 100%, whereby Tree Cover Density is defined as the "vertical projection of tree crowns to a horizontal earth’s surface“. The production of the High Resolution Forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. The HRL Forest product consists of 3 types of (status) products and additional change products. The status products are available for 2012, 2015, and 2018 reference years: 1. Tree cover density (TCD) (level of tree cover density in a range from 0-100%) 2. Dominant leaf type (DLT) (broadleaved or coniferous majority) 3. Forest type product (FTY). The forest type product allows to get as close as possible to the FAO forest definition. In its original (10m (2018) / 20m (2012, 2015)) resolution it consists of two products: a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps (now only available on demand), based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and imperviousness 2009 data). For the final 100 m product trees under agricultural use and urban context from the support layer are removed. NEW for 2018: the 10m 2018 reference year FTY product now also has the agricultural/urban trees removed. In the past this was done only for the 100m product, now it is consistently applied for both the 10m and the 100m FTY products. This dataset is provided as 10 meter rasters (fully conformant with the EEA reference grid) in 100 x 100 km tiles grouped according to the EEA38 countries and the United Kingdom. 547 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-wave-hindcast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_WAV_003_015 Baltic Sea Wave Hindcast Short description : This Baltic Sea wave model hindcast product provides a hindcast for the wave conditions in the Baltic Sea since 1/1 1993 and up to 0.5-1 year compared to real time. This hindcast product consists of a dataset with hourly data for significant wave height, wave period and wave direction for total sea, wind sea and swell, and also Stokes drift. The product is based on the wave model WAM cycle 4.6.2, and surface forcing from ECMWF's ERA5 reanalysis products. The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The product provides hourly instantaneously model data. DOI (product) :https://doi.org/10.48670/moi-00014 https://doi.org/10.48670/moi-00014 548 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/seasonal-productivity-2017-present-raster-10-m-europe https://www.wekeo.eu/data?view=viewer&t=1577905116279&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1QSEVOT0xPR1ktQU5ELVBST0RVQ1RJVklUWS1QQVJBTUVURVJTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVlBQX1NQUk9EX1NFQVNPTjFfMTBNIiwiekluZGV4Ijo4MCwiaXNFeHBsb3JpbmciOnRydWV9XQ%3D%3D&initial=1 Seasonal Productivity 2017-present (raster 10 m), Europe, yearly, Sept. 2021 The Seasonal Productivity (SPROD), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Seasonal Productivity (SPROD), or small integral, is the growing season integral that is computed as the sum of all daily Plant Phenology Index (PPI) values between the dates of the season start (SOSD) and end (EOSD), minus their base level value. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Seasonal Productivity is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-m… https://land.copernicus.eu/user-corner/technical-library/product-user-m… A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The SPROD dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year. 549 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/season-maximum-date-2017-present-raster-10-m-europe https://www.wekeo.eu/data?view=viewer&t=1577905116279&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1QSEVOT0xPR1ktQU5ELVBST0RVQ1RJVklUWS1QQVJBTUVURVJTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVlBQX01BWERfU0VBU09OMV8xME0iLCJ6SW5kZXgiOjgwfV0%3D&initial=1 Season Maximum Date 2017-present (raster 10 m), Europe, yearly, Sept. 2021 The Season Maximum Date (MAXD), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Season Maximum Date (MAXD) is the date in the vegetation growing season when the maximum Plant Phenology Index (PPI) value is reached. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Season Maximum Date is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-m… https://land.copernicus.eu/user-corner/technical-library/product-user-m… A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The MAXD dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year. 550 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-iberian-biscay-irish-ocean-biogeochemistry-non http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=IBI_MULTIYEAR_BGC_005_003 Atlantic-Iberian Biscay Irish- Ocean BioGeoChemistry NON ASSIMILATIVE Hindcast Short description: The IBI-MFC provides a biogeochemical reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly updated on a yearly basis. The model system is run by Mercator-Ocean, being the product post-processed to the user’s format by Nologin with the support of CESGA in terms of supercomputing resources. To this aim, an application of the biogeochemical model PISCES is run simultaneously with the ocean physical IBI reanalysis, generating both products at the same 1/12° horizontal resolution. The PISCES model is able to simulate the first levels of the marine food web, from nutrients up to mesozooplankton and it has 24 state variables. The product provides daily, monthly and yearly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon and surface partial pressure of carbon dioxide. Additionally, climatological parameters (monthly mean and standard deviation) of these variables for the period 1993-2016 are delivered. Product Citation: Please refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag…] http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (Product):https://doi.org/10.48670/moi-00029 https://doi.org/10.48670/moi-00029 551 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-colour-plankton-reflectance-transparency-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_ARC_BGC_L3_MY_009_123 Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 daily observations Short description: For the Arctic Ocean Satellite Observations, Italian National Research Council (CNR – Rome, Italy) is providing Bio-Geo_Chemical (BGC) products. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the ""multi"" products and S3A & S3B only for the ""olci"" products. * Variables: Chlorophyll-a (CHL), Phytoplankton Functional types and sizes (PFT), Suspended Matter (SPM), Diffuse Attenuation (KD490), Detrital and Dissolved Material Absorption Coef. (ADG443''), Phytoplankton Absorption Coef. (APH443''), Total Absorption Coef. (ATOT443'') and Reflectance (RRS). * Temporal resolutions: daily, monthly. * Spatial resolutions: 1 km (multi). * Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY). DOI (product) :https://doi.org/10.48670/moi-00292 https://doi.org/10.48670/moi-00292 552 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/pan-european-high-resolution-image-mosaic-2018-true https://land.copernicus.eu/imagery-in-situ/european-image-mosaics/high-resolution/high-resolution-image-mosaics-2018/high-resolution-image-mosaic-2018-true-colour-10m Pan-European High Resolution Image Mosaic 2018 - True Colour, Coverage 1 (10 m), Sept. 2019 The pan-European High Resolution (HR) Image Mosaic 2018 provides cloud-free high resolution true colour imagery for EEA39 countries. The mosaic has been produced using Sentinel-2 data in 10 meter resolution, at a Sentinel 2 tile level, and consists of 1079 Sentinel-2 tiles. The imagery for each state is acquired within a predefined window corresponding to the vegetation season in 2018. The true colour (RGB) composite consists of a three band stack and includes the following bands: Band 4 – Red (0.665 μm) Band 3 – Green (0.560 μm) Band 2 – Blue (0.490 μm) The mosaic primarily is used as input data in the production of various Copernicus Land Monitoring Service (CLMS) datasets and services, such as land cover maps and high resolution layers on land cover characteristic and can be also useful for CLMS users for visualizations and classifications on land. Since the pan-European High Resolution Mosaic 2018 is created exclusively from Sentinel-2 data, the imagery can be downloaded from The Copernicus Open Access Hub (mission ongoing since 2015) at https://scihub.copernicus.eu/. Global Sentinel-2 Image Mosaics Hub at https://land.copernicus.eu/imagery-in-situ/global-image-mosaics/ can be also used to automatically create mosaics over the area of interest. https://scihub.copernicus.eu/ https://land.copernicus.eu/imagery-in-situ/global-image-mosaics/ 553 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-biogeochemistry-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=ARCTIC_MULTIYEAR_BGC_002_005 Arctic Ocean Biogeochemistry Reanalysis Short description: The TOPAZ-ECOSMO reanalysis system assimilates satellite chlorophyll observations and in situ nutrient profiles. The model uses the Hybrid Coordinate Ocean Model (HYCOM) coupled online to a sea ice model and the ECOSMO biogeochemical model. It uses the Determinstic version of the Ensemble Kalman Smoother to assimilate remotely sensed colour data and nutrient profiles. Data assimilation, including the 80-member ensemble production, is performed every 8-days. Atmospheric forcing fields from the ECMWF ERA-5 dataset are used. DOI (product) :https://doi.org/10.48670/moi-00006 https://doi.org/10.48670/moi-00006 554 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/total-productivity-2017-present-raster-10-m-europe-yearly https://www.wekeo.eu/data?view=viewer&t=1577905116279&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1QSEVOT0xPR1ktQU5ELVBST0RVQ1RJVklUWS1QQVJBTUVURVJTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVlBQX1RQUk9EX1NFQVNPTjFfMTBNIiwiekluZGV4Ijo4MH1d&initial=1 Total Productivity 2017-present (raster 10 m), Europe, yearly, Sept. 2021 The Total Productivity (TPROD), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Total Productivity (TPROD), or large integral, is the growing season integral computed as the sum of all daily Plant Phenology Index values between the dates of the season start (SOSD) and end (EOSD). The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Total Productivity is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-m… https://land.copernicus.eu/user-corner/technical-library/product-user-m… A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The TPROD dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year. 555 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/land-cover-2015-2019-raster-100-m-global-annual-version-3 https://lcviewer.vito.be/ Land Cover 2015-2019 (raster 100 m), global, annual - version 3 Land cover maps represent spatial information on different types (classes) of physical coverage of the Earth's surface, e.g. forests, grasslands, croplands, lakes, wetlands. Dynamic land cover maps include transitions of land cover classes over time and hence captures land cover changes. This Collection 3 includes the global land cover maps, at 100m resolution, for base year 2015 and subsequent years. These consist of different layers: the base classification with 23 classes, versatile fractional cover (0-100% per pixel) for the main classes, forest type and related quality information (e.g. classification probability, input data density and confidence level for the change detection). 556 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-era5-pressure-levels-monthly-means https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly-means reanalysis-era5-pressure-levels-monthly-means ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. ERA5 Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. So far this has only been the case for the month September 2021, while it will also be the case for October, November and December 2021. For months prior to September 2021 the final release has always been equal to ERA5T, and the goal is to align the two again after December 2021. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. this article these guidelines Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on pressure levels from 1940 to present". DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid. Horizontal coverage Global Horizontal resolution Reanalysis: 0.25° x 0.25° Ensemble members: 0.5° x 0.5° Vertical coverage 1000 hPa to 1 hPa Vertical resolution 37 pressure levels Temporal coverage 1940 to present Temporal resolution Monthly File format GRIB DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid. Projection Regular latitude-longitude grid. Horizontal coverage Global Horizontal coverage Global Horizontal resolution Reanalysis: 0.25° x 0.25° Ensemble members: 0.5° x 0.5° Horizontal resolution Reanalysis: 0.25° x 0.25° Ensemble members: 0.5° x 0.5° Vertical coverage 1000 hPa to 1 hPa Vertical coverage 1000 hPa to 1 hPa Vertical resolution 37 pressure levels Vertical resolution 37 pressure levels Temporal coverage 1940 to present Temporal coverage 1940 to present Temporal resolution Monthly Temporal resolution Monthly File format GRIB File format GRIB MAIN VARIABLES Name Units Description Divergence s-1 This parameter is the horizontal divergence of velocity. It is the rate at which air is spreading out horizontally from a point, per square metre. This parameter is positive for air that is spreading out, or diverging, and negative for the opposite, for air that is concentrating, or converging (convergence). Fraction of cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud (liquid or ice) and varies between zero and one. This parameter is available on multiple levels through the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography. Ozone mass mixing ratio kg kg-1 This parameter is the mass of ozone per kilogram of air. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. Most of the IFS chemical species are archived as mass mixing ratios [kg kg-1]. Potential vorticity K m2 kg-1 s-1 Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere. Relative humidity % This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function. Specific cloud ice water content kg kg-1 This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'. Specific cloud liquid water content kg kg-1 This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific humidity kg kg-1 This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. Specific rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Temperature K This parameter is the temperature in the atmosphere. It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere. U-component of wind m s-1 This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east. A negative sign indicates air moving towards the west. V-component of wind m s-1 This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north. A negative sign indicates air moving towards the south. Vertical velocity Pa s-1 This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion. Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values). Vorticity (relative) s-1 This parameter is a measure of the rotation of air in the horizontal, around a vertical axis, relative to a fixed point on the surface of the Earth. On the scale of weather systems, troughs (weather features that can include rain) are associated with anticlockwise rotation (in the northern hemisphere), and ridges (weather features that bring light or still winds) are associated with clockwise rotation. Adding the effect of rotation of the Earth, the Coriolis parameter, to the relative vorticity produces the absolute vorticity. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Divergence s-1 This parameter is the horizontal divergence of velocity. It is the rate at which air is spreading out horizontally from a point, per square metre. This parameter is positive for air that is spreading out, or diverging, and negative for the opposite, for air that is concentrating, or converging (convergence). Divergence s-1 This parameter is the horizontal divergence of velocity. It is the rate at which air is spreading out horizontally from a point, per square metre. This parameter is positive for air that is spreading out, or diverging, and negative for the opposite, for air that is concentrating, or converging (convergence). Fraction of cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud (liquid or ice) and varies between zero and one. This parameter is available on multiple levels through the atmosphere. Fraction of cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud (liquid or ice) and varies between zero and one. This parameter is available on multiple levels through the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography. Ozone mass mixing ratio kg kg-1 This parameter is the mass of ozone per kilogram of air. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. Most of the IFS chemical species are archived as mass mixing ratios [kg kg-1]. Ozone mass mixing ratio kg kg-1 This parameter is the mass of ozone per kilogram of air. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. Most of the IFS chemical species are archived as mass mixing ratios [kg kg-1]. Potential vorticity K m2 kg-1 s-1 Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere. Potential vorticity K m2 kg-1 s-1 Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere. Relative humidity % This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function. Relative humidity % This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function. Specific cloud ice water content kg kg-1 This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'. Specific cloud ice water content kg kg-1 This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'. Specific cloud liquid water content kg kg-1 This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud liquid water content kg kg-1 This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific humidity kg kg-1 This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. Specific humidity kg kg-1 This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. Specific rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Temperature K This parameter is the temperature in the atmosphere. It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere. Temperature K This parameter is the temperature in the atmosphere. It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere. U-component of wind m s-1 This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east. A negative sign indicates air moving towards the west. U-component of wind m s-1 This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east. A negative sign indicates air moving towards the west. V-component of wind m s-1 This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north. A negative sign indicates air moving towards the south. V-component of wind m s-1 This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north. A negative sign indicates air moving towards the south. Vertical velocity Pa s-1 This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion. Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values). Vertical velocity Pa s-1 This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion. Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values). Vorticity (relative) s-1 This parameter is a measure of the rotation of air in the horizontal, around a vertical axis, relative to a fixed point on the surface of the Earth. On the scale of weather systems, troughs (weather features that can include rain) are associated with anticlockwise rotation (in the northern hemisphere), and ridges (weather features that bring light or still winds) are associated with clockwise rotation. Adding the effect of rotation of the Earth, the Coriolis parameter, to the relative vorticity produces the absolute vorticity. Vorticity (relative) s-1 This parameter is a measure of the rotation of air in the horizontal, around a vertical axis, relative to a fixed point on the surface of the Earth. On the scale of weather systems, troughs (weather features that can include rain) are associated with anticlockwise rotation (in the northern hemisphere), and ridges (weather features that bring light or still winds) are associated with clockwise rotation. Adding the effect of rotation of the Earth, the Coriolis parameter, to the relative vorticity produces the absolute vorticity. 557 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-chlorophyll-time-series-and-trend-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OMI_HEALTH_CHL_BLKSEA_OCEANCOLOUR_area_averaged_mean Black Sea Chlorophyll-a time series and trend from Observations Reprocessing DEFINITION The time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3A-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of two different regional algorithms developed with the BiOMaP data set (Zibordi et al., 2011): a band-ratio algorithm (B/R) (Zibordi et al., 2015) and a Multilayer Perceptron (MLP) neural net algorithm based on Rrs values at three individual wavelengths (490, 510 and 555 nm) (Kajiyama et al., 2018). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2021). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens’s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend. CONTEXT Phytoplankton and chlorophyll concentration as a proxy for phytoplankton respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Gregg and Rousseaux, 2014, Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Black Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) (Oguz et al. 2003). CMEMS KEY FINDINGS In the Black Sea, the trend average for the 1997-2021 period is definitely negative (-1.39±0.91% per year). Nevertheless, this negative trend is lower than the one estimated in the previous release (related to 1997-2020). The negative trend is mainly due to the marked change on chlorophyll concentrations between 2002 and 2004. From 2004 onwards, minima and maxima are strongly variable year by year. However, on average, the minima/maxima variability can be considered quite constant with a continuous decrease of maxima from 2015 up to mid 2020 where signal seems to change again with relative high chlorophyll values in 2021. The general negative trend in the Black Sea is also confirmed by the analysis of Sathyendranath et al. (2018), that reveals an increasing trend in chlorophyll concentration in all the European Seas, except for the Black Sea. DOI (product):https://doi.org/10.48670/moi-00211 https://doi.org/10.48670/moi-00211 558 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hemispherical-albedo-1999-2020-raster-1-km-global-10 http://land.copernicus.eu/global/products/sa Hemispherical Albedo 1999-2020 (raster 1 km), global, 10-daily - version 1 The surface albedo quantifies the fraction of irradiance reflected by the surface of the Earth. It provides information on the radiative basis, thus on the temperature and water balance. The hemispherical albedo or bi-hemispherical reflectance (also called white-sky albedo) is defined as the integration of the directional albedo over the illumination hemisphere. It assumes a complete diffuse illumination. 559 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-biodiversity-suitability-grasslands https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-biodiversity-suitability-grasslands app-biodiversity-suitability-grasslands This application explores six bioclimatic variables relevant for grassland conservation and management over Europe, driven by CMIP5 climate projections (bias-adjusted to ERA5) from ten Global Circulation Models. Grassland suitability provides stakeholders within the agro-ecology sector with information about the climate impact on semi-natural grassland ecosystems to support management decisions. The application's interactive map shows 20-year average climate suitability for three selected grassland species that are characteristic to semi-natural xeric grasslands, for two climate scenarios/experiments (Representative Concentration Pathways RCP4.5 (moderate scenario) and RCP8.5 (pessimistic scenario)). Clicking on a country displays time series graphs for the six bioclimatic variables relevant to the selected species' suitability. The modelled climate suitability each of the three xeric grassland species is based on species-specific preferences for annual minimum temperature, annual precipitation and Koeppen-Geiger class. User-selectable parameters User-selectable parameters Variable: The data visualised in the interactive map. Experiment: Emissions scenario, Representative Concentration Pathways (RCP) 4.5 (moderate) or 8.5 (high). Species: Grassland species for which to visualise climate suitability. Variable: The data visualised in the interactive map. Experiment: Emissions scenario, Representative Concentration Pathways (RCP) 4.5 (moderate) or 8.5 (high). Species: Grassland species for which to visualise climate suitability. INPUT VARIABLES Name Units Description Source Annual mean temperature (BIO01) K Annual mean of the daily mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Bioclimatic indicators Annual precipitation (BIO12) m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Bioclimatic indicators Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Bioclimatic indicators Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). Bioclimatic indicators Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). Bioclimatic indicators Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds. Bioclimatic indicators Monthly mean precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Bioclimatic indicators INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Annual mean temperature (BIO01) K Annual mean of the daily mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Bioclimatic indicators Annual mean temperature (BIO01) K Annual mean of the daily mean temperature at 2 m above the surface. This indicator corresponds to the official BIOCLIM variable BIO01 that is used in ecological niche modelling. Bioclimatic indicators Bioclimatic indicators Annual precipitation (BIO12) m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Bioclimatic indicators Annual precipitation (BIO12) m s-1 Annual mean of the daily mean precipitation rate (both liquid and solid phases). This indicator corresponds to the official BIOCLIM variable BIO12. To compute the total precipitation sum over the year, a conversion factor should be applied of 3600x24x365x1000 (mm year-1). Bioclimatic indicators Bioclimatic indicators Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Bioclimatic indicators Dry days day Number of days within a year where total daily precipitation does not exceed 2 mm. Bioclimatic indicators Bioclimatic indicators Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). Bioclimatic indicators Frost days day Number of days during the growing season with minimum temperature below 273 K (0 oC). Bioclimatic indicators Bioclimatic indicators Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). Bioclimatic indicators Growing degree days K day year-1 The sum of daily degrees above the daily mean temperature of 278 K (5 oC). Bioclimatic indicators Bioclimatic indicators Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds. Bioclimatic indicators Koeppen-Geiger class Dimensionless A climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds. Bioclimatic indicators Bioclimatic indicators Monthly mean precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Bioclimatic indicators Monthly mean precipitation m s-1 Average over the daily mean precipitation. The data is aggregated over the month and the year. To compute the total precipitation sum over the aggregation period, a conversion factor should be applied of 3600x24x1000x30.4 (average number of days per month) or x365 (average number of days per year). Bioclimatic indicators Bioclimatic indicators OUTPUT VARIABLES Name Units Description Annual mean temperature K The 20-year mean of the annual mean temperature at 2 m above the surface. Annual precipitation m s-1 The 20-year mean of the annual mean precipitation rate. Climate suitability Dimensionless Climate suitability (0-1) for different grassland species in accordance to the above bioclimate indicators, where 0 is completely climatically unsuitable and 1 means completely climatically suitable. Dry days day The 20-year mean of the number of days within a year where total daily precipitation does not exceed 2 mm. Frost days day The 20-year mean of the number of days during the growing season with minimum temperature below 273 K (0 oC). Growing degree days K day year-1 The 20-year mean of the sum of daily degrees above the daily mean temperature of 278 K (5 oC). Koeppen-Geiger class Dimensionless The 20-year mean of a climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds. Monthly mean precipitation m s-1 The 20-year mean of monthly mean precipitation. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Annual mean temperature K The 20-year mean of the annual mean temperature at 2 m above the surface. Annual mean temperature K The 20-year mean of the annual mean temperature at 2 m above the surface. Annual precipitation m s-1 The 20-year mean of the annual mean precipitation rate. Annual precipitation m s-1 The 20-year mean of the annual mean precipitation rate. Climate suitability Dimensionless Climate suitability (0-1) for different grassland species in accordance to the above bioclimate indicators, where 0 is completely climatically unsuitable and 1 means completely climatically suitable. Climate suitability Dimensionless Climate suitability (0-1) for different grassland species in accordance to the above bioclimate indicators, where 0 is completely climatically unsuitable and 1 means completely climatically suitable. Dry days day The 20-year mean of the number of days within a year where total daily precipitation does not exceed 2 mm. Dry days day The 20-year mean of the number of days within a year where total daily precipitation does not exceed 2 mm. Frost days day The 20-year mean of the number of days during the growing season with minimum temperature below 273 K (0 oC). Frost days day The 20-year mean of the number of days during the growing season with minimum temperature below 273 K (0 oC). Growing degree days K day year-1 The 20-year mean of the sum of daily degrees above the daily mean temperature of 278 K (5 oC). Growing degree days K day year-1 The 20-year mean of the sum of daily degrees above the daily mean temperature of 278 K (5 oC). Koeppen-Geiger class Dimensionless The 20-year mean of a climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds. Koeppen-Geiger class Dimensionless The 20-year mean of a climate classification that divides worldwide climates into separate classes depending on temperature and precipitation thresholds. Monthly mean precipitation m s-1 The 20-year mean of monthly mean precipitation. Monthly mean precipitation m s-1 The 20-year mean of monthly mean precipitation. 560 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-aerosol-properties https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-aerosol-properties satellite-aerosol-properties This data set provides observational records of aerosol properties obtained from observations collected by various satellite instruments. Aerosols are minor constituents of the atmosphere by mass, but critical components in terms of impact on climate. Aerosols influence the global radiation balance directly by scattering and absorbing radiation, and indirectly through influencing cloud reflectivity, cloud cover and cloud lifetime. The main variables provided by this dataset are: aerosol optical depth, fine mode aerosol optical depth, dust aerosol optical depth, single scattering albedo, aerosol layer height and aerosol extinction coefficient. These variables are derived from observations from several sensors using a set of different processing techniques. This provides the possibility to derive a large set of complementary aerosol properties needed to describe the complex nature of atmospheric aerosols. Furthermore, different algorithms have their specific strengths and weaknesses, meaning that datasets originating from the same sensor but processed by different algorithms provide a way to evaluate uncertainties (e.g. areas of good or bad agreement between them). Altogether, the aerosol properties dataset is very extensive and offers a choice of complementary options – which is appropriate depends on the intended application. Selected observational records in this dataset are extended in time on a semi-annual basis. At the moment of extending, these records are up-to-date until five months behind present time. This dataset is produced on behalf of C3S. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal resolution 2.5° x 2.5° for aerosol extinction coefficient 1° x 1° for all other variables Temporal coverage June 1995 to present with 5 month delay Temporal resolution 5-daily composite for the aerosol extinction coefficient Daily and monthly for all other variables File format NetCDF Conventions Climate and Forecast (CF) Metadata Convention v1.6 Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions All v1.11 data has now been superseded by v1.12. In version 1.11 of the product derived from SLSTR on SENTINEL 3A observations with the SWANSEA (Swansea University) algorithm, the variables Aerosol optical depth and Fine-mode aerosol optical depth have been mistakenly swapped. This issue has been fixed in version 1.12 and the latter should be used instead. Update frequency 6 months Full mission reprocessing every 2-3 years DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid Projection Regular latitude-longitude grid Horizontal coverage Global Horizontal coverage Global Horizontal resolution 2.5° x 2.5° for aerosol extinction coefficient 1° x 1° for all other variables Horizontal resolution 2.5° x 2.5° for aerosol extinction coefficient 1° x 1° for all other variables 2.5° x 2.5° for aerosol extinction coefficient 1° x 1° for all other variables Temporal coverage June 1995 to present with 5 month delay Temporal coverage June 1995 to present with 5 month delay Temporal resolution 5-daily composite for the aerosol extinction coefficient Daily and monthly for all other variables Temporal resolution 5-daily composite for the aerosol extinction coefficient Daily and monthly for all other variables 5-daily composite for the aerosol extinction coefficient Daily and monthly for all other variables File format NetCDF File format NetCDF Conventions Climate and Forecast (CF) Metadata Convention v1.6 Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Attribute Convention for Dataset Discovery (ACDD) v1.3 Climate and Forecast (CF) Metadata Convention v1.6 Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions All v1.11 data has now been superseded by v1.12. In version 1.11 of the product derived from SLSTR on SENTINEL 3A observations with the SWANSEA (Swansea University) algorithm, the variables Aerosol optical depth and Fine-mode aerosol optical depth have been mistakenly swapped. This issue has been fixed in version 1.12 and the latter should be used instead. Versions All v1.11 data has now been superseded by v1.12. In version 1.11 of the product derived from SLSTR on SENTINEL 3A observations with the SWANSEA (Swansea University) algorithm, the variables Aerosol optical depth and Fine-mode aerosol optical depth have been mistakenly swapped. This issue has been fixed in version 1.12 and the latter should be used instead. Update frequency 6 months Full mission reprocessing every 2-3 years Update frequency 6 months Full mission reprocessing every 2-3 years 6 months Full mission reprocessing every 2-3 years MAIN VARIABLES Name Units Description Aerosol extinction coefficient km-1 Aerosol extinction coefficient (AEX) is the fraction of radiant flux (or light) absorbed and scattered by aerosol present in a volume of atmosphere, per unit length. Extinction coefficients are provided on multiple levels through the atmosphere. Aerosol layer height km Aerosol layer height (ALH), provides the average altitude of the aerosol loading. It can be interpreted as the height level at which the largest aerosol extinction is observed. Aerosol layer height is is derived from observations collected by different sensors. Aerosol optical depth Dimensionless Aerosol optical depth (AOD, or sometimes Aerosol optical thickness) is a measure of the degree to which transmission of light through a volume of atmosphere is reduced due to extinction (scattering and absorption) by aerosol. It is equivalent to the integral of the extinction coefficient over a vertical column of unit cross section. Typical global average aerosol optical depth is about 0.15; in rare cases atmospheric aerosol optical depth can reach 3. Typically aerosol optical depth observations are reported at the mid-visible reference wavelength of 550 nm. Dust aerosol layer height km Aerosol layer height specific to dust type aerosol. Dust aerosol optical depth Dimensionless Dust aerosol optical depth (DAOD) is the part of total aerosol optical depth resulting from the presence of mineral dust particles (emitted from deserts such as the Sahara and potentially transported in the atmosphere over thousands of kilometers). Dust aerosol optical depth is helpful for constraining the type of aerosol. Fine mode aerosol optical depth Dimensionless Fine mode aerosol optical depth (FMAOD) is the part of total aerosol optical depth resulting from the presence of aerosol particles with diameter smaller than 1 micrometer (this includes mostly secondary aerosols from combustion and fires such as sulfates, nitrates, black and brown carbon). Fine mode aerosol optical depth is helpful for constraining the type of aerosol. Single scattering albedo Dimensionless Single scattering albedo (SSA), provides the fraction of total aerosol extinction that results from scattering (as opposed to absorbtion) of light by aerosol particles. Typical values range from 0.8 for strongly absorbing aerosol types (e.g. soot) to 1 for non-absorbing aerosol types (e.g. sea salt). Single scattering albedo is helpful for constraining aerosol the type of aerosol. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Aerosol extinction coefficient km-1 Aerosol extinction coefficient (AEX) is the fraction of radiant flux (or light) absorbed and scattered by aerosol present in a volume of atmosphere, per unit length. Extinction coefficients are provided on multiple levels through the atmosphere. Aerosol extinction coefficient km-1 Aerosol extinction coefficient (AEX) is the fraction of radiant flux (or light) absorbed and scattered by aerosol present in a volume of atmosphere, per unit length. Extinction coefficients are provided on multiple levels through the atmosphere. Aerosol layer height km Aerosol layer height (ALH), provides the average altitude of the aerosol loading. It can be interpreted as the height level at which the largest aerosol extinction is observed. Aerosol layer height is is derived from observations collected by different sensors. Aerosol layer height km Aerosol layer height (ALH), provides the average altitude of the aerosol loading. It can be interpreted as the height level at which the largest aerosol extinction is observed. Aerosol layer height is is derived from observations collected by different sensors. Aerosol optical depth Dimensionless Aerosol optical depth (AOD, or sometimes Aerosol optical thickness) is a measure of the degree to which transmission of light through a volume of atmosphere is reduced due to extinction (scattering and absorption) by aerosol. It is equivalent to the integral of the extinction coefficient over a vertical column of unit cross section. Typical global average aerosol optical depth is about 0.15; in rare cases atmospheric aerosol optical depth can reach 3. Typically aerosol optical depth observations are reported at the mid-visible reference wavelength of 550 nm. Aerosol optical depth Dimensionless Aerosol optical depth (AOD, or sometimes Aerosol optical thickness) is a measure of the degree to which transmission of light through a volume of atmosphere is reduced due to extinction (scattering and absorption) by aerosol. It is equivalent to the integral of the extinction coefficient over a vertical column of unit cross section. Typical global average aerosol optical depth is about 0.15; in rare cases atmospheric aerosol optical depth can reach 3. Typically aerosol optical depth observations are reported at the mid-visible reference wavelength of 550 nm. Dust aerosol layer height km Aerosol layer height specific to dust type aerosol. Dust aerosol layer height km Aerosol layer height specific to dust type aerosol. Dust aerosol optical depth Dimensionless Dust aerosol optical depth (DAOD) is the part of total aerosol optical depth resulting from the presence of mineral dust particles (emitted from deserts such as the Sahara and potentially transported in the atmosphere over thousands of kilometers). Dust aerosol optical depth is helpful for constraining the type of aerosol. Dust aerosol optical depth Dimensionless Dust aerosol optical depth (DAOD) is the part of total aerosol optical depth resulting from the presence of mineral dust particles (emitted from deserts such as the Sahara and potentially transported in the atmosphere over thousands of kilometers). Dust aerosol optical depth is helpful for constraining the type of aerosol. Fine mode aerosol optical depth Dimensionless Fine mode aerosol optical depth (FMAOD) is the part of total aerosol optical depth resulting from the presence of aerosol particles with diameter smaller than 1 micrometer (this includes mostly secondary aerosols from combustion and fires such as sulfates, nitrates, black and brown carbon). Fine mode aerosol optical depth is helpful for constraining the type of aerosol. Fine mode aerosol optical depth Dimensionless Fine mode aerosol optical depth (FMAOD) is the part of total aerosol optical depth resulting from the presence of aerosol particles with diameter smaller than 1 micrometer (this includes mostly secondary aerosols from combustion and fires such as sulfates, nitrates, black and brown carbon). Fine mode aerosol optical depth is helpful for constraining the type of aerosol. Single scattering albedo Dimensionless Single scattering albedo (SSA), provides the fraction of total aerosol extinction that results from scattering (as opposed to absorbtion) of light by aerosol particles. Typical values range from 0.8 for strongly absorbing aerosol types (e.g. soot) to 1 for non-absorbing aerosol types (e.g. sea salt). Single scattering albedo is helpful for constraining aerosol the type of aerosol. Single scattering albedo Dimensionless Single scattering albedo (SSA), provides the fraction of total aerosol extinction that results from scattering (as opposed to absorbtion) of light by aerosol particles. Typical values range from 0.8 for strongly absorbing aerosol types (e.g. soot) to 1 for non-absorbing aerosol types (e.g. sea salt). Single scattering albedo is helpful for constraining aerosol the type of aerosol. RELATED VARIABLES A number of variables accounting for uncertainty of the data provided and diagnostic variables on assumed or co-retrieved variables (such as further aerosol properties or surface brightness and cloud cover) are also included in the files along the main variables. They help users understand better on per datum level the possible variations of the main variables due to changes in the processing algorithms and their assumptions. RELATED VARIABLES RELATED VARIABLES A number of variables accounting for uncertainty of the data provided and diagnostic variables on assumed or co-retrieved variables (such as further aerosol properties or surface brightness and cloud cover) are also included in the files along the main variables. They help users understand better on per datum level the possible variations of the main variables due to changes in the processing algorithms and their assumptions. A number of variables accounting for uncertainty of the data provided and diagnostic variables on assumed or co-retrieved variables (such as further aerosol properties or surface brightness and cloud cover) are also included in the files along the main variables. They help users understand better on per datum level the possible variations of the main variables due to changes in the processing algorithms and their assumptions. 561 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-era5-pressure-levels https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels reanalysis-era5-pressure-levels ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. ERA5 Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. this article these guidelines Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on pressure levels from 1940 to present". DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid. Horizontal coverage Global Horizontal resolution Reanalysis: 0.25° x 0.25° Mean, spread and members: 0.5° x 0.5° Vertical coverage 1000 hPa to 1 hPa Vertical resolution 37 pressure levels Temporal coverage 1940 to present Temporal resolution Hourly File format GRIB DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid. Projection Regular latitude-longitude grid. Horizontal coverage Global Horizontal coverage Global Horizontal resolution Reanalysis: 0.25° x 0.25° Mean, spread and members: 0.5° x 0.5° Horizontal resolution Reanalysis: 0.25° x 0.25° Mean, spread and members: 0.5° x 0.5° Reanalysis: 0.25° x 0.25° Mean, spread and members: 0.5° x 0.5° Vertical coverage 1000 hPa to 1 hPa Vertical coverage 1000 hPa to 1 hPa Vertical resolution 37 pressure levels Vertical resolution 37 pressure levels Temporal coverage 1940 to present Temporal coverage 1940 to present Temporal resolution Hourly Temporal resolution Hourly File format GRIB File format GRIB MAIN VARIABLES Name Units Description Divergence s-1 This parameter is the horizontal divergence of velocity. It is the rate at which air is spreading out horizontally from a point, per square metre. This parameter is positive for air that is spreading out, or diverging, and negative for the opposite, for air that is concentrating, or converging (convergence). Fraction of cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud (liquid or ice) and varies between zero and one. This parameter is available on multiple levels through the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography. Ozone mass mixing ratio kg kg-1 This parameter is the mass of ozone per kilogram of air. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. Most of the IFS chemical species are archived as mass mixing ratios [kg kg-1]. Potential vorticity K m2 kg-1 s-1 Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere. Relative humidity % This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function. Specific cloud ice water content kg kg-1 This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'. Specific cloud liquid water content kg kg-1 This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific humidity kg kg-1 This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. Specific rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Temperature K This parameter is the temperature in the atmosphere. It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere. U-component of wind m s-1 This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east. A negative sign indicates air moving towards the west. This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind. V-component of wind m s-1 This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north. A negative sign indicates air moving towards the south. This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind. Vertical velocity Pa s-1 This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion. Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values). Vorticity (relative) s-1 This parameter is a measure of the rotation of air in the horizontal, around a vertical axis, relative to a fixed point on the surface of the Earth. On the scale of weather systems, troughs (weather features that can include rain) are associated with anticlockwise rotation (in the northern hemisphere), and ridges (weather features that bring light or still winds) are associated with clockwise rotation. Adding the effect of rotation of the Earth, the Coriolis parameter, to the relative vorticity produces the absolute vorticity. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Divergence s-1 This parameter is the horizontal divergence of velocity. It is the rate at which air is spreading out horizontally from a point, per square metre. This parameter is positive for air that is spreading out, or diverging, and negative for the opposite, for air that is concentrating, or converging (convergence). Divergence s-1 This parameter is the horizontal divergence of velocity. It is the rate at which air is spreading out horizontally from a point, per square metre. This parameter is positive for air that is spreading out, or diverging, and negative for the opposite, for air that is concentrating, or converging (convergence). Fraction of cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud (liquid or ice) and varies between zero and one. This parameter is available on multiple levels through the atmosphere. Fraction of cloud cover Dimensionless This parameter is the proportion of a grid box covered by cloud (liquid or ice) and varies between zero and one. This parameter is available on multiple levels through the atmosphere. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography. Geopotential m2 s-2 This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography. Ozone mass mixing ratio kg kg-1 This parameter is the mass of ozone per kilogram of air. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. Most of the IFS chemical species are archived as mass mixing ratios [kg kg-1]. Ozone mass mixing ratio kg kg-1 This parameter is the mass of ozone per kilogram of air. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. Most of the IFS chemical species are archived as mass mixing ratios [kg kg-1]. Potential vorticity K m2 kg-1 s-1 Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere. Potential vorticity K m2 kg-1 s-1 Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere. Relative humidity % This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function. Relative humidity % This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function. Specific cloud ice water content kg kg-1 This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'. Specific cloud ice water content kg kg-1 This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'. Specific cloud liquid water content kg kg-1 This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific cloud liquid water content kg kg-1 This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Specific humidity kg kg-1 This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. Specific humidity kg kg-1 This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. Specific rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific rain water content kg kg-1 The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Specific snow water content kg kg-1 The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS. Temperature K This parameter is the temperature in the atmosphere. It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere. Temperature K This parameter is the temperature in the atmosphere. It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere. U-component of wind m s-1 This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east. A negative sign indicates air moving towards the west. This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind. U-component of wind m s-1 This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east. A negative sign indicates air moving towards the west. This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind. V-component of wind m s-1 This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north. A negative sign indicates air moving towards the south. This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind. V-component of wind m s-1 This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north. A negative sign indicates air moving towards the south. This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind. Vertical velocity Pa s-1 This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion. Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values). Vertical velocity Pa s-1 This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion. Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values). Vorticity (relative) s-1 This parameter is a measure of the rotation of air in the horizontal, around a vertical axis, relative to a fixed point on the surface of the Earth. On the scale of weather systems, troughs (weather features that can include rain) are associated with anticlockwise rotation (in the northern hemisphere), and ridges (weather features that bring light or still winds) are associated with clockwise rotation. Adding the effect of rotation of the Earth, the Coriolis parameter, to the relative vorticity produces the absolute vorticity. Vorticity (relative) s-1 This parameter is a measure of the rotation of air in the horizontal, around a vertical axis, relative to a fixed point on the surface of the Earth. On the scale of weather systems, troughs (weather features that can include rain) are associated with anticlockwise rotation (in the northern hemisphere), and ridges (weather features that bring light or still winds) are associated with clockwise rotation. Adding the effect of rotation of the Earth, the Coriolis parameter, to the relative vorticity produces the absolute vorticity. 562 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-physics-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MEDSEA_ANALYSISFORECAST_PHY_006_013 Mediterranean Sea Physics Analysis and Forecast Short Description The physical component of the Mediterranean Forecasting System (Med-Physics) is a coupled hydrodynamic-wave model implemented over the whole Mediterranean Basin including tides. The model horizontal grid resolution is 1/24˚ (ca. 4 km) and has 141 unevenly spaced vertical levels. The hydrodynamics are supplied by the Nucleous for European Modelling of the Ocean (NEMO v3.6) and include the representation of tides, while the wave component is provided by Wave Watch-III; the model solutions are corrected by a 3DVAR assimilation scheme (OceanVar) of temperature and salinity vertical profiles and along track satellite Sea Level Anomaly observations. ''Product Citation'': Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… ''DOI (Product)'': https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS7 https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_PHY_006_013_EAS7 563 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-water-level-change-indicators https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-water-level-change-indicators sis-water-level-change-indicators The dataset presents extreme-value, return period, and percentile indicators for coastal sea levels in a European-wide domain. The indicators are computed from tidal dynamics, storm surge and sea level rise data based upon past observational data and future climate projections. This dataset provides an understanding of the European coastal hydrodynamics under the impact of climate change. It provides added value for various coastal sectors and studies such as port, shipping, and coastal management. To compute these indicators, the Deltares Global Tide and Surge Model (GTSM) version 3.0 is used together with regional climate forcing and sea level rise initial conditions. The regional climate forcing employed is the HIRHAM5 model from the Danish Meteorological Institute (DMI), a member of the EURO-CORDEX climate model ensemble, which is downscaled from the global climate model EC-EARTH. By using a regional climate model and a high-resolution forcing field, GTSM is able to produce a more consistent and high quality dataset. In order to assess the impact of climate change, the GTSM model is run for three different climate scenarios: the present climate (labelled 'historical'), and two Representative Concentration Pathway (RCP) scenarios that correspond to an optimistic emission scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century often called the business-as-usual scenario (RCP8.5). Given that the projections of these climate scenarios are based on a single combination of the regional and global climate models, users of these data should take in consideration that there is an inevitable underestimation of the uncertainty associated with this dataset. In addition to the climate scenarios, a reanalysis dataset is computed by forcing GTSM with ERA5 reanalysis. This provides recent historical water-levels that can be used to look at specific (extreme) events in the past. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Time-series for a vector of point locations Horizontal coverage Europe Horizontal resolution Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Vertical coverage Surface Vertical resolution Single level Temporal coverage Statistics for ERA5 reanalysis: from 1979 to 2017 Statistics for historical: from 1977 to 2005 Statistics for RCP8.5: from 2041 to 2070 Statistics for RCP4.5: from 2071 to 2100 Temporal resolution No temporal resolution as the indicators are derived from the daily series and represents statistics over the temporal coverage File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Time-series for a vector of point locations Data type Time-series for a vector of point locations Horizontal coverage Europe Horizontal coverage Europe Horizontal resolution Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Horizontal resolution Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage Statistics for ERA5 reanalysis: from 1979 to 2017 Statistics for historical: from 1977 to 2005 Statistics for RCP8.5: from 2041 to 2070 Statistics for RCP4.5: from 2071 to 2100 Temporal coverage Statistics for ERA5 reanalysis: from 1979 to 2017 Statistics for historical: from 1977 to 2005 Statistics for RCP8.5: from 2041 to 2070 Statistics for RCP4.5: from 2071 to 2100 Statistics for ERA5 reanalysis: from 1979 to 2017 Statistics for historical: from 1977 to 2005 Statistics for RCP8.5: from 2041 to 2070 Statistics for RCP4.5: from 2071 to 2100 Temporal resolution No temporal resolution as the indicators are derived from the daily series and represents statistics over the temporal coverage Temporal resolution No temporal resolution as the indicators are derived from the daily series and represents statistics over the temporal coverage File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Annual highest high water m Annual highest high tide including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Annual lowest low water m Annual lowest low tide including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Annual mean highest high water m Annual average of the highest high tide of each tidal day (25 hour window) including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Annual mean lowest low water m Annual average of the lowest low tide of each tidal day (25 hour window) including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Epoch-mean highest high water m Highest high tide of each tidal day (25 hour window), including mean sea level and sea level rise, averaged over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Epoch-mean lowest low water m Lowest low tide of each tidal day (25 hour window), including mean sea level and sea level rise, averaged over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Highest astronomical tide m Highest astronomical tide over the 30-year period simulated. Lowest astronomical tide m Lowest astronomical tide over the 30-year period simulated. Mean sea level m Mean sea level including sea level rise observed over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Surge level for different percentiles m Storm surge level, defined as the difference between the pure tide and the total water level simulations, for the following percentiles: 10th, 25th, 50th, 75th and 90th. Surge level for different return periods m Storm surge level, defined as the difference between the pure tide and the total water level simulations, for the following return periods: 2, 5, 10, 25, 50 and 100 years. The return period is a standard way of describing the likelihood and severity of an event. It describes the estimated time interval between events of a similar size or intensity. Tidal range m Average tidal range observed over the 30-year period simulated. Total water level for different percentiles m Total water level, including the pure tide and storm surge level, for the following percentiles: 10th, 25th, 50th, 75th and 90th. Total water level for different return periods m Total water level, including tide, surge level and taking future sea level rise into account, for the following return periods: 2, 5, 10, 25, 50 and 100 years. The return period is a standard way of describing the likelihood and severity of an event. It describes the estimated time interval between events of a similar size or intensity. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Annual highest high water m Annual highest high tide including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Annual highest high water m Annual highest high tide including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Annual lowest low water m Annual lowest low tide including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Annual lowest low water m Annual lowest low tide including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Annual mean highest high water m Annual average of the highest high tide of each tidal day (25 hour window) including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Annual mean highest high water m Annual average of the highest high tide of each tidal day (25 hour window) including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Annual mean lowest low water m Annual average of the lowest low tide of each tidal day (25 hour window) including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Annual mean lowest low water m Annual average of the lowest low tide of each tidal day (25 hour window) including mean sea level and sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Epoch-mean highest high water m Highest high tide of each tidal day (25 hour window), including mean sea level and sea level rise, averaged over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Epoch-mean highest high water m Highest high tide of each tidal day (25 hour window), including mean sea level and sea level rise, averaged over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Epoch-mean lowest low water m Lowest low tide of each tidal day (25 hour window), including mean sea level and sea level rise, averaged over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Epoch-mean lowest low water m Lowest low tide of each tidal day (25 hour window), including mean sea level and sea level rise, averaged over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Highest astronomical tide m Highest astronomical tide over the 30-year period simulated. Highest astronomical tide m Highest astronomical tide over the 30-year period simulated. Lowest astronomical tide m Lowest astronomical tide over the 30-year period simulated. Lowest astronomical tide m Lowest astronomical tide over the 30-year period simulated. Mean sea level m Mean sea level including sea level rise observed over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Mean sea level m Mean sea level including sea level rise observed over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Surge level for different percentiles m Storm surge level, defined as the difference between the pure tide and the total water level simulations, for the following percentiles: 10th, 25th, 50th, 75th and 90th. Surge level for different percentiles m Storm surge level, defined as the difference between the pure tide and the total water level simulations, for the following percentiles: 10th, 25th, 50th, 75th and 90th. Surge level for different return periods m Storm surge level, defined as the difference between the pure tide and the total water level simulations, for the following return periods: 2, 5, 10, 25, 50 and 100 years. The return period is a standard way of describing the likelihood and severity of an event. It describes the estimated time interval between events of a similar size or intensity. Surge level for different return periods m Storm surge level, defined as the difference between the pure tide and the total water level simulations, for the following return periods: 2, 5, 10, 25, 50 and 100 years. The return period is a standard way of describing the likelihood and severity of an event. It describes the estimated time interval between events of a similar size or intensity. Tidal range m Average tidal range observed over the 30-year period simulated. Tidal range m Average tidal range observed over the 30-year period simulated. Total water level for different percentiles m Total water level, including the pure tide and storm surge level, for the following percentiles: 10th, 25th, 50th, 75th and 90th. Total water level for different percentiles m Total water level, including the pure tide and storm surge level, for the following percentiles: 10th, 25th, 50th, 75th and 90th. Total water level for different return periods m Total water level, including tide, surge level and taking future sea level rise into account, for the following return periods: 2, 5, 10, 25, 50 and 100 years. The return period is a standard way of describing the likelihood and severity of an event. It describes the estimated time interval between events of a similar size or intensity. Total water level for different return periods m Total water level, including tide, surge level and taking future sea level rise into account, for the following return periods: 2, 5, 10, 25, 50 and 100 years. The return period is a standard way of describing the likelihood and severity of an event. It describes the estimated time interval between events of a similar size or intensity. 564 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-meridional-overturning-circulation-amoc http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries Atlantic Meridional Overturning Circulation AMOC timeseries at 26N from Reanalysis DEFINITION The Atlantic Meridional Overturning strength at 26.5N is obtained by integrating the meridional transport at 26.5 N across the Atlantic basin (zonally) and then doing a cumulative integral in depth. The maximum value in depth is then taken as the strength in Sverdrups (Sv=1x106m3/s). The observations come from the RAPID array (Smeed et al, 2017). CONTEXT The Atlantic Meridional Overturning Circulation (AMOC) transports heat northwards in the Atlantic and plays a key role in regional and global climate (Srokosz et al, 2012). There is a northwards transport in the upper kilometer resulting from northwards flow in the Gulf Stream and wind-driven Ekman transport, and southwards flow in the ocean interior and in deep western boundary currents (Srokosz et al, 2012). The observations have revealed variability at monthly to decadal timescales including a temporary weakening in 2009/10 (McCarthy et al, 2012) and a decrease from 2005-2012 (Smeed et al, 2014; Smeed et al, 2018). Other studies have suggested that this weakening may be a result of variability (Smeed et al, 2014; Jackson et al 2017). CMEMS KEY FINDINGS The AMOC strength exhibits significant variability on many timescales with a temporary weakening in 2009/10. There has been a weakening from 2005-2012 (-0.67 Sv/year, (p=0.03) in the observations and -0.53 Sv/year (p=0.04) in the multi-product mean). The multi-product suggests an earlier increase from 2001-2006 (0.48 Sv/yr, p=0.04), and a weakening in 1998-99, however before this period there is significant uncertainty. This indicates that the changes observed are likely to be variability rather than an ongoing trend (see also Jackson et al, 2018). Note: The key findings will be updated annually in November, in line with OMI evolutions. DOI (product):https://doi.org/10.48670/moi-00232 https://doi.org/10.48670/moi-00232 565 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-waves-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BLKSEA_ANALYSISFORECAST_WAV_007_003 Black Sea Waves Analysis and Forecast Short description: The wave analysis and forecasts for the Black Sea are produced with the third generation spectral wave model WAM Cycle 6. The hindcast and ten days forecast are produced every day on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40° x 1/40°) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74518. The model takes into account depth refraction, wave breaking, and assimilation of satellite wave and wind data. The system provides a hindcast and ten days forecast with one-hourly output once a day. The atmospheric forcing is taken from ECMWF analyses and forecast data. Additionally, WAM is forced by surface currents and sea surface height from BLKSEA_ANALYSISFORECAST_PHY_007_001. Monthly statistics are provided operationally following the CMEMS metrics definitions. Product Citation: Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (Product):https://doi.org/10.25423/cmcc/blksea_analysisforecast_wav_007_003_eas5 https://doi.org/10.25423/cmcc/blksea_analysisforecast_wav_007_003_eas5 566 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/pan-european-very-high-resolution-image-mosaic-2018-true https://land.copernicus.eu/imagery-in-situ/european-image-mosaics/very-high-resolution/very-high-resolution-image-mosaic-2018-true-colour-2m Pan-European Very High Resolution Image Mosaic 2018 - True Colour (2 m), Oct. 2021 The pan-European Very High Resolution (VHR) Image Mosaic 2018 is a seamless mosaic of the VHR 2018 dataset, based on watershed segmentation of image overlaps. The input data consists of a mix of Pleiades, SPOT, DOVE, Kompsat-4, Deimos-2, SuperView, and TripleSat images. The input imagery has been colour balanced against the Sentinel-2 based HR mosaic from 2018. Colour balancing is done through iterative histogram matching, where the first iteration is used to identify clouds and snow, and the second iteration re-balances, with the bright objects masked out. Cloud cover has been minimized through an innovative approach to cloud masking, which relies on automatically identifying and de-prioritizing overly bright areas in the resulting mosaic. Some clouds and snow remain, as all pixels have to have a value, meaning that if no cloud or snow free images were available for a given area, the bright pixels will remain. The mosaic primarily is used as input data in the production of various Copernicus Land Monitoring Service (CLMS) datasets and services, such as land cover maps and high resolution layers on land cover characteristic and can be also useful for CLMS users for visualizations and classifications on land. The input imagery for the creation of the mosaic is provided by ESA. Due to license restrictions, VHR Image Mosaic 2018 is only available as a web service (WMS), and not for data download. 567 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-shipping-consumption-routes https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-shipping-consumption-on-routes sis-shipping-consumption-on-routes The dataset presents ship performance indicators for 80 popular global commercial shipping routes computed using a bespoke Fuel Consumption Model (FCM) developed for the Copernicus Climate Change Service. The purpose of the dataset is to provide information on the weather dependence and seasonality of ship performances with the aim of reducing fuel consumption by optimizing the route selection and ship's sailing speed. A trader or a fleet manager can use this information to estimate the average time and fuel consumption of a journey for a given month together with the uncertainty on meeting the target time of arrival. The seasonal forecast data quantifies the impact of the monthly wind anomalies on the fuel consumption and trip duration. The fuel consumption model comprises two major modules: resistance due to calm water, and resistance due to met-ocean parameters of wind, waves, and currents. For wind (eastward wind speed component, northward wind speed component), and waves (significant wave height, mean wave direction, peak wave period), the fifth generation ECMWF reanalysis for the global climate (ERA5) is used. For ocean currents (eastward current speed component, northward current speed component), monthly mean reanalysis from the ECMWF's ocean reanalysis system 4 (ORAS4) is used. For seasonal forecast products, monthly anomalies from the ECMWF's seasonal prediction system 5 (SEAS5) are used for wind, whilst the monthly mean climatology from ERA5 and ORAS4 is used for waves and currents, respectively. The dataset was produced on behalf of the Copernicus Climate Change Sector using reanalysis data available on the Climate Data Store. DATA DESCRIPTION Data type Point data Projection Latitude-longitude grid Horizontal coverage Global along specified trajectories Horizontal resolution 1.0° x 1.0° (for the underpinning wind, wave and current data) Temporal coverage 1993 to 2019 Temporal resolution Monthly or daily depending of the product type File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency Monthly DATA DESCRIPTION DATA DESCRIPTION Data type Point data Data type Point data Projection Latitude-longitude grid Projection Latitude-longitude grid Horizontal coverage Global along specified trajectories Horizontal coverage Global along specified trajectories Horizontal resolution 1.0° x 1.0° (for the underpinning wind, wave and current data) Horizontal resolution 1.0° x 1.0° (for the underpinning wind, wave and current data) Temporal coverage 1993 to 2019 Temporal coverage 1993 to 2019 Temporal resolution Monthly or daily depending of the product type Temporal resolution Monthly or daily depending of the product type File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency Monthly Update frequency Monthly MAIN VARIABLES Name Units Description Fuel consumption at fixed power kg for fuel oil or m3 for gas. Total fuel consumption of a ship operating at fixed shaft power along a specific route taking into consideration the resistance due to wind, current and waves and engine efficiency. The cumulative fuel consumption at each way point of the route is provided. Fuel consumption at fixed speed kg for fuel oil or m3 for gas. Total fuel consumption of a ship operating at fixed speed over ground along a specific route taking into consideration the resistance due to wind, current and waves and engine efficiency. The cumulative fuel consumption at each way point of the route is provided. Shaft power at fixed speed W Mechanical propulsion power required by the ship to maintain a fixed speed over ground along a specific route taking into consideration the resistance due to calm water, wind, current and waves and engine efficiency. The instantaneous shaft power at each way point of the route is provided. Ship speed at fixed shaft power m s-1 Speed over ground of a ship operating at fixed shaft power along a specific route taking into consideration the resistance due to calm water, wind, current and waves and engine efficiency. The instantaneous speed at each way point of the route is provided. Trip duration at fixed power s Voyage duration of a ship operating at fixed shaft power along a specific route taking into consideration the resistance due to calm water, wind, current and waves and engine efficiency. The cumulative voyage duration at each way point of the route is provided. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Fuel consumption at fixed power kg for fuel oil or m3 for gas. Total fuel consumption of a ship operating at fixed shaft power along a specific route taking into consideration the resistance due to wind, current and waves and engine efficiency. The cumulative fuel consumption at each way point of the route is provided. Fuel consumption at fixed power kg for fuel oil or m3 for gas. Total fuel consumption of a ship operating at fixed shaft power along a specific route taking into consideration the resistance due to wind, current and waves and engine efficiency. The cumulative fuel consumption at each way point of the route is provided. Fuel consumption at fixed speed kg for fuel oil or m3 for gas. Total fuel consumption of a ship operating at fixed speed over ground along a specific route taking into consideration the resistance due to wind, current and waves and engine efficiency. The cumulative fuel consumption at each way point of the route is provided. Fuel consumption at fixed speed kg for fuel oil or m3 for gas. Total fuel consumption of a ship operating at fixed speed over ground along a specific route taking into consideration the resistance due to wind, current and waves and engine efficiency. The cumulative fuel consumption at each way point of the route is provided. Shaft power at fixed speed W Mechanical propulsion power required by the ship to maintain a fixed speed over ground along a specific route taking into consideration the resistance due to calm water, wind, current and waves and engine efficiency. The instantaneous shaft power at each way point of the route is provided. Shaft power at fixed speed W Mechanical propulsion power required by the ship to maintain a fixed speed over ground along a specific route taking into consideration the resistance due to calm water, wind, current and waves and engine efficiency. The instantaneous shaft power at each way point of the route is provided. Ship speed at fixed shaft power m s-1 Speed over ground of a ship operating at fixed shaft power along a specific route taking into consideration the resistance due to calm water, wind, current and waves and engine efficiency. The instantaneous speed at each way point of the route is provided. Ship speed at fixed shaft power m s-1 Speed over ground of a ship operating at fixed shaft power along a specific route taking into consideration the resistance due to calm water, wind, current and waves and engine efficiency. The instantaneous speed at each way point of the route is provided. Trip duration at fixed power s Voyage duration of a ship operating at fixed shaft power along a specific route taking into consideration the resistance due to calm water, wind, current and waves and engine efficiency. The cumulative voyage duration at each way point of the route is provided. Trip duration at fixed power s Voyage duration of a ship operating at fixed shaft power along a specific route taking into consideration the resistance due to calm water, wind, current and waves and engine efficiency. The cumulative voyage duration at each way point of the route is provided. 568 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-waves-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BLKSEA_MULTIYEAR_WAV_007_006 Black Sea Waves Reanalysis Short description: The wave reanalysis for the Black Sea is produced with the third generation spectral wave model WAM Cycle 6. The reanalysis is produced on the HPC at Helmholtz-Zentrum Hereon and is continuously updated every six months, covering the period since January 1979. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40° x 1/40°) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74518. The model takes into account wave breaking and assimilation of Jason satellite wave and wind data. The system provides one-hourly output and the atmospheric forcing is taken from ECMWF ERA5 data. Product Citation: Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (Product):https://doi.org/10.25423/cmcc/blksea_multiyear_wav_007_006_eas4 https://doi.org/10.25423/cmcc/blksea_multiyear_wav_007_006_eas4 569 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/high-resolution-snow-and-ice-monitoring-wetdry-snow https://cryo.land.copernicus.eu/finder/ High Resolution Snow and Ice Monitoring: Wet/Dry Snow (raster 60m) The Copernicus Wet/Dry Snow (WDS) product is generated in near real-time for the entire EEA38 and the United Kingdom, based on radar satellite data from the Sentinel-1 constellation. The product differentiates the snow state conditions within the snow mask defined by the FSCTOC information (snow fraction at the top of canopy - see the Copernicus Fractional Snow Cover product) with a spatial resolution of 60 m x 60 m. In other words, it provides a binary discrimination of wet and dry snow, identifying patchy snow or snow free areas. The WDS product is distributed in raster files covering an area of 110 km by 110 km with a pixel size of 60 m by 60 m in UTM/WGS84 projection, which corresponds to the Sentinel-2 input L1C product tile. Each product is composed of two separate GeoTIFF files corresponding to the different layers of the product (the snow state classification -SSC- and the associated quality layer -QCSSC-) and a metadata file. The WDS is one of the products of the pan-European High-Resolution Snow & Ice service (HR-S&I), which are provided at high spatial resolution (20 m x 20 m and 60 m x 60 m), from the Sentinel-2 and Sentinel-1 constellations data from September 1, 2016 onwards. Visit https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… to get more information on the different HR-S&I products (Snow products : FSC, WDS, SWS, GFSC, and PSA. Ice products : RLIE and ARLIE). https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… 570 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-situ-near-real-time-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_GLO_PHYBGCWAV_DISCRETE_MYNRT_013_030 Global Ocean- In-Situ Near-Real-Time Observations Short description: Global Ocean - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average. Data are collected mainly through global networks (Argo, OceanSites, GOSUD, EGO) and through the GTS DOI (product) :https://doi.org/10.48670/moi-00036 https://doi.org/10.48670/moi-00036 571 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/insitu-observations-surface-land https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-observations-surface-land insitu-observations-surface-land This set of data holdings provides access to data collected from land surface meteorological observations across the globe. Data are available at the observational level and also at daily and monthly aggregations. Data have been collated and harmonised and quality control checks have been performed, but no attempt has been made to assess for potential biases. Data are provided for a range of commonly observed variables. Surface meteorological observations taken by a broad variety of organisations, including but not limited to National Meteorological Services, are being collated, reconciled, and harmonised. This work provides data that can underpin the development of reanalyses products as well as the production of gridded products and data services. Work is ongoing to prepare and analyse several hundred sources secured to date and new sources continue to accrue. Documentation on sources secured to date can be found in the inventory information provided in the documentation tab. Users with new sources are invited to upload these via the data deposition service for ingestion and consideration. Sources are available in a broad range of formats and frequently individual series have been shared multiple times. Steps have been undertaken to harmonise data formats and reconcile data sources to yield the longest possible individiual station records. data deposition service Station period-of-record varies on a station-by-station basis with most stations starting after 1950, but many stations extending much further back in time. Daily and monthly aggregations extend further back in time than sub-daily records in general. Periodic updates and reissues shall refresh historical holdings availability and will also build on aspects such as quality checking and flagging of data. Each such release will be accompanied by a version number increment and a reissue of the product user guide. Updates for recent observations are in process of being provided at a latency of 1-2 days for those stations that are operational and sharing data across the WMO Information System, starting with updates to the daily holdings. Data are available for several commonly observed variables as described in the main variables table. Note that which variables are available varies by the chosen timescale of data aggregation and by station. To aid users an inventory of stations by name, location, start and end date, and variables available is provided in the documentation page differentiated by timescale. Attributes are described in the related-variables tables and users can choose whether to receive all attributes or solely essential attributes. Data are downloaded as comma-seperated values (CSV) files organised as one observations of one variable per row. Users should ensure an appropriate assessment of long-term data quality prior to use in those applications which require consideration of such aspects. More details about the land meteorological station holdings are available in the product user guide, and details around data formatting can be found in the common data model documentation, both of which can be found in the documentation section. This work is being completed on behalf of C3S in sustained collaboration with colleagues at NOAA's National Centres for Environmental Information who are the WMO designated World Data Centre for meteorology. NOAA's National Centres for Environmental Information DATA DESCRIPTION Data type Point observation Horizontal coverage Global land (75,247 daily stations, 72,667 monthly stations, 19,587 sub-daily stations) Horizontal resolution Variable Vertical resolution Surface Temporal coverage 1755 to 2020, start date and period of record is station dependent Temporal resolution Sub-daily, daily, monthly File format CSV Versions Current version - 1 Update frequency Yearly DATA DESCRIPTION DATA DESCRIPTION Data type Point observation Data type Point observation Horizontal coverage Global land (75,247 daily stations, 72,667 monthly stations, 19,587 sub-daily stations) Horizontal coverage Global land (75,247 daily stations, 72,667 monthly stations, 19,587 sub-daily stations) Horizontal resolution Variable Horizontal resolution Variable Vertical resolution Surface Vertical resolution Surface Temporal coverage 1755 to 2020, start date and period of record is station dependent Temporal coverage 1755 to 2020, start date and period of record is station dependent Temporal resolution Sub-daily, daily, monthly Temporal resolution Sub-daily, daily, monthly File format CSV File format CSV Versions Current version - 1 Versions Current version - 1 Update frequency Yearly Update frequency Yearly MAIN VARIABLES Name Units Description Accumulated precipitation mm Accumulated precipitation over specified period. (Available at frequencies: daily, monthly) Air pressure Pa Pressure of air column at the height of station. (Available at frequencies: sub-daily) Air pressure at sea level Pa Sea level means mean sea level, which is close to the geoid in sea areas. Air pressure at sea level is the quantity often abbreviated as MSLP or PMSL. (Available at frequencies: sub-daily) Air temperature K Air temperature is the bulk temperature of the air, not the surface (skin) temperature. (Available at frequencies: sub-daily, daily, monthly) Dew point temperature K Dew point temperature is the temperature at which a parcel of air reaches saturation upon being cooled at constant pressure and specific humidity. (Available at frequencies: sub-daily) Fresh snow mm New snow accumulated between consecutive observations or over reporting period. (Available at frequencies: daily, monthly) Snow depth cm Vertical distance from the snow surface to the underlying surface (ground, glacier ice or sea ice). (Available at frequencies: daily) Snow water equivalent mm Surface snow amount. (Available at frequencies: daily) Wind from direction Degrees Direction from which the wind is blowing. (Available at frequencies: sub-daily, daily) Wind speed m s-1 Speed is the magnitude of velocity. Wind is defined as a two-dimensional (horizontal) air velocity vector, with no vertical component. (Vertical motion in the atmosphere has the standard name upward air velocity.) The wind speed is the magnitude of the wind velocity. (Available at frequencies: sub-daily, daily, monthly) MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Accumulated precipitation mm Accumulated precipitation over specified period. (Available at frequencies: daily, monthly) Accumulated precipitation mm Accumulated precipitation over specified period. (Available at frequencies: daily, monthly) Air pressure Pa Pressure of air column at the height of station. (Available at frequencies: sub-daily) Air pressure Pa Pressure of air column at the height of station. (Available at frequencies: sub-daily) Air pressure at sea level Pa Sea level means mean sea level, which is close to the geoid in sea areas. Air pressure at sea level is the quantity often abbreviated as MSLP or PMSL. (Available at frequencies: sub-daily) Air pressure at sea level Pa Sea level means mean sea level, which is close to the geoid in sea areas. Air pressure at sea level is the quantity often abbreviated as MSLP or PMSL. (Available at frequencies: sub-daily) Air temperature K Air temperature is the bulk temperature of the air, not the surface (skin) temperature. (Available at frequencies: sub-daily, daily, monthly) Air temperature K Air temperature is the bulk temperature of the air, not the surface (skin) temperature. (Available at frequencies: sub-daily, daily, monthly) Dew point temperature K Dew point temperature is the temperature at which a parcel of air reaches saturation upon being cooled at constant pressure and specific humidity. (Available at frequencies: sub-daily) Dew point temperature K Dew point temperature is the temperature at which a parcel of air reaches saturation upon being cooled at constant pressure and specific humidity. (Available at frequencies: sub-daily) Fresh snow mm New snow accumulated between consecutive observations or over reporting period. (Available at frequencies: daily, monthly) Fresh snow mm New snow accumulated between consecutive observations or over reporting period. (Available at frequencies: daily, monthly) Snow depth cm Vertical distance from the snow surface to the underlying surface (ground, glacier ice or sea ice). (Available at frequencies: daily) Snow depth cm Vertical distance from the snow surface to the underlying surface (ground, glacier ice or sea ice). (Available at frequencies: daily) Snow water equivalent mm Surface snow amount. (Available at frequencies: daily) Snow water equivalent mm Surface snow amount. (Available at frequencies: daily) Wind from direction Degrees Direction from which the wind is blowing. (Available at frequencies: sub-daily, daily) Wind from direction Degrees Direction from which the wind is blowing. (Available at frequencies: sub-daily, daily) Wind speed m s-1 Speed is the magnitude of velocity. Wind is defined as a two-dimensional (horizontal) air velocity vector, with no vertical component. (Vertical motion in the atmosphere has the standard name upward air velocity.) The wind speed is the magnitude of the wind velocity. (Available at frequencies: sub-daily, daily, monthly) Wind speed m s-1 Speed is the magnitude of velocity. Wind is defined as a two-dimensional (horizontal) air velocity vector, with no vertical component. (Vertical motion in the atmosphere has the standard name upward air velocity.) The wind speed is the magnitude of the wind velocity. (Available at frequencies: sub-daily, daily, monthly) RELATED VARIABLES Name Units Description Data policy licence None Data policy licence (presently either open or WMO Resolution 40) (optional metadata) Date time None Timestamp for observation (basic metadata) specified as YYYY-MM-DD HH:MM:00+00 Date time meaning None Whether the date and time of observation given denotes start, middle or end of period of observation (optional metadata) Height above surface m Altitude of the station as reported (if known) (basic metadata) Latitude Degrees north Observation latitude, bounded between -90 and 90 (basic metadata) Longitude Degrees east Observation longitude bounded between -180 and 180 (basic metadata) Observation duration Seconds The period over which the observation was taken (basic metadata) Observation id None Unique identifier associated with each individual observation consisting of the station identifier, station configuration and date time stamp (basic metadata) Observation value As specified in the Units column Value of measurement Observed variable None The variable being observed / measured (basic metadata) Platform type None The generic type of observing system (optional metadata) Primary station id None Identifier used within C3S and by NOAA NCEI to uniquely identify the station (hopefully to be replaced with WIGOS Station identifiers in collaboration with the World Meteorological Organization in the medium term) (basic metadata) Quality flag None Integer flag system to reflect quality assessment of the observation. See Land User Guide or Common Data Model documentation for further particulars (basic metadata) Report type None The type of report e.g. synoptic, METAR etc. (optional metadata) Source id None The unique source identifier associated with the observation to permit traceability and for citation and acknowledgement purposes Station name None Station primary name assigned (station may also have existed under other names) (basic metadata) Station type None Type of observing station (if known) (optional metadata) Units None Unit of the measurement Value significance None Whether minimum, maximum, mean or sum (basic metadata) RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Data policy licence None Data policy licence (presently either open or WMO Resolution 40) (optional metadata) Data policy licence None Data policy licence (presently either open or WMO Resolution 40) (optional metadata) Date time None Timestamp for observation (basic metadata) specified as YYYY-MM-DD HH:MM:00+00 Date time None Timestamp for observation (basic metadata) specified as YYYY-MM-DD HH:MM:00+00 Date time meaning None Whether the date and time of observation given denotes start, middle or end of period of observation (optional metadata) Date time meaning None Whether the date and time of observation given denotes start, middle or end of period of observation (optional metadata) Height above surface m Altitude of the station as reported (if known) (basic metadata) Height above surface m Altitude of the station as reported (if known) (basic metadata) Latitude Degrees north Observation latitude, bounded between -90 and 90 (basic metadata) Latitude Degrees north Observation latitude, bounded between -90 and 90 (basic metadata) Longitude Degrees east Observation longitude bounded between -180 and 180 (basic metadata) Longitude Degrees east Observation longitude bounded between -180 and 180 (basic metadata) Observation duration Seconds The period over which the observation was taken (basic metadata) Observation duration Seconds The period over which the observation was taken (basic metadata) Observation id None Unique identifier associated with each individual observation consisting of the station identifier, station configuration and date time stamp (basic metadata) Observation id None Unique identifier associated with each individual observation consisting of the station identifier, station configuration and date time stamp (basic metadata) Observation value As specified in the Units column Value of measurement Observation value As specified in the Units column Value of measurement Observed variable None The variable being observed / measured (basic metadata) Observed variable None The variable being observed / measured (basic metadata) Platform type None The generic type of observing system (optional metadata) Platform type None The generic type of observing system (optional metadata) Primary station id None Identifier used within C3S and by NOAA NCEI to uniquely identify the station (hopefully to be replaced with WIGOS Station identifiers in collaboration with the World Meteorological Organization in the medium term) (basic metadata) Primary station id None Identifier used within C3S and by NOAA NCEI to uniquely identify the station (hopefully to be replaced with WIGOS Station identifiers in collaboration with the World Meteorological Organization in the medium term) (basic metadata) Quality flag None Integer flag system to reflect quality assessment of the observation. See Land User Guide or Common Data Model documentation for further particulars (basic metadata) Quality flag None Integer flag system to reflect quality assessment of the observation. See Land User Guide or Common Data Model documentation for further particulars (basic metadata) Report type None The type of report e.g. synoptic, METAR etc. (optional metadata) Report type None The type of report e.g. synoptic, METAR etc. (optional metadata) Source id None The unique source identifier associated with the observation to permit traceability and for citation and acknowledgement purposes Source id None The unique source identifier associated with the observation to permit traceability and for citation and acknowledgement purposes Station name None Station primary name assigned (station may also have existed under other names) (basic metadata) Station name None Station primary name assigned (station may also have existed under other names) (basic metadata) Station type None Type of observing station (if known) (optional metadata) Station type None Type of observing station (if known) (optional metadata) Units None Unit of the measurement Units None Unit of the measurement Value significance None Whether minimum, maximum, mean or sum (basic metadata) Value significance None Whether minimum, maximum, mean or sum (basic metadata) 572 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-biogeochemistry-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=ARCTIC_ANALYSISFORECAST_BGC_002_004 Arctic Ocean Biogeochemistry Analysis and Forecast Short description: The operational TOPAZ5-ECOSMO Arctic Ocean system uses the ECOSMO biological model coupled online to the TOPAZ5 physical model planned for a future update of the ARCTIC_ANALYSIS_FORECAST_PHYS_002_001_a physical forecast. It is run daily to provide 10 days of forecast of 3D biogeochemical variables ocean. The coupling is done by the FABM framework. Coupling to a biological ocean model provides a description of the evolution of basic biogeochemical variables. The output consists of daily mean fields interpolated onto a standard grid and 40 fixed levels in NetCDF4 CF format. Variables include 3D fields of nutrients (nitrate, phosphate, silicate), phytoplankton and zooplankton biomass, oxygen, chlorophyll, primary productivity, carbon cycle variables (pH, dissolved inorganic carbon and surface partial CO2 pressure in seawater, carbon export) and light attenuation coefficient. Surface Chlorophyll-a from satellite ocean colour is assimilated every week and projected downwards using the Uitz et al. (2006) method. A new 10-day forecast is produced daily using the previous day's forecast and the most up-to-date prognostic forcing fields. Output products have 6.25 km resolution at the North Pole (equivalent to 1/8 deg) on a stereographic projection. See the Product User Manual for the exact projection parameters. DOI (product) :https://doi.org/10.48670/moi-00003 https://doi.org/10.48670/moi-00003 573 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-methane https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-methane satellite-methane This dataset provides observations of atmospheric methane (CH4) amounts obtained from observations collected by several current and historical satellite instruments. Methane is a naturally occurring Greenhouse Gas (GHG), but one whose abundance has been increased substantially above its pre-industrial value of some 720 ppb by human activities, primarily because of agricultural emissions (e.g., rice production, ruminants) and fossil fuel production and use. A clear annual cycle is largely due to seasonal wetland emissions. Atmospheric methane abundance is indirectly observed by various satellite instruments. These instruments measure spectrally resolved near-infrared and infrared radiation reflected or emitted by the Earth and its atmosphere. In the measured signal, molecular absorption signatures from methane and constituent gasses can be identified. It is through analysis of those absorption lines in these radiance observations that the averaged methane abundance in the sampled atmospheric column can be determined. The software used to analyse the absorption lines and determine the methane concentration in the sampled atmospheric column is referred to as the retrieval algorithm. For this dataset, methane abundances have been determined by applying several algorithms to different satellite instruments. The data set consists of 2 types of products: column-averaged mixing ratios of CH4, denoted XCH4 mid-tropospheric CH4 columns. column-averaged mixing ratios of CH4, denoted XCH4 mid-tropospheric CH4 columns. The XCH4 products have been retrieved from SCIAMACHY/ENVISAT, TANSO-FTS/GOSA Tand TANSO-FTS-2/GOSAT-2. The mid-tropospheric CH4 product has been retrieved from the IASI instruments onboard the Metop satellite series. The XCH4 products are available as Level 2 (L2) products (satellite orbit tracks) and as Level 3 (L3) product (gridded). The L2 products are available as individual sensor products (SCIAMACHY: WFMD and IMAP algorithms; GOSAT: OCFP, OCPR, SRFP and SRPR algorithms) and as a multi-sensor merged product (EMMA algorithm). The L3 XCH4 product is provided in OBS4MIPS format. The IASI products are available as L2 products generated with the NLIS algorithm. This data set is updated on a yearly basis, with each update cycle adding (if required) a new data version for the entire period, up to one year behind real time. This dataset is produced on behalf of C3S with the exception of the SCIAMACHY L2 products that were generated in the framework of the GHG-CCI project of the European Space Agency (ESA) Climate Change Initiative (CCI). DATA DESCRIPTION Data type Level 2 (L2): Along satellite orbit tracks Level 3 (L3): Gridded Horizontal coverage Between approximately 70°N and 70°S Horizontal resolution SCIAMACHY (L2): 30x60 km2 TANSO and TANSO-2 (L2): 10 km (diameter) IASI (L2): 12 km (diameter) L3 products: 5° x 5° Vertical coverage SCIAMACHY, TANSO, TANSO-2 and L3 products: Total atmospheric column AIRS and IASI: Mid-troposphere Vertical resolution Single layer Temporal coverage SCIAMACHY (L2): October 2002 until April 2012 IASI (L2): July 2007 until December 2021 TANSO (L2): April 2009 until December 2021 TANSO-2 (L2): Februaray 2019 until December 2021 L3 products: January 2003 until March 2021 Temporal resolution L3 products: Monthly L2 products are provided as observation footprints along satellite orbits at the sampling frequency of the sensor. Please refer to the documentation for more details. File format NetCDF4 Conventions Climate and Forecast Metadata Convention v1.6 (CF-1.6) Versions Multiple algorithm and file versions are available for several sensor/algorithm combinations. Users are advised to use the latest version available. Update frequency Yearly DATA DESCRIPTION DATA DESCRIPTION Data type Level 2 (L2): Along satellite orbit tracks Level 3 (L3): Gridded Data type Level 2 (L2): Along satellite orbit tracks Level 3 (L3): Gridded Level 2 (L2): Along satellite orbit tracks Level 3 (L3): Gridded Horizontal coverage Between approximately 70°N and 70°S Horizontal coverage Between approximately 70°N and 70°S Horizontal resolution SCIAMACHY (L2): 30x60 km2 TANSO and TANSO-2 (L2): 10 km (diameter) IASI (L2): 12 km (diameter) L3 products: 5° x 5° Horizontal resolution SCIAMACHY (L2): 30x60 km2 TANSO and TANSO-2 (L2): 10 km (diameter) IASI (L2): 12 km (diameter) L3 products: 5° x 5° SCIAMACHY (L2): 30x60 km2 TANSO and TANSO-2 (L2): 10 km (diameter) IASI (L2): 12 km (diameter) L3 products: 5° x 5° Vertical coverage SCIAMACHY, TANSO, TANSO-2 and L3 products: Total atmospheric column AIRS and IASI: Mid-troposphere Vertical coverage SCIAMACHY, TANSO, TANSO-2 and L3 products: Total atmospheric column AIRS and IASI: Mid-troposphere SCIAMACHY, TANSO, TANSO-2 and L3 products: Total atmospheric column AIRS and IASI: Mid-troposphere Vertical resolution Single layer Vertical resolution Single layer Temporal coverage SCIAMACHY (L2): October 2002 until April 2012 IASI (L2): July 2007 until December 2021 TANSO (L2): April 2009 until December 2021 TANSO-2 (L2): Februaray 2019 until December 2021 L3 products: January 2003 until March 2021 Temporal coverage SCIAMACHY (L2): October 2002 until April 2012 IASI (L2): July 2007 until December 2021 TANSO (L2): April 2009 until December 2021 TANSO-2 (L2): Februaray 2019 until December 2021 L3 products: January 2003 until March 2021 SCIAMACHY (L2): October 2002 until April 2012 IASI (L2): July 2007 until December 2021 TANSO (L2): April 2009 until December 2021 TANSO-2 (L2): Februaray 2019 until December 2021 L3 products: January 2003 until March 2021 Temporal resolution L3 products: Monthly L2 products are provided as observation footprints along satellite orbits at the sampling frequency of the sensor. Please refer to the documentation for more details. Temporal resolution L3 products: Monthly L2 products are provided as observation footprints along satellite orbits at the sampling frequency of the sensor. Please refer to the documentation for more details. L3 products: Monthly L2 products are provided as observation footprints along satellite orbits at the sampling frequency of the sensor. Please refer to the documentation for more details. File format NetCDF4 File format NetCDF4 Conventions Climate and Forecast Metadata Convention v1.6 (CF-1.6) Conventions Climate and Forecast Metadata Convention v1.6 (CF-1.6) Versions Multiple algorithm and file versions are available for several sensor/algorithm combinations. Users are advised to use the latest version available. Versions Multiple algorithm and file versions are available for several sensor/algorithm combinations. Users are advised to use the latest version available. Update frequency Yearly Update frequency Yearly MAIN VARIABLES Name Units Description Column-average dry-air mole fraction of atmospheric methane (XCH4) ppb Average molar mixing ratio (or mole fraction in micro mole methane (CH4) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CH4 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCH4 indicates that the reported quantity is a "mole fraction". Mid-tropospheric columns of atmospheric methane (CH4) ppb Average CH4 mixing ratio of the mid-troposphere. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Column-average dry-air mole fraction of atmospheric methane (XCH4) ppb Average molar mixing ratio (or mole fraction in micro mole methane (CH4) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CH4 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCH4 indicates that the reported quantity is a "mole fraction". Column-average dry-air mole fraction of atmospheric methane (XCH4) ppb Average molar mixing ratio (or mole fraction in micro mole methane (CH4) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CH4 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCH4 indicates that the reported quantity is a "mole fraction". Average molar mixing ratio (or mole fraction in micro mole methane (CH4) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CH4 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCH4 indicates that the reported quantity is a "mole fraction". Mid-tropospheric columns of atmospheric methane (CH4) ppb Average CH4 mixing ratio of the mid-troposphere. Mid-tropospheric columns of atmospheric methane (CH4) ppb Average CH4 mixing ratio of the mid-troposphere. RELATED VARIABLES The optimal estimation inversion algorithms used to compute the column average CH4 are based on a number of atmospheric variables like pressure, temperature, water vapour, scattering by aerosols and clouds, spectral albedo, including initial a-priori values and averaging kernels as well as estimates of uncertainty on the values of CH4. Depending on the sensor and on the algorithm, a number of these variables are also included in the files along the main variable CH4. RELATED VARIABLES RELATED VARIABLES The optimal estimation inversion algorithms used to compute the column average CH4 are based on a number of atmospheric variables like pressure, temperature, water vapour, scattering by aerosols and clouds, spectral albedo, including initial a-priori values and averaging kernels as well as estimates of uncertainty on the values of CH4. Depending on the sensor and on the algorithm, a number of these variables are also included in the files along the main variable CH4. The optimal estimation inversion algorithms used to compute the column average CH4 are based on a number of atmospheric variables like pressure, temperature, water vapour, scattering by aerosols and clouds, spectral albedo, including initial a-priori values and averaging kernels as well as estimates of uncertainty on the values of CH4. Depending on the sensor and on the algorithm, a number of these variables are also included in the files along the main variable CH4. 574 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/start-season-date-2017-present-raster-10-m-europe-yearly https://www.wekeo.eu/data?view=viewer&t=1577905116279&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1QSEVOT0xPR1ktQU5ELVBST0RVQ1RJVklUWS1QQVJBTUVURVJTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVlBQX1NPU0RfU0VBU09OMV8xME0iLCJ6SW5kZXgiOjgwfV0%3D&initial=1 Start-of-season Date 2017-present (raster 10 m), Europe, yearly, Sept. 2021 The Start-of-Season Date (SOSD), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Start-of-Season Date (SOSD) marks the date when the vegetation growing season starts in the time profile of the Plant Phenology Index (PPI). The start-of-season occurs, by definition, when the PPI value reaches 25% of the season amplitude during the green-up period. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Start-of-Season Date is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-m… https://land.copernicus.eu/user-corner/technical-library/product-user-m… A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The SOSD dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year. 575 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/high-resolution-snow-and-ice-monitoring-sar-wet-snow https://cryo.land.copernicus.eu/finder/ High Resolution Snow and Ice Monitoring: SAR Wet Snow (raster 60m) The Copernicus SAR Wet Snow (SWS) product is generated in near real-time for the entire EEA38 and the United Kingdom, based on radar satellite data from the Sentinel-1 constellation. The product provides binary information on the wet snow extent and the snow free or patchy snow or dry snow extent in high mountain areas with a spatial resolution of 60 m x 60 m. The SWS product is distributed in raster files covering an area of 110 km by 110 km with a pixel size of 60 m by 60 m in UTM/WGS84 projection, which corresponds to the Sentinel-2 input L1C product tile. It is available in several mountainous regions in Iceland, the Pyrenees, the Alps, Eastern Türkiye and Scandinavia. Each product is composed of two separate GeoTIFF files corresponding to the different layers of the product, and a metadata file.The WSM (Wet Snow classification in high Mountainous areas) layer provides the wet snow extent derived from S1 Level 1 GRD data over high montainous areas, and the QCWSM (Quality) layer provides the per-pixel accuracy information associated with the WSM layer for all pixels with detected snow areas. The SWS is one of the products of the pan-European High-Resolution Snow & Ice service (HR-S&I), which are provided at high spatial resolution (20 m x 20 m and 60 m x 60 m), from the Sentinel-2 and Sentinel-1 constellations data from September 1, 2016 onwards. Visit https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… to get more information on the different HR-S&I products (Snow products : FSC, WDS, SWS, GFSC, and PSA. Ice products: RLIE and ARLIE). https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… 576 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-health-birch-pollen-season-onset-current-climate https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-health-birch-pollen-season-onset-current-climate app-health-birch-pollen-season-onset-current-climate This application presents the onset date of birch pollen for the period 2010 to the last full year of ERA5 reanalysis available. Birch pollen is a dominant tree pollen in Northern and Central Europe and it is one of the most common airborne allergens in the spring. Birch pollen is linked to health conditions such as allergies and, possibly, asthma. Users can visualize the Birch Pollen onset data, which is the number of days since 1st of January, at national level and regional level. This application focuses on the onset of the pollen season, which is solely determined by a meteorological variable from ERA5; hourly near-surface air temperature. Using the SILAM approach (the System for Integrated modeLling of Atmospheric composition), the starting date of the birch season is defined as the first day on which cumulative temperature sum exceeds a defined threshold. Such thresholds vary from location to location and are based on a comparison with measurement data. Clicking on a highlighted country or administrative region will open a window with a focused view on that region. The focused view contains: A series of map plots of the birch pollen onset date, one for each available year. A time series which displays the mean (with +/- standard deviation) and the mean of the 10th, 50th and 90th percentiles for the entire region. Users can select which statistics to view by clicking on them in the legend. A series of map plots of the birch pollen onset date, one for each available year. A time series which displays the mean (with +/- standard deviation) and the mean of the 10th, 50th and 90th percentiles for the entire region. Users can select which statistics to view by clicking on them in the legend. User-selectable parameters User-selectable parameters Year: from 2010 to last full year of ERA5 reanalysis Year: from 2010 to last full year of ERA5 reanalysis Year More details about the products are given in the Documentation section. INPUT VARIABLES Name Units Description Source Air temperature K 2 m air temperature. ERA5 Temperature sum thresholds K Temperature sum threshold as calculated in Sofiev et al. (2013) Int. J. Biometeorol. Brokered externally INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Air temperature K 2 m air temperature. ERA5 Air temperature K 2 m air temperature. ERA5 ERA5 Temperature sum thresholds K Temperature sum threshold as calculated in Sofiev et al. (2013) Int. J. Biometeorol. Brokered externally Temperature sum thresholds K Temperature sum threshold as calculated in Sofiev et al. (2013) Int. J. Biometeorol. Brokered externally 577 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/surface-soil-moisture-2014-present-raster-1-km-europe https://land.copernicus.eu/global/products/ssm Surface Soil Moisture 2014-present (raster 1 km), Europe, daily - version 1 Surface Soil Moisture (SSM) is the relative water content of the top few centimetres soil, describing how wet or dry the soil is in its topmost layer, expressed in percent saturation. It is measured by satellite radar sensors and allows insights in local precipitation impacts and soil conditions. SSM is a key driver of water and heat fluxes between the ground and the atmosphere, regulating air temperature and humidity. Moreover, in its role as water supply, it is vital to vegetation health. Vice versa, SSM is very sensitive to external forcing in the form of precipitation, temperature, solar irradiation, humidity, and wind. SSM is thus both an integrator of climatic conditions and a driver of local weather and climate, and plays a major role in global water-, energy- and carbon- cycles. Knowledge on the dynamics of soil moisture is important in the understanding of processes in many environmental and socio-economic fields, e.g., its impact on vegetation vitality, crop yield, droughts or exposure to flood threats. 578 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/slope-green-period-2017-present-raster-10-m-europe-yearly https://www.wekeo.eu/data?view=viewer&t=1577905116279&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1QSEVOT0xPR1ktQU5ELVBST0RVQ1RJVklUWS1QQVJBTUVURVJTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVlBQX0xTTE9QRV9TRUFTT04xXzEwTSIsInpJbmRleCI6ODB9XQ%3D%3D&initial=1 Slope of the Green-up Period 2017-present (raster 10 m), Europe, yearly, Sept. 2021 The Slope of the green-up period (Left Slope, LSLOPE), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The slope of the green-up period (LSLOPE) expresses the rate of change in the values of the Plant Phenology Index (PPI) at the day when the vegetation growing season starts. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The green-up period slope is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-m… https://land.copernicus.eu/user-corner/technical-library/product-user-m… A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The LSLOPE dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year. 579 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-c3s-snow-precip-ratio https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-c3s-snow-precip-ratio app-c3s-snow-precip-ratio This application explores how the percentage of winter (December-January) precipitation that falls as snow might change over the coming decades, driven by snowfall and rainfall data from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Recent studies have suggested that rain may replace snow as the most common precipitation in the Arctic – possibly within the next 50 years. This could have profound implications, from accelerating global warming and sea level rise to melting permafrost and sinking roads. This application explores whether Europe may see a similar shift in winter precipitation from snow to rain, particularly over the Christmas period. This application visualises projections of the percentage of total precipitation which will fall as snow over the Christmas period, underpinned by multi-model projections of monthly mean (December-January) snowfall flux and total precipitation from 12 CMIP6 models for the years 2015-2100. Three different Shared Socioeconomic Pathways (SSPs) have been included to account for a range of different end-of-century climate change outcomes: SSP1-2.6 (warming limited to 2°C by 2100), SSP2-4.5 (warming limited to 3°C by 2100) and SSP5-8.5 (a worst-case scenario). The proportion of total precipitation that reaches the Earth's surface as snow has been calculated by dividing the snowfall flux by total precipitation flux, and applying a 10-year rolling average in order to account for uncertainty in annual variations in the projections. The application interface comprises an interactive map showing the percentage of precipitation falling as snow over the Christmas period for the years 2020-2095, averaged over Nomenclature of Territorial Units for Statistics (NUTS) level 2 regions. Selecting a NUTS region generates a region-specific breakdown of the projected percentage of December-January precipitation that will fall as snow: a graph comparing the multi-model mean and uncertainty for the three different SSPs described above; "snow stripes" - a visualisation of the annual December-January snowfall as a proportion of total precipitation, on a scale of white-to-dark green representing the years with the highest proportion of precipitation falling as snow (white) and the lowest proportion of precipitation falling as snow (dark green) in the selected region. a graph comparing the multi-model mean and uncertainty for the three different SSPs described above; "snow stripes" - a visualisation of the annual December-January snowfall as a proportion of total precipitation, on a scale of white-to-dark green representing the years with the highest proportion of precipitation falling as snow (white) and the lowest proportion of precipitation falling as snow (dark green) in the selected region. INPUT VARIABLES Name Units Description Source Precipitation kg m-2 s-1 The sum of liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. CMIP6 Snowfall flux kg m-2 s-1 Mass of water in the form of snow that falls to the Earth's surface. CMIP6 INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Precipitation kg m-2 s-1 The sum of liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. CMIP6 Precipitation kg m-2 s-1 The sum of liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. CMIP6 CMIP6 Snowfall flux kg m-2 s-1 Mass of water in the form of snow that falls to the Earth's surface. CMIP6 Snowfall flux kg m-2 s-1 Mass of water in the form of snow that falls to the Earth's surface. CMIP6 CMIP6 OUTPUT VARIABLES Name Units Description Percentage of precipitation falling as snow % The percentage of all falling precipitation that will fall as snow. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Percentage of precipitation falling as snow % The percentage of all falling precipitation that will fall as snow. Percentage of precipitation falling as snow % The percentage of all falling precipitation that will fall as snow. 580 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-european-north-west-shelf-ocean-biogeochemistry http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011 Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis Short Description: The ocean biogeochemistry reanalysis for the North-West European Shelf is produced using the European Regional Seas Ecosystem Model (ERSEM), coupled online to the forecasting ocean assimilation model at 7 km horizontal resolution, NEMO-NEMOVAR. ERSEM (Butenschön et al. 2016) is developed and maintained at Plymouth Marine Laboratory. NEMOVAR system was used to assimilate observations of sea surface chlorophyll concentration from ocean colour satellite data and all the physical variables described in [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NWSHELF_MULTIYEAR_PHY_004_009]. Biogeochemical boundary conditions and river inputs used climatologies; nitrogen deposition at the surface used time-varying data. https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… The description of the model and its configuration, including the products validation is provided in the [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004… CMEMS-NWS-QUID-004-011]. http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004… Products are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are concentration of chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, net primary production, light attenuation coefficient, pH, surface partial pressure of CO2, concentration of diatoms expressed as chlorophyll, concentration of dinoflagellates expressed as chlorophyll, concentration of nanophytoplankton expressed as chlorophyll, concentration of picophytoplankton expressed as chlorophyll in sea water. All, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually, providing a six-month extension of the time series. See [http://resources.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-0… CMEMS-NWS-PUM-004-009_011] for details. http://resources.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-0… Associated products: This model is coupled with a hydrodynamic model (NEMO) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NWSHELF_MULTIYEAR_PHY_004_009]. An analysis-forecast product is available from: [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NWSHELF_MULTIYEAR_BGC_004_011]. https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… DOI (product) :https://doi.org/10.48670/moi-00058 https://doi.org/10.48670/moi-00058 581 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/snow-water-equivalent-2006-present-raster-5-km-northern https://land.copernicus.eu/global/access Snow Water Equivalent 2006-present (raster 5 km), northern hemisphere, daily - version 1 Snow Water Equivalent products describe the equivalent amount of liquid water stored in the snow pack. It indicates the water column that would theoretically result should the whole snow pack melt instantaneously and is defined as product between the snow layer?s depth and density. Information about snow water equivalent is needed in applications such as flood forecasting, controlling the water level of power plant reservoirs, planning for forestry and crop irrigation and as input and control variable for many environment research purposes, including climate change research. 582 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/season-amplitude-2017-present-raster-10-m-europe-yearly https://www.wekeo.eu/data?view=viewer&t=1577905116279&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1QSEVOT0xPR1ktQU5ELVBST0RVQ1RJVklUWS1QQVJBTUVURVJTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVlBQX0FNUExfU0VBU09OMV8xME0iLCJ6SW5kZXgiOjgwfV0%3D&initial=1 Season Amplitude 2017-present (raster 10 m), Europe, yearly, Sept. 2021 The Season Amplitude (AMPL), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Season Amplitude (AMPL) is the difference between the maximum and minimum Plant Phenology Index (PPI) values reached during the season. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Amplitude is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-m… https://land.copernicus.eu/user-corner/technical-library/product-user-m… A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The AMPL dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year. 583 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-water-quantity-swicca https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-water-quantity-swicca sis-water-quantity-swicca This dataset contains modelled data for water runoff and wetness, river flow, snow water equivalent, soil water content and other water related quantities for the European region. These variables were computed as a part of a proof of concept contract designed to speed up the workflow in impact assessments and to simplify climate change adaptation of water management practices across Europe. These quantities were modelled using the Swedish Meteorological and Hydrological Institute E-HYPE, the Wageningen University VIC model and the Joint Research Center Lisflood models. These models work at different resolutions, thus the data is provided at different resolutions. E-HYPE and Lisflood were upscaled in order to show the model ensemble. runoff wetness river flow snow water equivalent soil water content Most variables are provided as averages over 30 year periods, either for each calendar month or for the whole period. However, for some of the variables, day, percentile and return periods are also provided. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded and catchment Horizontal coverage Pan European domain Horizontal resolution Different resolutions: 0.5° x 0.5°, 0.1° x 0.1° and catchement resolution (215 km2 in average) Temporal coverage Reference period: 1971-2000. Future periods: three different 30-year periods, 2011-2040, 2041-2070 and 2071-2100. Temporal resolution The indicator is derived from the daily series and represents statistics over a long period. As such, it does not have a temporal resolution File format NetCDF DATA DESCRIPTION DATA DESCRIPTION Data type Gridded and catchment Data type Gridded and catchment Horizontal coverage Pan European domain Horizontal coverage Pan European domain Horizontal resolution Different resolutions: 0.5° x 0.5°, 0.1° x 0.1° and catchement resolution (215 km2 in average) Horizontal resolution Different resolutions: 0.5° x 0.5°, 0.1° x 0.1° and catchement resolution (215 km2 in average) Temporal coverage Reference period: 1971-2000. Future periods: three different 30-year periods, 2011-2040, 2041-2070 and 2071-2100. Temporal coverage Reference period: 1971-2000. Future periods: three different 30-year periods, 2011-2040, 2041-2070 and 2071-2100. Temporal resolution The indicator is derived from the daily series and represents statistics over a long period. As such, it does not have a temporal resolution Temporal resolution The indicator is derived from the daily series and represents statistics over a long period. As such, it does not have a temporal resolution File format NetCDF File format NetCDF MAIN VARIABLES Name Units Description Aridity 1 % Ratio between potential evapotranspiration and precipitation. Potential evapotranspiration is the modelled evapotranspiration when there is abundant water. Aridity 1 values can be larger than 1. Aridity 2 % Ratio between actual evapotranspiration and precipitation. Actual evapotranspiration is the modelled evapotranspiration computed only with available water. Aridity 2 cannot normally exceed 1. River flow m3 s-1 for the reference period % of change for the future periods. Volume rate of water flow that is transported through a given cross-sectional area. It is synonymous to river discharge or streamflow. Snow water equivalent mm for the reference period % mm change for future periods. Amount of water contained in the snow pack. It can be considered as the depth of water that theoretically would result if the whole snow pack instantaneously melts. Snow water equivalent is the product of snow depth and snow density. Soil water content % for the reference period % of change for future periods. Volume fraction of soil occupied by water, averaged over those soil layers that provide moisture for plant transpiration. This term includes all phases of water. Unregulated river flow m3 s-1 for the reference period % of change for the future periods. Volume rate of water flow that is transported through a given cross-sectional area. It is synonymous to river discharge or streamflow. Water runoff mm day-1 for the reference period % change for future periods. Sum of surface and subsurface runoff to streams for each grid cell or catchment. Wetness 1 mm day-1 Precipitation minus potential evapotranspiration. Potential evapotranspiration is the modelled evapotranspiration when there is abundant water. Wetness 2 mm day-1 Precipitation minus actual evapotranspiration. Actual evapotranspiration is the modelled evapotranspiration computed only with available water. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Aridity 1 % Ratio between potential evapotranspiration and precipitation. Potential evapotranspiration is the modelled evapotranspiration when there is abundant water. Aridity 1 values can be larger than 1. Aridity 1 % Ratio between potential evapotranspiration and precipitation. Potential evapotranspiration is the modelled evapotranspiration when there is abundant water. Aridity 1 values can be larger than 1. Aridity 2 % Ratio between actual evapotranspiration and precipitation. Actual evapotranspiration is the modelled evapotranspiration computed only with available water. Aridity 2 cannot normally exceed 1. Aridity 2 % Ratio between actual evapotranspiration and precipitation. Actual evapotranspiration is the modelled evapotranspiration computed only with available water. Aridity 2 cannot normally exceed 1. River flow m3 s-1 for the reference period % of change for the future periods. Volume rate of water flow that is transported through a given cross-sectional area. It is synonymous to river discharge or streamflow. River flow m3 s-1 for the reference period % of change for the future periods. m3 s-1 for the reference period % of change for the future periods. Volume rate of water flow that is transported through a given cross-sectional area. It is synonymous to river discharge or streamflow. Snow water equivalent mm for the reference period % mm change for future periods. Amount of water contained in the snow pack. It can be considered as the depth of water that theoretically would result if the whole snow pack instantaneously melts. Snow water equivalent is the product of snow depth and snow density. Snow water equivalent mm for the reference period % mm change for future periods. mm for the reference period % mm change for future periods. Amount of water contained in the snow pack. It can be considered as the depth of water that theoretically would result if the whole snow pack instantaneously melts. Snow water equivalent is the product of snow depth and snow density. Soil water content % for the reference period % of change for future periods. Volume fraction of soil occupied by water, averaged over those soil layers that provide moisture for plant transpiration. This term includes all phases of water. Soil water content % for the reference period % of change for future periods. % for the reference period % of change for future periods. Volume fraction of soil occupied by water, averaged over those soil layers that provide moisture for plant transpiration. This term includes all phases of water. Unregulated river flow m3 s-1 for the reference period % of change for the future periods. Volume rate of water flow that is transported through a given cross-sectional area. It is synonymous to river discharge or streamflow. Unregulated river flow m3 s-1 for the reference period % of change for the future periods. m3 s-1 for the reference period % of change for the future periods. Volume rate of water flow that is transported through a given cross-sectional area. It is synonymous to river discharge or streamflow. Water runoff mm day-1 for the reference period % change for future periods. Sum of surface and subsurface runoff to streams for each grid cell or catchment. Water runoff mm day-1 for the reference period % change for future periods. mm day-1 for the reference period % change for future periods. Sum of surface and subsurface runoff to streams for each grid cell or catchment. Wetness 1 mm day-1 Precipitation minus potential evapotranspiration. Potential evapotranspiration is the modelled evapotranspiration when there is abundant water. Wetness 1 mm day-1 Precipitation minus potential evapotranspiration. Potential evapotranspiration is the modelled evapotranspiration when there is abundant water. Wetness 2 mm day-1 Precipitation minus actual evapotranspiration. Actual evapotranspiration is the modelled evapotranspiration computed only with available water. Wetness 2 mm day-1 Precipitation minus actual evapotranspiration. Actual evapotranspiration is the modelled evapotranspiration computed only with available water. 584 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/tree-cover-density-2018-raster-100-m-europe-3-yearly-sep https://land.copernicus.eu/pan-european/high-resolution-layers/forests/tree-cover-density/status-maps/tree-cover-density-2018 Tree Cover Density 2018 (raster 100 m), Europe, 3-yearly, Sep. 2020 This metadata refers to the HRL Forest 2018 primary status layer Tree Cover Density (TCD). The TCD raster product provides information on the proportional crown coverage per pixel at 10m spatial resolution and ranges from 0% (all non-tree covered areas) to 100%, whereby Tree Cover Density is defined as the "vertical projection of tree crowns to a horizontal earth’s surface“. The production of the High Resolution Forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. The HRL Forest product consists of 3 types of (status) products and additional change products. The status products are available for 2012, 2015, and 2018 reference years: 1. Tree cover density (TCD) (level of tree cover density in a range from 0-100%) 2. Dominant leaf type (DLT) (broadleaved or coniferous majority) 3. Forest type product (FTY). The forest type product allows to get as close as possible to the FAO forest definition. In its original (10m (2018) / 20m (2012, 2015)) resolution it consists of two products: a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps (now only available on demand), based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and imperviousness 2009 data). For the final 100 m product trees under agricultural use and urban context from the support layer are removed. NEW for 2018: the 10m 2018 reference year FTY product now also has the agricultural/urban trees removed. In the past this was done only for the 100m product, now it is consistently applied for both the 10m and the 100m FTY products. This metadata corresponds to the 100 meter aggregate raster, derived through spatial aggregation from the 10m status layer. It is provided as a full mosaic covering EEA38 countries and the United Kingdom. 585 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-iberian-biscay-irish-ocean-situ-near-real-time http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_IBI_PHYBGCWAV_DISCRETE_MYNRT_013_033 Atlantic Iberian Biscay Irish Ocean- In-Situ Near Real Time Observations Short description: IBI Seas - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average DOI (product) :https://doi.org/10.48670/moi-00043 https://doi.org/10.48670/moi-00043 586 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-sea-ice-thickness-reprocessed http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_013 Arctic Ocean - Sea Ice Thickness REPROCESSED Short description: Arctic sea ice L3 data in separate monthly files. The time series is based on reprocessed radar altimeter satellite data from Envisat and CryoSat and is available in the freezing season between October and April. The product is brokered from the Copernicus Climate Change Service (C3S). DOI (product) :https://doi.org/10.48670/moi-00127 https://doi.org/10.48670/moi-00127 587 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/slope-green-down-period-2017-present-raster-10-m-europe https://www.wekeo.eu/data?view=viewer&t=1577905116279&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1QSEVOT0xPR1ktQU5ELVBST0RVQ1RJVklUWS1QQVJBTUVURVJTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVlBQX1JTTE9QRV9TRUFTT04xXzEwTSIsInpJbmRleCI6ODB9XQ%3D%3D&initial=1 Slope of the Green-down Period 2017-present (raster 10 m), Europe, yearly, Sept. 2021 The slope of the green-down or senescent period (Right Slope, RSLOPE), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The slope of the green-down or senescent period (RSLOPE) expresses the rate of change in the values of the Plant Phenology Index (PPI) at the day when the vegetation growing season ends. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The green-down period slope is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-m… https://land.copernicus.eu/user-corner/technical-library/product-user-m… A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The RSLOPE dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year. 588 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-satellite-soil-moisture https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-satellite-soil-moisture app-satellite-soil-moisture The application presents global satellite soil moisture (SM) anomalies with respect to the long-term climatological (average) conditions. SM anomalies are typically used as input for the prediction of droughts, their impact on vegetation growth or the assessment of crop yield. Satellite soil moisture data in the CDS are available from three different sensor types: the independent “Active” and “Passive” data are based on spaceborne scatterometer, respectively radiometer measurements only. The “Combined” product merges active and passive observations into a single data set with optimal data quality and coverage. All products are available in quarter degree resolution and are temporally resampled to represent the average daily, dekadal (10-daily) or monthly conditions. The interactive map displays the monthly soil moisture climatology for the selected product and climatology baseline selection made; there is one layer for each month. Clicking a location on the map, or selecting a city from the search bar in the upper right of the interactive map, opens a detailed view of that location which contains two time-series plots: The monthly climatology of the soil moisture at the selected location(s). The soil moisture anomaly (deviation from the climatology) for each month in the climate data record (1991 to 2019, zoom out to see points earlier than 2010). The monthly climatology of the soil moisture at the selected location(s). The soil moisture anomaly (deviation from the climatology) for each month in the climate data record (1991 to 2019, zoom out to see points earlier than 2010). Subsequent clicks and/or city selections will append lines to the time-series so that the soil moisture in multiple locations can be compared. User selectable parameters User selectable parameters Satellite soil moisture product: “Combined”, “Active” or “Passive” observations. Climatology baseline: the year range used to calculate the climatology baseline. The predefined 20 year periods have been precalculated so will respond much quicker. Selecting custom allows user to define there own reference period. Custom climatology baseline: a custom year range used to calculate the climatology baseline. Satellite soil moisture product: “Combined”, “Active” or “Passive” observations. Climatology baseline: the year range used to calculate the climatology baseline. The predefined 20 year periods have been precalculated so will respond much quicker. Selecting custom allows user to define there own reference period. Custom climatology baseline: a custom year range used to calculate the climatology baseline. INPUT VARIABLES Name Units Description Source Surface soil moisture % Content of liquid water in a surface soil layer of 2 to 5 cm depth expressed as the percentage of total saturation. Satellite soil moisture Volumetric soil moisture m3 m-3 Content of liquid water in a surface soil layer of 2 to 5 cm depth expressed as m3 water per m3 soil. Satellite soil moisture INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Surface soil moisture % Content of liquid water in a surface soil layer of 2 to 5 cm depth expressed as the percentage of total saturation. Satellite soil moisture Surface soil moisture % Content of liquid water in a surface soil layer of 2 to 5 cm depth expressed as the percentage of total saturation. Satellite soil moisture Satellite soil moisture Volumetric soil moisture m3 m-3 Content of liquid water in a surface soil layer of 2 to 5 cm depth expressed as m3 water per m3 soil. Satellite soil moisture Volumetric soil moisture m3 m-3 Content of liquid water in a surface soil layer of 2 to 5 cm depth expressed as m3 water per m3 soil. Satellite soil moisture Satellite soil moisture 589 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-wave-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=ARCTIC_ANALYSIS_FORECAST_WAV_002_014 Arctic Ocean Wave Analysis and Forecast Short description: The Arctic Ocean Wave Analysis and Forecast system uses the WAM model at 3 km resolution forced with surface winds and boundary wave spectra from the ECMWF (European Centre for Medium-Range Weather Forecasts) together with currents and ice from the ARC MFC analysis (Sea Ice concentration and thickness). WAM runs twice daily providing one hourly 10 days forecast and one hourly 5 days forecast. From the output variables the most commonly used are significant wave height, peak period and mean direction. DOI (product) :https://doi.org/10.48670/moi-00002 https://doi.org/10.48670/moi-00002 590 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/north-atlantic-and-european-seas-along-track-high http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_ATL_PHY_HR_L3_MY_008_064 NORTH ATLANTIC AND EUROPEAN SEAS ALONG-TRACK HIGH RESOLUTION L3 SEA LEVEL ANOMALIES Short description: Experimental altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 5Hz (~1.3km) sampling. All the missions are homogenized with respect to a reference mission (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmosphic Correction, Ocean Tides, Long Wavelength Errors, Internal tide, …) that can be used to change the physical content for specific needs This product was generated as experimental products in a CNES R&D context. It was processed by the DUACS multimission altimeter data processing system. http://duacs.cls.fr http://duacs.cls.fr DOI (product) :https://doi.org/10.48670/moi-00137 https://doi.org/10.48670/moi-00137 591 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-physics-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=ARCTIC_ANALYSIS_FORECAST_PHYS_002_001_a Arctic Ocean Physics Analysis and Forecast Short description: The operational TOPAZ4 Arctic Ocean system uses the HYCOM model and a 100-member EnKF assimilation scheme. It is run daily to provide 10 days of forecast (average of 10 members) of the 3D physical ocean, including sea ice; data assimilation is performed weekly to provide 7 days of analysis (ensemble average). Output products are interpolated on a grid of 12.5 km resolution at the North Pole (equivalent to 1/8 deg in mid-latitudes) on a polar stereographic projection. The geographical projection follows these proj4 library parameters: proj4 = "+units=m +proj=stere +a=6378273.0 +b=6378273.0 +lon_0=-45.0 +lat_0=90.0 +lat_ts=90.0 " DOI (product) :https://doi.org/10.48670/moi-00001 https://doi.org/10.48670/moi-00001 592 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-reanalysis-eac4 https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4 cams-global-reanalysis-eac4 EAC4 (ECMWF Atmospheric Composition Reanalysis 4) is the fourth generation ECMWF global reanalysis of atmospheric composition. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. The assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available. The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with. The observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. For this reason, EAC4 is only available from 2003 onwards. Although the analysis procedure considers chunks of data in a window of 12 hours in one go, EAC4 provides estimates every 3 hours, worldwide. This is made possible by the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 0.75°x0.75° Vertical coverage Surface, total column, model levels and pressure levels. Vertical resolution 60 model levels. Pressure levels: 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa Temporal coverage 2003 to 2022 Temporal resolution 3-hourly File format GRIB (optional conversion to netCDF) Versions Only one version Update frequency Twice a year with 4-6 month delay DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.75°x0.75° Horizontal resolution 0.75°x0.75° Vertical coverage Surface, total column, model levels and pressure levels. Vertical coverage Surface, total column, model levels and pressure levels. Vertical resolution 60 model levels. Pressure levels: 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa Vertical resolution 60 model levels. Pressure levels: 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa Temporal coverage 2003 to 2022 Temporal coverage 2003 to 2022 Temporal resolution 3-hourly Temporal resolution 3-hourly File format GRIB (optional conversion to netCDF) File format GRIB (optional conversion to netCDF) Versions Only one version Versions Only one version Update frequency Twice a year with 4-6 month delay Update frequency Twice a year with 4-6 month delay MAIN VARIABLES Name Units 10m u-component of wind m s-1 10m v-component of wind m s-1 2m dewpoint temperature K 2m temperature K Acetone kg kg-1 Acetone product kg kg-1 Aldehydes kg kg-1 Amine kg kg-1 Ammonia kg kg-1 Ammonium kg kg-1 Black carbon aerosol optical depth at 550 nm dimensionless Carbon monoxide kg kg-1 Dimethyl sulfide kg kg-1 Dinitrogen pentoxide kg kg-1 Dust aerosol (0.03 - 0.55 µm) mixing ratio kg kg-1 Dust aerosol (0.55 - 0.9 µm) mixing ratio kg kg-1 Dust aerosol (0.9 - 20 µm) mixing ratio kg kg-1 Dust aerosol optical depth at 550 nm dimensionless Ethane kg kg-1 Ethanol kg kg-1 Ethene kg kg-1 Formaldehyde kg kg-1 Formic acid kg kg-1 Fraction of cloud cover (0 - 1) Geopotential m2 s-2 High cloud cover (0 - 1) High vegetation cover (0 - 1) Hydrogen peroxide kg kg-1 Hydroperoxy radical kg kg-1 Hydrophilic black carbon aerosol mixing ratio kg kg-1 Hydrophilic organic matter aerosol mixing ratio kg kg-1 Hydrophobic black carbon aerosol mixing ratio kg kg-1 Hydrophobic organic matter aerosol mixing ratio kg kg-1 Hydroxyl radical kg kg-1 Isoprene kg kg-1 Lake cover (0 - 1) Land-sea mask (0 - 1) Lead kg kg-1 Leaf area index, high vegetation m2 m-2 Leaf area index, low vegetation m2 m-2 Lifting threshold speed m s-1 Low cloud cover (0 - 1) Low vegetation cover (0 - 1) Mean altitude of maximum injection m Mean sea level pressure Pa Medium cloud cover (0 - 1) Methacrolein MVK kg kg-1 Methacrylic acid kg kg-1 Methane (chemistry) kg kg-1 Methane sulfonic acid kg kg-1 Methanol kg kg-1 Methyl glyoxal kg kg-1 Methyl peroxide kg kg-1 Methylperoxy radical kg kg-1 Near IR albedo for diffuse radiation (0 - 1) Near IR albedo for direct radiation (0 - 1) Nitrate kg kg-1 Nitrate radical kg kg-1 Nitric acid kg kg-1 Nitrogen dioxide kg kg-1 Nitrogen monoxide kg kg-1 Olefins kg kg-1 Organic ethers kg kg-1 Organic matter aerosol optical depth at 550 nm dimensionless Organic nitrates kg kg-1 Ozone kg kg-1 Paraffins kg kg-1 Particulate matter d < 1 µm (PM1) kg m-3 Particulate matter d < 10 µm (PM10) kg m-3 Particulate matter d < 2.5 µm (PM2.5) kg m-3 Pernitric acid kg kg-1 Peroxides kg kg-1 Peroxy acetyl radical kg kg-1 Peroxyacetyl nitrate kg kg-1 Potential vorticity K m2 kg-1 s-1 Propane kg kg-1 Propene kg kg-1 Radon kg kg-1 Relative humidity % Sea salt aerosol (0.03 - 0.5 µm) mixing ratio kg kg-1 Sea salt aerosol (0.5 - 5 µm) mixing ratio kg kg-1 Sea salt aerosol (5 - 20 µm) mixing ratio kg kg-1 Sea salt aerosol optical depth at 550 nm dimensionless Sea surface temperature K Sea-ice cover (0 - 1) Skin reservoir content m of water equivalent Skin temperature K Snow albedo (0 - 1) Snow depth m of water equivalent Soil clay content % Soil type dimensionless Specific cloud ice water content kg kg-1 Specific cloud liquid water content kg kg-1 Specific humidity kg kg-1 Specific rain water content kg kg-1 Specific snow water content kg kg-1 Stratospheric ozone tracer kg kg-1 Sulphate aerosol mixing ratio kg kg-1 Sulphate aerosol optical depth at 550 nm dimensionless Sulphur dioxide kg kg-1 Surface Geopotential m2 s-2 Surface pressure Pa Surface roughness m Temperature K Terpenes kg kg-1 Total aerosol optical depth at 1240 nm dimensionless Total aerosol optical depth at 469 nm dimensionless Total aerosol optical depth at 550 nm dimensionless Total aerosol optical depth at 670 nm dimensionless Total aerosol optical depth at 865 nm dimensionless Total cloud cover (0 - 1) Total column acetone kg m-2 Total column aldehydes kg m-2 Total column carbon monoxide kg m-2 Total column ethane kg m-2 Total column ethanol kg m-2 Total column ethene kg m-2 Total column formaldehyde kg m-2 Total column formic acid kg m-2 Total column hydrogen peroxide kg m-2 Total column hydroxyl radical kg m-2 Total column isoprene kg m-2 Total column methane kg m-2 Total column methanol kg m-2 Total column methyl peroxide kg m-2 Total column nitric acid kg m-2 Total column nitrogen dioxide kg m-2 Total column nitrogen monoxide kg m-2 Total column olefins kg m-2 Total column organic nitrates kg m-2 Total column ozone kg m-2 Total column paraffins kg m-2 Total column peroxyacetyl nitrate kg m-2 Total column propane kg m-2 Total column sulphur dioxide kg m-2 Total column water kg m-2 Total column water vapour kg m-2 Type of high vegetation dimensionless Type of low vegetation dimensionless U-component of wind m s-1 UV visible albedo for diffuse radiation (0 - 1) UV visible albedo for direct radiation (0 - 1) V-component of wind m s-1 Vertical velocity Pa s-1 Vertically integrated mass of dust aerosol (0.03 - 0.55 µm) kg m-2 Vertically integrated mass of dust aerosol (0.55 - 9 µm) kg m-2 Vertically integrated mass of dust aerosol (9 - 20 µm) kg m-2 Vertically integrated mass of hydrophilic black carbon aerosol kg m-2 Vertically integrated mass of hydrophilic organic matter aerosol kg m-2 Vertically integrated mass of hydrophobic black carbon aerosol kg m-2 Vertically integrated mass of hydrophobic organic matter aerosol kg m-2 Vertically integrated mass of sea salt aerosol (0.03 - 0.5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (0.5 - 5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (5 - 20 µm) kg m-2 Vertically integrated mass of sulphate aerosol kg m-2 MAIN VARIABLES MAIN VARIABLES Name Units Name Units 10m u-component of wind m s-1 10m u-component of wind m s-1 10m v-component of wind m s-1 10m v-component of wind m s-1 2m dewpoint temperature K 2m dewpoint temperature K 2m temperature K 2m temperature K Acetone kg kg-1 Acetone kg kg-1 Acetone product kg kg-1 Acetone product kg kg-1 Aldehydes kg kg-1 Aldehydes kg kg-1 Amine kg kg-1 Amine kg kg-1 Ammonia kg kg-1 Ammonia kg kg-1 Ammonium kg kg-1 Ammonium kg kg-1 Black carbon aerosol optical depth at 550 nm dimensionless Black carbon aerosol optical depth at 550 nm dimensionless Carbon monoxide kg kg-1 Carbon monoxide kg kg-1 Dimethyl sulfide kg kg-1 Dimethyl sulfide kg kg-1 Dinitrogen pentoxide kg kg-1 Dinitrogen pentoxide kg kg-1 Dust aerosol (0.03 - 0.55 µm) mixing ratio kg kg-1 Dust aerosol (0.03 - 0.55 µm) mixing ratio kg kg-1 Dust aerosol (0.55 - 0.9 µm) mixing ratio kg kg-1 Dust aerosol (0.55 - 0.9 µm) mixing ratio kg kg-1 Dust aerosol (0.9 - 20 µm) mixing ratio kg kg-1 Dust aerosol (0.9 - 20 µm) mixing ratio kg kg-1 Dust aerosol optical depth at 550 nm dimensionless Dust aerosol optical depth at 550 nm dimensionless Ethane kg kg-1 Ethane kg kg-1 Ethanol kg kg-1 Ethanol kg kg-1 Ethene kg kg-1 Ethene kg kg-1 Formaldehyde kg kg-1 Formaldehyde kg kg-1 Formic acid kg kg-1 Formic acid kg kg-1 Fraction of cloud cover (0 - 1) Fraction of cloud cover (0 - 1) Geopotential m2 s-2 Geopotential m2 s-2 High cloud cover (0 - 1) High cloud cover (0 - 1) High vegetation cover (0 - 1) High vegetation cover (0 - 1) Hydrogen peroxide kg kg-1 Hydrogen peroxide kg kg-1 Hydroperoxy radical kg kg-1 Hydroperoxy radical kg kg-1 Hydrophilic black carbon aerosol mixing ratio kg kg-1 Hydrophilic black carbon aerosol mixing ratio kg kg-1 Hydrophilic organic matter aerosol mixing ratio kg kg-1 Hydrophilic organic matter aerosol mixing ratio kg kg-1 Hydrophobic black carbon aerosol mixing ratio kg kg-1 Hydrophobic black carbon aerosol mixing ratio kg kg-1 Hydrophobic organic matter aerosol mixing ratio kg kg-1 Hydrophobic organic matter aerosol mixing ratio kg kg-1 Hydroxyl radical kg kg-1 Hydroxyl radical kg kg-1 Isoprene kg kg-1 Isoprene kg kg-1 Lake cover (0 - 1) Lake cover (0 - 1) Land-sea mask (0 - 1) Land-sea mask (0 - 1) Lead kg kg-1 Lead kg kg-1 Leaf area index, high vegetation m2 m-2 Leaf area index, high vegetation m2 m-2 Leaf area index, low vegetation m2 m-2 Leaf area index, low vegetation m2 m-2 Lifting threshold speed m s-1 Lifting threshold speed m s-1 Low cloud cover (0 - 1) Low cloud cover (0 - 1) Low vegetation cover (0 - 1) Low vegetation cover (0 - 1) Mean altitude of maximum injection m Mean altitude of maximum injection m Mean sea level pressure Pa Mean sea level pressure Pa Medium cloud cover (0 - 1) Medium cloud cover (0 - 1) Methacrolein MVK kg kg-1 Methacrolein MVK kg kg-1 Methacrylic acid kg kg-1 Methacrylic acid kg kg-1 Methane (chemistry) kg kg-1 Methane (chemistry) kg kg-1 Methane sulfonic acid kg kg-1 Methane sulfonic acid kg kg-1 Methanol kg kg-1 Methanol kg kg-1 Methyl glyoxal kg kg-1 Methyl glyoxal kg kg-1 Methyl peroxide kg kg-1 Methyl peroxide kg kg-1 Methylperoxy radical kg kg-1 Methylperoxy radical kg kg-1 Near IR albedo for diffuse radiation (0 - 1) Near IR albedo for diffuse radiation (0 - 1) Near IR albedo for direct radiation (0 - 1) Near IR albedo for direct radiation (0 - 1) Nitrate kg kg-1 Nitrate kg kg-1 Nitrate radical kg kg-1 Nitrate radical kg kg-1 Nitric acid kg kg-1 Nitric acid kg kg-1 Nitrogen dioxide kg kg-1 Nitrogen dioxide kg kg-1 Nitrogen monoxide kg kg-1 Nitrogen monoxide kg kg-1 Olefins kg kg-1 Olefins kg kg-1 Organic ethers kg kg-1 Organic ethers kg kg-1 Organic matter aerosol optical depth at 550 nm dimensionless Organic matter aerosol optical depth at 550 nm dimensionless Organic nitrates kg kg-1 Organic nitrates kg kg-1 Ozone kg kg-1 Ozone kg kg-1 Paraffins kg kg-1 Paraffins kg kg-1 Particulate matter d < 1 µm (PM1) kg m-3 Particulate matter d < 1 µm (PM1) kg m-3 Particulate matter d < 10 µm (PM10) kg m-3 Particulate matter d < 10 µm (PM10) kg m-3 Particulate matter d < 2.5 µm (PM2.5) kg m-3 Particulate matter d < 2.5 µm (PM2.5) kg m-3 Pernitric acid kg kg-1 Pernitric acid kg kg-1 Peroxides kg kg-1 Peroxides kg kg-1 Peroxy acetyl radical kg kg-1 Peroxy acetyl radical kg kg-1 Peroxyacetyl nitrate kg kg-1 Peroxyacetyl nitrate kg kg-1 Potential vorticity K m2 kg-1 s-1 Potential vorticity K m2 kg-1 s-1 Propane kg kg-1 Propane kg kg-1 Propene kg kg-1 Propene kg kg-1 Radon kg kg-1 Radon kg kg-1 Relative humidity % Relative humidity % Sea salt aerosol (0.03 - 0.5 µm) mixing ratio kg kg-1 Sea salt aerosol (0.03 - 0.5 µm) mixing ratio kg kg-1 Sea salt aerosol (0.5 - 5 µm) mixing ratio kg kg-1 Sea salt aerosol (0.5 - 5 µm) mixing ratio kg kg-1 Sea salt aerosol (5 - 20 µm) mixing ratio kg kg-1 Sea salt aerosol (5 - 20 µm) mixing ratio kg kg-1 Sea salt aerosol optical depth at 550 nm dimensionless Sea salt aerosol optical depth at 550 nm dimensionless Sea surface temperature K Sea surface temperature K Sea-ice cover (0 - 1) Sea-ice cover (0 - 1) Skin reservoir content m of water equivalent Skin reservoir content m of water equivalent Skin temperature K Skin temperature K Snow albedo (0 - 1) Snow albedo (0 - 1) Snow depth m of water equivalent Snow depth m of water equivalent Soil clay content % Soil clay content % Soil type dimensionless Soil type dimensionless Specific cloud ice water content kg kg-1 Specific cloud ice water content kg kg-1 Specific cloud liquid water content kg kg-1 Specific cloud liquid water content kg kg-1 Specific humidity kg kg-1 Specific humidity kg kg-1 Specific rain water content kg kg-1 Specific rain water content kg kg-1 Specific snow water content kg kg-1 Specific snow water content kg kg-1 Stratospheric ozone tracer kg kg-1 Stratospheric ozone tracer kg kg-1 Sulphate aerosol mixing ratio kg kg-1 Sulphate aerosol mixing ratio kg kg-1 Sulphate aerosol optical depth at 550 nm dimensionless Sulphate aerosol optical depth at 550 nm dimensionless Sulphur dioxide kg kg-1 Sulphur dioxide kg kg-1 Surface Geopotential m2 s-2 Surface Geopotential m2 s-2 Surface pressure Pa Surface pressure Pa Surface roughness m Surface roughness m Temperature K Temperature K Terpenes kg kg-1 Terpenes kg kg-1 Total aerosol optical depth at 1240 nm dimensionless Total aerosol optical depth at 1240 nm dimensionless Total aerosol optical depth at 469 nm dimensionless Total aerosol optical depth at 469 nm dimensionless Total aerosol optical depth at 550 nm dimensionless Total aerosol optical depth at 550 nm dimensionless Total aerosol optical depth at 670 nm dimensionless Total aerosol optical depth at 670 nm dimensionless Total aerosol optical depth at 865 nm dimensionless Total aerosol optical depth at 865 nm dimensionless Total cloud cover (0 - 1) Total cloud cover (0 - 1) Total column acetone kg m-2 Total column acetone kg m-2 Total column aldehydes kg m-2 Total column aldehydes kg m-2 Total column carbon monoxide kg m-2 Total column carbon monoxide kg m-2 Total column ethane kg m-2 Total column ethane kg m-2 Total column ethanol kg m-2 Total column ethanol kg m-2 Total column ethene kg m-2 Total column ethene kg m-2 Total column formaldehyde kg m-2 Total column formaldehyde kg m-2 Total column formic acid kg m-2 Total column formic acid kg m-2 Total column hydrogen peroxide kg m-2 Total column hydrogen peroxide kg m-2 Total column hydroxyl radical kg m-2 Total column hydroxyl radical kg m-2 Total column isoprene kg m-2 Total column isoprene kg m-2 Total column methane kg m-2 Total column methane kg m-2 Total column methanol kg m-2 Total column methanol kg m-2 Total column methyl peroxide kg m-2 Total column methyl peroxide kg m-2 Total column nitric acid kg m-2 Total column nitric acid kg m-2 Total column nitrogen dioxide kg m-2 Total column nitrogen dioxide kg m-2 Total column nitrogen monoxide kg m-2 Total column nitrogen monoxide kg m-2 Total column olefins kg m-2 Total column olefins kg m-2 Total column organic nitrates kg m-2 Total column organic nitrates kg m-2 Total column ozone kg m-2 Total column ozone kg m-2 Total column paraffins kg m-2 Total column paraffins kg m-2 Total column peroxyacetyl nitrate kg m-2 Total column peroxyacetyl nitrate kg m-2 Total column propane kg m-2 Total column propane kg m-2 Total column sulphur dioxide kg m-2 Total column sulphur dioxide kg m-2 Total column water kg m-2 Total column water kg m-2 Total column water vapour kg m-2 Total column water vapour kg m-2 Type of high vegetation dimensionless Type of high vegetation dimensionless Type of low vegetation dimensionless Type of low vegetation dimensionless U-component of wind m s-1 U-component of wind m s-1 UV visible albedo for diffuse radiation (0 - 1) UV visible albedo for diffuse radiation (0 - 1) UV visible albedo for direct radiation (0 - 1) UV visible albedo for direct radiation (0 - 1) V-component of wind m s-1 V-component of wind m s-1 Vertical velocity Pa s-1 Vertical velocity Pa s-1 Vertically integrated mass of dust aerosol (0.03 - 0.55 µm) kg m-2 Vertically integrated mass of dust aerosol (0.03 - 0.55 µm) kg m-2 Vertically integrated mass of dust aerosol (0.55 - 9 µm) kg m-2 Vertically integrated mass of dust aerosol (0.55 - 9 µm) kg m-2 Vertically integrated mass of dust aerosol (9 - 20 µm) kg m-2 Vertically integrated mass of dust aerosol (9 - 20 µm) kg m-2 Vertically integrated mass of hydrophilic black carbon aerosol kg m-2 Vertically integrated mass of hydrophilic black carbon aerosol kg m-2 Vertically integrated mass of hydrophilic organic matter aerosol kg m-2 Vertically integrated mass of hydrophilic organic matter aerosol kg m-2 Vertically integrated mass of hydrophobic black carbon aerosol kg m-2 Vertically integrated mass of hydrophobic black carbon aerosol kg m-2 Vertically integrated mass of hydrophobic organic matter aerosol kg m-2 Vertically integrated mass of hydrophobic organic matter aerosol kg m-2 Vertically integrated mass of sea salt aerosol (0.03 - 0.5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (0.03 - 0.5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (0.5 - 5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (0.5 - 5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (5 - 20 µm) kg m-2 Vertically integrated mass of sea salt aerosol (5 - 20 µm) kg m-2 Vertically integrated mass of sulphate aerosol kg m-2 Vertically integrated mass of sulphate aerosol kg m-2 593 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-high-resolution-and-ultra-high-resolution-l3s http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SST_BS_SST_L3S_NRT_OBSERVATIONS_010_013 Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature Short description: For the Black Sea (BS), the CNR BS Sea Surface Temperature (SST) processing chain provides supercollated (merged multisensor, L3S) SST data remapped over the Black Sea at high (1/16°) and Ultra High (0.01°) spatial resolution, representative of nighttime SST values (00:00 UTC). The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. Consequently, the L3S processing is run daily, but L3S files are produced only if valid SST measurements are present on the area considered. DOI (product) :https://doi.org/10.48670/moi-00158 https://doi.org/10.48670/moi-00158 594 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-l4-spectral-parameters-nrt-satellite http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=WAVE_GLO_PHY_SPC_L4_NRT_014_004 GLOBAL OCEAN L4 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS Short description: Near-Real-Time multi-mission global satellite-based spectral integral parameters. Only valid data are used, based on the L3 corresponding product. Included wave parameters are partition significant wave height, partition peak period and partition peak or principal direction. Those parameters are propagated in space and time at a 3-hour timestep and on a regular space grid, providing information of the swell propagation characteristics, from source to land. One file gathers one swell system, gathering observations originating from the same storm source. This product is processed by the WAVE-TAC multi-mission SAR data processing system to serve in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes data from the following SAR missions: Sentinel-1A and Sentinel-1B. All the spectral parameter measurements are optimally interpolated using swell observations belonging to the same swell field. The SAR data processing system produces wave integral parameters by partition (partition significant wave height, partition peak period and partition peak or principal direction) and the associated standard deviation and density of propagated observations. DOI (product) :https://doi.org/10.48670/moi-00175 https://doi.org/10.48670/moi-00175 595 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/grassland-change-2015-2018-raster-20-m-europe-3-yearly https://land.copernicus.eu/pan-european/high-resolution-layers/grassland/change-maps/grassland-change-2015-2018?tab=download Grassland Change 2015-2018 (raster 20 m), Europe, 3-yearly, Oct. 2020 The High Resolution Layer (HRL) Grassland Change 2015-2018 raster product at 20m resolution provides information on changes in grassland vegetation cover between the reference years 2015 and 2018. The thematic classes indicate all non-grassland areas, grassland gain and grassland loss, unchanged grassland in both years and unverified grassland gain and loss areas for the pan-European area of EEA38 and the United Kingdom. The production of the High Resolution grassland layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. This dataset is provided as 20 meter rasters in 100 x 100 km tiles grouped according to the EEA38 countries and the United Kingdom (fully conformant with the EEA reference grid). The HRL Grassland layer is the main High Resolution grassland product. This grassy and non-woody vegetation baseline product includes all kinds of grasslands: managed grassland, semi-natural grassland and natural grassy vegetation. It is a binary status layer for the 2015 reference year mapping grassland and all non-grassland areas in 20m and (aggregated) 100m pixel size and, for the 2018 reference year, in 10m and (aggregated) 100m pixel size. 596 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-water-quality-swicca https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-water-quality-swicca sis-water-quality-swicca This dataset contains modelled data for phosphorous and nitrogen concentrations and loads. The data comes from the Swedish Meteorological and Hydrological Institute E-HYPE model at catchment level for Europe. These water quality indicators were computed as a part of a proof of concept contract designed to speed up the workflow in impact assessments and to simplify climate change adaptation of water management practices across Europe. These indicators are provided as averages over 30 year periods, either for each calendar month or for the whole period. For the reference period (1971-2000) the absolute values are given, whereas for the future periods the relative changes are provided. In addition to total amounts, organic and inorganic parts are provided for nitrogen. For phosphorous, in addition to the total amount, particulate and soluble parts are provided. Values of the temperature of the water is provided for the same periods. More details about the products are given in the Documentation section. DATA DESCRIPTION Horizontal coverage Pan European domain Horizontal resolution Irregular catchment polygons, median catchmentsize 215 km2 Temporal coverage 1971-2100 Temporal resolution The indicator is derived from the daily series and represents statistics over a long period. As such, it does not have a temporal resolution File format NetCDF DATA DESCRIPTION DATA DESCRIPTION Horizontal coverage Pan European domain Horizontal coverage Pan European domain Horizontal resolution Irregular catchment polygons, median catchmentsize 215 km2 Horizontal resolution Irregular catchment polygons, median catchmentsize 215 km2 Temporal coverage 1971-2100 Temporal coverage 1971-2100 Temporal resolution The indicator is derived from the daily series and represents statistics over a long period. As such, it does not have a temporal resolution Temporal resolution The indicator is derived from the daily series and represents statistics over a long period. As such, it does not have a temporal resolution File format NetCDF File format NetCDF MAIN VARIABLES Name Units Description Nitrogen concentrations μg/l for the reference period % of change for future periods. Mass of nitrogen divided by the volume of water. Nitrogen loads kg for the reference period % of change for future periods. Product of the river flow volume and the nitrogen concentrations. Phosphorous concentrations μg/l for the reference period % of change for the future periods. Mass of phosphorous divided by the volume of water. Phosphorous loads kg for the reference period % of change for future periods. Product of the river flow volume and the phosphorous concentrations. Water temperature °C In-stream water temperature for the reference period In-stream change of temperature for future periods. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Nitrogen concentrations μg/l for the reference period % of change for future periods. Mass of nitrogen divided by the volume of water. Nitrogen concentrations μg/l for the reference period % of change for future periods. Mass of nitrogen divided by the volume of water. Nitrogen loads kg for the reference period % of change for future periods. Product of the river flow volume and the nitrogen concentrations. Nitrogen loads kg for the reference period % of change for future periods. Product of the river flow volume and the nitrogen concentrations. Phosphorous concentrations μg/l for the reference period % of change for the future periods. Mass of phosphorous divided by the volume of water. Phosphorous concentrations μg/l for the reference period % of change for the future periods. Mass of phosphorous divided by the volume of water. Phosphorous loads kg for the reference period % of change for future periods. Product of the river flow volume and the phosphorous concentrations. Phosphorous loads kg for the reference period % of change for future periods. Product of the river flow volume and the phosphorous concentrations. Water temperature °C In-stream water temperature for the reference period In-stream change of temperature for future periods. Water temperature °C In-stream water temperature for the reference period In-stream change of temperature for future periods. 597 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-waves-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MEDSEA_ANALYSISFORECAST_WAV_006_017 Mediterranean Sea Waves Analysis and Forecast Short description: MEDSEA_ANALYSISFORECAST_WAV_006_017 is the nominal wave product of the Mediterranean Sea Forecasting system, composed by hourly wave parameters at 1/24º horizontal resolution covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The waves forecast component (Med-WAV system) is a wave model based on the WAM Cycle 6. The Med-WAV modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins and the model solutions are corrected by an optimal interpolation data assimilation scheme of all available along track satellite significant wave height observations. The atmospheric forcing is provided by the operational ECMWF Numerical Weather Prediction model and the wave model is forced with hourly averaged surface currents and sea level obtained from MEDSEA_ANALYSISFORECAST_PHY_006_013 at 1/24° resolution. The model uses wave spectra for Open Boundary Conditions from GLOBAL_ANALYSIS_FORECAST_WAV_001_027 product. The wave system includes 2 forecast cycles providing twice per day a Mediterranean wave analysis and 10 days of wave forecasts. ''Product Citation'': Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (Product): https://doi.org/10.25423/cmcc/medsea_analysisforecast_wav_006_017_medwa… https://doi.org/10.25423/cmcc/medsea_analysisforecast_wav_006_017_medwa… 598 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-biogeochemistry-hindcast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_MULTIYEAR_BGC_001_029 Global Ocean Biogeochemistry Hindcast Short description The biogeochemical hindcast for global ocean is produced at Mercator-Ocean (Toulouse. France). It provides 3D biogeochemical fields for the time period 1993-2019 at 1/4 degree and on 75 vertical levels. It uses PISCES biogeochemical model (available on the NEMO[https://www.nemo-ocean.eu/] modelling platform). No data assimilation in this product. https://www.nemo-ocean.eu/ * Latest NEMO version (v3.6_STABLE) * Forcings: GLORYS2V4-FREE [https://www.mercator-ocean.eu/solutions-expertises/acceder-aux-donnees-…] ocean physics produced at Mercator-Ocean and ERA-Interim[https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis…] atmosphere produced at ECMWF at a daily frequency * Outputs: Daily (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production) and monthly (chlorophyll. nitrate. phosphate. silicate. dissolved oxygen. primary production. iron. phytoplankton in carbon) 3D mean fields interpolated on a standard regular grid in NetCDF format. The simulation is performed once and for all. * Initial conditions: World Ocean Atlas 2013 for nitrate. phosphate. silicate and dissolved oxygen. GLODAPv2 for DIC and Alkalinity. and climatological model outputs for Iron and DOC * Quality/Accuracy/Calibration information: See the related Quality Information Document https://www.mercator-ocean.eu/solutions-expertises/acceder-aux-donnees-… https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis… DOI (product):https://doi.org/10.48670/moi-00019 https://doi.org/10.48670/moi-00019 599 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-hydrology-variables-derived-projections https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-hydrology-variables-derived-projections sis-hydrology-variables-derived-projections This dataset provides water variables and indicators based on hydrological impact modelling, forced by bias adjusted regional climate simulations from the European Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX). The dataset contains Essential Climate Variable (ECV) data in the form of daily mean river discharge and a set of climate impact indicators (CIIs) for both water quantity and quality. ECV datasets provide the empirical evidence needed to understand the current climate and predict future changes. CIIs contain condensed climate information which facilitate relatively quick and efficient subsequent analysis. Therefore, CIIs make climate information accessible to application focussed users within a sector. The ECVs and CIIs provided here were derived within the water management sectoral information service to address questions specific to the water sector. However, the products are provided in a generic form and are relevant for a range of sectors, for example agriculture and energy. The data represent the current state-of-the-art in Europe for regional climate and hydrological modelling and indicator production. Eight bias adjusted model simulations from the EURO-CORDEX EUR-11 were used to force a multi-model setup of the hydrological model E-HYPEcatch at a pan-European domain. A total of 18 water quality and quantity CIIs and 1 water ECV are provided in this dataset at catchment scale and on a 5km x 5km grid. The CIIs are provided as mean values over a 30-year time period. For the reference period (1971-2000) data is provided as absolute values, for the future periods the data is provided as absolute values and as the relative or absolute change from the reference period. The future periods cover 3 fixed time periods (2011-2040, 2041-2070 and 2071-2100) and 3 "degree scenario" periods defined by when global warming exceeds a given threshold (1.5 °C, 2.0 °C and 3.0 °C). The global warming is calculated from the global climate model (GCM) used, therefore the actual time period of the degree scenarios will be different for each GCM. The river discharge ECV data meet the technical specification set by the Global Climate Observing System (GCOS), as such they are provided on a daily time step. Note these are model output data, not observation data as is the general case for ECVs. This dataset is produced and quality assured by the Swedish Meteorological and Hydrological Institute on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded and catchments Projection Lambert azimuthal equal area and rotated grid Horizontal coverage Europe Horizontal resolution 5km x 5km and catchments Vertical coverage Single level Vertical resolution Surface Temporal coverage From 1970 to 2100 Temporal resolution Daily and 30 year annual and monthly means File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v.1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency Updates expected on irregular intervals DATA DESCRIPTION DATA DESCRIPTION Data type Gridded and catchments Data type Gridded and catchments Projection Lambert azimuthal equal area and rotated grid Projection Lambert azimuthal equal area and rotated grid Horizontal coverage Europe Horizontal coverage Europe Horizontal resolution 5km x 5km and catchments Horizontal resolution 5km x 5km and catchments Vertical coverage Single level Vertical coverage Single level Vertical resolution Surface Vertical resolution Surface Temporal coverage From 1970 to 2100 Temporal coverage From 1970 to 2100 Temporal resolution Daily and 30 year annual and monthly means Temporal resolution Daily and 30 year annual and monthly means File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v.1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v.1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 Update frequency Updates expected on irregular intervals Update frequency Updates expected on irregular intervals MAIN VARIABLES Name Units Description Aridity actual Dimensionless Aridity actual is calculated as the monthly mean values of the ratio between actual evapotranspiration and precipitation over a 30 year period. Actual evapotranspiration is the modelled evapotranspiration computed only with available water. For future periods the indicator is given as a relative change against the reference period (1971-2000). Aridity potential Dimensionless Aridity potential is calculated as the monthly mean values of the ratio between potential evapotranspiration and precipitation over a 30 year period. Potential evapotranspiration is the modelled evapotranspiration when there is abundant water. For future periods the indicator is given as a relative change against the reference period (1971-2000). Flood recurrence m3 s-1 Return values of annual maximum river discharge. Data are provided as the 2, 5, 10 and 50 year return period of annual daily maximum river discharge estimated using a Gumbel distribution. For future periods the indicator is given as a relative change against the reference period (1971-2000). Maximum river discharge m3 s-1 Maximum river discharge is calculated as the mean annual daily maximum discharge over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Mean runoff mm month-1 Runoff is defined as the sum of surface and subsurface runoff to streams for each grid cell or catchment. The indicator is calculated as the monthly or annual mean values of daily runoff averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Mean soil moisture Dimensionless Soil moisture is the water stored in the soil and is affected by precipitation, temperature, soil characteristics, and more. The soil moisture is defined slightly differently in different hydrological models, and is here generally defined as soil moisture in the root zone as fraction of the field capacity volume. Data are provided as monthly or annual mean values, averaged over a 30 years period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Minimum river discharge m3 s-1 Minimum river discharge is calculated as the mean annual daily minimum discharge over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). River discharge m3 s-1 Volume rate of water flow that is transported through a given cross-sectional area. It is synonymous to streamflow. The essential climate variable (ECV) data are provided at daily resolution. The climate impact indicator (CII) of river discharge is calculated as the monthly or annual mean values of daily runoff averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Nitrogen concentration in catchments mg L-1 Nitrogen concentration is the mass of nitrogen divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total nitrogen concentration, from a catchment averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Nitrogen concentration in local streams mg L-1 Nitrogen concentration is the mass of nitrogen divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total nitrogen concentration, from a local stream averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Nitrogen load in catchments kg year-1 kg month-1 Nitrogen load is the product of the river discharge volume and the nitrogen concentrations. The indicator is calculated as the annual (kg/year) or monthly (kg/month) mean values of total nitrogen load from a catchment averaged over of a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Phosphorus concentration in catchments mg L-1 Phosphorus concentration is the mass of phosphorus divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total phosphorus concentration, from a catchment averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Phosphorus concentration in local streams mg L-1 Phosphorus concentration is the mass of phosphorus divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total phosphorus concentration, from a local stream averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Phosphorus load in catchments kg year-1 kg month-1 Phosphorus load is the product of the river discharge volume and the phosphorus concentrations. The indicator is calculated as the annual (kg/year) or monthly (kg/month) mean values of total phosphorus load from a catchment averaged over of a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Water temperature in catchments oC Water temperature is the simulated water temperature in a catchment. The indicator is calculated as mean annual values of water temperature for a 30 years period.For future periods the indicator is given as an absolute change against the reference period (1971-2000). Water temperature in local streams oC Water temperature is the simulated water temperature in local streams. The indicator is calculated as mean annual values of water temperature for a 30 years period.For future periods the indicator is given as an absolute change against the reference period (1971-2000). Wetness actual mm month-1 Wetness actual is calculated as the monthly mean values of precipitation minus actual evapotranspiration averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Wetness potential mm month-1 Wetness potential is calculated as the monthly mean values of precipitation minus potential evapotranspiration averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Aridity actual Dimensionless Aridity actual is calculated as the monthly mean values of the ratio between actual evapotranspiration and precipitation over a 30 year period. Actual evapotranspiration is the modelled evapotranspiration computed only with available water. For future periods the indicator is given as a relative change against the reference period (1971-2000). Aridity actual Dimensionless Aridity actual is calculated as the monthly mean values of the ratio between actual evapotranspiration and precipitation over a 30 year period. Actual evapotranspiration is the modelled evapotranspiration computed only with available water. For future periods the indicator is given as a relative change against the reference period (1971-2000). Aridity potential Dimensionless Aridity potential is calculated as the monthly mean values of the ratio between potential evapotranspiration and precipitation over a 30 year period. Potential evapotranspiration is the modelled evapotranspiration when there is abundant water. For future periods the indicator is given as a relative change against the reference period (1971-2000). Aridity potential Dimensionless Aridity potential is calculated as the monthly mean values of the ratio between potential evapotranspiration and precipitation over a 30 year period. Potential evapotranspiration is the modelled evapotranspiration when there is abundant water. For future periods the indicator is given as a relative change against the reference period (1971-2000). Flood recurrence m3 s-1 Return values of annual maximum river discharge. Data are provided as the 2, 5, 10 and 50 year return period of annual daily maximum river discharge estimated using a Gumbel distribution. For future periods the indicator is given as a relative change against the reference period (1971-2000). Flood recurrence m3 s-1 Return values of annual maximum river discharge. Data are provided as the 2, 5, 10 and 50 year return period of annual daily maximum river discharge estimated using a Gumbel distribution. For future periods the indicator is given as a relative change against the reference period (1971-2000). Maximum river discharge m3 s-1 Maximum river discharge is calculated as the mean annual daily maximum discharge over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Maximum river discharge m3 s-1 Maximum river discharge is calculated as the mean annual daily maximum discharge over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Mean runoff mm month-1 Runoff is defined as the sum of surface and subsurface runoff to streams for each grid cell or catchment. The indicator is calculated as the monthly or annual mean values of daily runoff averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Mean runoff mm month-1 Runoff is defined as the sum of surface and subsurface runoff to streams for each grid cell or catchment. The indicator is calculated as the monthly or annual mean values of daily runoff averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Mean soil moisture Dimensionless Soil moisture is the water stored in the soil and is affected by precipitation, temperature, soil characteristics, and more. The soil moisture is defined slightly differently in different hydrological models, and is here generally defined as soil moisture in the root zone as fraction of the field capacity volume. Data are provided as monthly or annual mean values, averaged over a 30 years period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Mean soil moisture Dimensionless Soil moisture is the water stored in the soil and is affected by precipitation, temperature, soil characteristics, and more. The soil moisture is defined slightly differently in different hydrological models, and is here generally defined as soil moisture in the root zone as fraction of the field capacity volume. Data are provided as monthly or annual mean values, averaged over a 30 years period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Minimum river discharge m3 s-1 Minimum river discharge is calculated as the mean annual daily minimum discharge over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Minimum river discharge m3 s-1 Minimum river discharge is calculated as the mean annual daily minimum discharge over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). River discharge m3 s-1 Volume rate of water flow that is transported through a given cross-sectional area. It is synonymous to streamflow. The essential climate variable (ECV) data are provided at daily resolution. The climate impact indicator (CII) of river discharge is calculated as the monthly or annual mean values of daily runoff averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). River discharge m3 s-1 Volume rate of water flow that is transported through a given cross-sectional area. It is synonymous to streamflow. The essential climate variable (ECV) data are provided at daily resolution. The climate impact indicator (CII) of river discharge is calculated as the monthly or annual mean values of daily runoff averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Volume rate of water flow that is transported through a given cross-sectional area. It is synonymous to streamflow. The essential climate variable (ECV) data are provided at daily resolution. The climate impact indicator (CII) of river discharge is calculated as the monthly or annual mean values of daily runoff averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Nitrogen concentration in catchments mg L-1 Nitrogen concentration is the mass of nitrogen divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total nitrogen concentration, from a catchment averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Nitrogen concentration in catchments mg L-1 Nitrogen concentration is the mass of nitrogen divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total nitrogen concentration, from a catchment averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Nitrogen concentration in local streams mg L-1 Nitrogen concentration is the mass of nitrogen divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total nitrogen concentration, from a local stream averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Nitrogen concentration in local streams mg L-1 Nitrogen concentration is the mass of nitrogen divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total nitrogen concentration, from a local stream averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Nitrogen load in catchments kg year-1 kg month-1 Nitrogen load is the product of the river discharge volume and the nitrogen concentrations. The indicator is calculated as the annual (kg/year) or monthly (kg/month) mean values of total nitrogen load from a catchment averaged over of a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Nitrogen load in catchments kg year-1 kg month-1 kg year-1 kg month-1 Nitrogen load is the product of the river discharge volume and the nitrogen concentrations. The indicator is calculated as the annual (kg/year) or monthly (kg/month) mean values of total nitrogen load from a catchment averaged over of a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Phosphorus concentration in catchments mg L-1 Phosphorus concentration is the mass of phosphorus divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total phosphorus concentration, from a catchment averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Phosphorus concentration in catchments mg L-1 Phosphorus concentration is the mass of phosphorus divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total phosphorus concentration, from a catchment averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Phosphorus concentration in local streams mg L-1 Phosphorus concentration is the mass of phosphorus divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total phosphorus concentration, from a local stream averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Phosphorus concentration in local streams mg L-1 Phosphorus concentration is the mass of phosphorus divided by the volume of water. The indicator is calculated as the monthly or annual mean values of total phosphorus concentration, from a local stream averaged over a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Phosphorus load in catchments kg year-1 kg month-1 Phosphorus load is the product of the river discharge volume and the phosphorus concentrations. The indicator is calculated as the annual (kg/year) or monthly (kg/month) mean values of total phosphorus load from a catchment averaged over of a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Total Phosphorus load in catchments kg year-1 kg month-1 kg year-1 kg month-1 Phosphorus load is the product of the river discharge volume and the phosphorus concentrations. The indicator is calculated as the annual (kg/year) or monthly (kg/month) mean values of total phosphorus load from a catchment averaged over of a 30 year period. For future periods the indicator is given as a relative change against the reference period (1971-2000). Water temperature in catchments oC Water temperature is the simulated water temperature in a catchment. The indicator is calculated as mean annual values of water temperature for a 30 years period.For future periods the indicator is given as an absolute change against the reference period (1971-2000). Water temperature in catchments oC Water temperature is the simulated water temperature in a catchment. The indicator is calculated as mean annual values of water temperature for a 30 years period.For future periods the indicator is given as an absolute change against the reference period (1971-2000). Water temperature in local streams oC Water temperature is the simulated water temperature in local streams. The indicator is calculated as mean annual values of water temperature for a 30 years period.For future periods the indicator is given as an absolute change against the reference period (1971-2000). Water temperature in local streams oC Water temperature is the simulated water temperature in local streams. The indicator is calculated as mean annual values of water temperature for a 30 years period.For future periods the indicator is given as an absolute change against the reference period (1971-2000). Wetness actual mm month-1 Wetness actual is calculated as the monthly mean values of precipitation minus actual evapotranspiration averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Wetness actual mm month-1 Wetness actual is calculated as the monthly mean values of precipitation minus actual evapotranspiration averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Wetness potential mm month-1 Wetness potential is calculated as the monthly mean values of precipitation minus potential evapotranspiration averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). Wetness potential mm month-1 Wetness potential is calculated as the monthly mean values of precipitation minus potential evapotranspiration averaged over a 30 year period. For future periods the indicator is given as an absolute change against the reference period (1971-2000). 600 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-iberian-biscay-irish-ocean-wave-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=IBI_MULTIYEAR_WAV_005_006 Atlantic -Iberian Biscay Irish- Ocean Wave Reanalysis Short description: The IBI-MFC provides a high-resolution wave reanalysis product for the Iberia-Biscay-Ireland (IBI) area starting in 01/01/1993 and being regularly extended on a yearly basis. The model system is run by Nologin with the support of CESGA in terms of supercomputing resources. The MY model set-up is as much analogous as possible to the model set-up used in the IBI-MFC Near-Real-Time (NRT) analysis and forecast system. The model application is based on the MFWAM model developed by Meteo-France (MF), with the same grid of 5 km of horizontal resolution. Both, the MY and the NRT products, are fed by ECMWF hourly winds. Specifically, the MY system is forced by the ERA5 reanalysis wind data. As boundary conditions, the NRT system uses the 2D wave spectra from the Copernicus Marine GLOBAL forecast system, whereas the MY system is nested to the GLOBAL reanalysis. The product offers hourly instantaneous fields of different wave parameters, including Wave Height, Period and Direction for total spectrum and fields of Wind Wave (or wind sea), Primary Swell Wave and Secondary Swell for partitioned wave spectra. Product Citation: Please refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag…] http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (Product):https://doi.org/10.48670/moi-00030 https://doi.org/10.48670/moi-00030 601 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-sea-ice-concentration-charts-svalbard-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002 Arctic Ocean - Sea Ice Concentration Charts - Svalbard and Greenland Short description: For the Arctic Ocean - The operational sea ice services at MET Norway and DMI provides ice charts of the Arctic area covering Baffin Bay, Greenland Sea, Fram Strait and Barents Sea. The charts show the ice concentration in WMO defined concentration intervals. The three different types of ice charts (datasets) are produced from twice to several times a week: MET charts are produced every weekday. DMI regional charts are produced at irregular intervals daily and a supplemental DMI overview chart is produced twice weekly. DOI (product) :https://doi.org/10.48670/moi-00128 https://doi.org/10.48670/moi-00128 602 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-soil-erosion-explorer-italy https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-soil-erosion-explorer-italy app-soil-erosion-explorer-italy The application enables the visualisation of soil erosion indicators, soil loss and rainfall erosivity (R factor), for Italy. These indicators combine land-based soil susceptibility variables with rainfall time series from: the ERA5-Land climate reanalysis (for the 30-year reference period 1981-2010); an ensemble of bias-corrected EURO-CORDEX simulations for near future (2021-2050) and far future (2051-2080) 30-year climatological periods under the Representative Concentration Pathway corresponding to a radiative forcing of 8.5 W/m2 in 2100 RCP 8.5. the ERA5-Land climate reanalysis (for the 30-year reference period 1981-2010); an ensemble of bias-corrected EURO-CORDEX simulations for near future (2021-2050) and far future (2051-2080) 30-year climatological periods under the Representative Concentration Pathway corresponding to a radiative forcing of 8.5 W/m2 in 2100 RCP 8.5. Spatial variability and basic statistics for the soil erosion indicators can be explored at different territorial unit levels (Italy and its geographical areas, regions or provinces). The impact of soil erosion is important as it is a cross-sectoral issue affecting many rural areas in Italy and causes significant physical damage to land and high economic costs. Users can select the indicator (Soil loss or R factor) from a drop-down menu on top of the map window, and then they can choose the desired scenario using the selection buttons in the upper right of the map window. An interactive livemap displays the selected soil erosion indicator for the desired scenario. Navigating and/or zooming in on the map makes different territorial units gradually visible; from the whole country to geographical regions, to administrative regions up to local Italian provinces. Clicking on a selected territorial unit will open a window on the right side of the map providing a focused view on the selected territorial unit. The focused view contains: A series of map plots for the different scenarios, allowing a user to scroll through them. An interactive boxplot graph that displays the main statistics across the entire territorial unit in terms of minimum, maximum, median and the 25th and 75th percentiles for each scenario. Users can zoom on a boxplot to highlight statistics about a particular period by interacting with the graph. A series of map plots for the different scenarios, allowing a user to scroll through them. An interactive boxplot graph that displays the main statistics across the entire territorial unit in terms of minimum, maximum, median and the 25th and 75th percentiles for each scenario. Users can zoom on a boxplot to highlight statistics about a particular period by interacting with the graph. An alternate application designed as a What-if analysis tool is available to explore how land use - relating to vegetation cover, management and soil-erosion protection practices - can impact the soil loss under different climate scenarios. Users are able to modify in the application the soil erosion factors related to land use, land cover and soil protection practices. alternate application User-selectable parameters User-selectable parameters Soil erosion indicators: Soil loss, representing the amount of soil detached per hectare on average every year Rainfall erosivity (R factor), representing the strength of rain drops in detaching soil particles Scenarios: Reference period 1981-2010 Near Future 2021-2050, ensemble mean under RCP 8.5 Near Future 2021-2050, as difference to reference period of the ensemble mean under RCP 8.5 Near Future 2021-2050, as relative difference to reference period of the ensemble mean under RCP 8.5 Far Future 2051-2080, ensemble mean under RCP 8.5 Far Future 2051-2080, as difference to reference period of the ensemble mean under RCP 8.5 Far Future 2051-2080, as relative difference to reference period of the ensemble mean under RCP 8.5 Soil erosion indicators: Soil loss, representing the amount of soil detached per hectare on average every year Rainfall erosivity (R factor), representing the strength of rain drops in detaching soil particles Scenarios: Reference period 1981-2010 Near Future 2021-2050, ensemble mean under RCP 8.5 Near Future 2021-2050, as difference to reference period of the ensemble mean under RCP 8.5 Near Future 2021-2050, as relative difference to reference period of the ensemble mean under RCP 8.5 Far Future 2051-2080, ensemble mean under RCP 8.5 Far Future 2051-2080, as difference to reference period of the ensemble mean under RCP 8.5 Far Future 2051-2080, as relative difference to reference period of the ensemble mean under RCP 8.5 INPUT VARIABLES Name Units Description Source R factor MJ mm ha-1 h-1 yr-1 This is the rainfall erosivity. It is the kinetic energy associated with rainfall-related mechanisms (raindrop impact or rate of associated runoff) used to quantify the effect of rainfall on sheet and rill erosion. It is computed according to six empirical models consolidated in the literature (cited in the documentation) using input data from time series of cumulated rainfall at monthly and/or annual time steps depending on each specific model. The values from the six models are then averaged to derive a single R factor value. The R factor is used in calculating the Revised Universal Soil Loss Equation (RUSLE). Soil erosion indicators for Italy from 1981 to 2080 Soil loss t ha-1 yr-1 Potential soil loss by rainfall-related rill and inter-rill erosion, obtained by Revised Universal Soil Loss Equation (RUSLE) approach. Soil erosion indicators for Italy from 1981 to 2080 INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source R factor MJ mm ha-1 h-1 yr-1 This is the rainfall erosivity. It is the kinetic energy associated with rainfall-related mechanisms (raindrop impact or rate of associated runoff) used to quantify the effect of rainfall on sheet and rill erosion. It is computed according to six empirical models consolidated in the literature (cited in the documentation) using input data from time series of cumulated rainfall at monthly and/or annual time steps depending on each specific model. The values from the six models are then averaged to derive a single R factor value. The R factor is used in calculating the Revised Universal Soil Loss Equation (RUSLE). Soil erosion indicators for Italy from 1981 to 2080 R factor MJ mm ha-1 h-1 yr-1 This is the rainfall erosivity. It is the kinetic energy associated with rainfall-related mechanisms (raindrop impact or rate of associated runoff) used to quantify the effect of rainfall on sheet and rill erosion. It is computed according to six empirical models consolidated in the literature (cited in the documentation) using input data from time series of cumulated rainfall at monthly and/or annual time steps depending on each specific model. The values from the six models are then averaged to derive a single R factor value. The R factor is used in calculating the Revised Universal Soil Loss Equation (RUSLE). Soil erosion indicators for Italy from 1981 to 2080 Soil erosion indicators for Italy from 1981 to 2080 Soil loss t ha-1 yr-1 Potential soil loss by rainfall-related rill and inter-rill erosion, obtained by Revised Universal Soil Loss Equation (RUSLE) approach. Soil erosion indicators for Italy from 1981 to 2080 Soil loss t ha-1 yr-1 Potential soil loss by rainfall-related rill and inter-rill erosion, obtained by Revised Universal Soil Loss Equation (RUSLE) approach. Soil erosion indicators for Italy from 1981 to 2080 Soil erosion indicators for Italy from 1981 to 2080 603 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-l4-significant-wave-height-nrt-satellite http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=WAVE_GLO_PHY_SWH_L4_NRT_014_003 GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS Short description: Near-Real-Time gridded multi-mission merged satellite significant wave height. Only valid data are included. This product is processed in Near-Real-Time by the WAVE-TAC multi-mission altimeter data processing system and is based on CMEMS level-3 SWH datasets (see the product WAVE_GLO_WAV_L3_SWH_NRT_OBSERVATIONS_014_001). It merges along-track SWH data from the following missions: Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2, CFOSAT and HaiYang-2B. The resulting gridded product has a 2° horizontal resolution and is produced daily. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity. DOI (product) :https://doi.org/10.48670/moi-00180 https://doi.org/10.48670/moi-00180 604 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-iberian-biscay-irish-ocean-biogeochemical http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=IBI_ANALYSISFORECAST_BGC_005_004 Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast Short description: The IBI-MFC provides a high-resolution biogeochemical analysis and forecast product covering the European waters, and more specifically the Iberia–Biscay–Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as daily averaged forecasts with a horizon of 10 days (updated on a weekly basis) are available on the catalogue. To this aim, an online coupled physical-biogeochemical operational system is based on NEMO-PISCES at 1/36° and adapted to the IBI area, being Mercator-Ocean in charge of the model code development. PISCES is a model of intermediate complexity, with 24 prognostic variables. It simulates marine biological productivity of the lower trophic levels and describes the biogeochemical cycles of carbon and of the main nutrients (P, N, Si, Fe). The product provides daily and monthly averages of the main biogeochemical variables: chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, surface partial pressure of carbon dioxide, and zooplankton. Product Citation: Please refer to our Technical FAQ for citing products.[http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag…] http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (Product):https://doi.org/10.48670/moi-00026 https://doi.org/10.48670/moi-00026 605 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-yearly-co2-sink-multi-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_HEALTH_carbon_co2_flux_integrated Global Ocean Yearly CO2 Sink from Multi-Observations Reprocessing DEFINITION The global yearly ocean CO2 sink represents the ocean uptake of CO2 from the atmosphere computed over the whole ocean. It is expressed in PgC per year. The ocean monitoring index is presented for the period 1985 to 2021. The yearly estimate of the ocean CO2 sink corresponds to the mean of a 100-member ensemble of CO2 flux estimates (Chau et al. 2022). The range of an estimate with the associated uncertainty is then defined by the empirical 68% interval computed from the ensemble. CONTEXT Since the onset of the industrial era in 1750, the atmospheric CO2 concentration has increased from about 277±3 ppm (Joos and Spahni, 2008) to 412.44±0.1 ppm in 2020 (Dlugokencky and Tans, 2020). By 2011, the ocean had absorbed approximately 28 ± 5% of all anthropogenic CO2 emissions, thus providing negative feedback to global warming and climate change (Ciais et al., 2013). The ocean CO2 sink is evaluated every year as part of the Global Carbon Budget (Friedlingstein et al. 2022). The uptake of CO2 occurs primarily in response to increasing atmospheric levels. The global flux is characterized by a significant variability on interannual to decadal time scales largely in response to natural climate variability (e.g., ENSO) (Friedlingstein et al. 2022, Chau et al. 2022). CMEMS KEY FINDINGS The rate of change of the integrated yearly surface downward flux has increased by 0.04±0.03e-1 PgC/yr2 over the period 1985-2021. The yearly flux time series shows a plateau in the 90s followed by an increase since 2000 with a growth rate of 0.06±0.04e-1 PgC/yr2. In 2021 (resp. 2020), the global ocean CO2 sink was 2.41±0.13 (resp. 2.50±0.12) PgC/yr. The average over the full period is 1.61±0.10 PgC/yr with an interannual variability (temporal standard deviation) of 0.46 PgC/yr. In order to compare these fluxes to Friedlingstein et al. (2022), the estimate of preindustrial outgassing of riverine carbon of 0.61 PgC/yr, which is in between the estimate by Jacobson et al. (2007) (0.45±0.18 PgC/yr) and the one by Resplandy et al. (2018) (0.78±0.41 PgC/yr) needs to be added. A full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020) and in Chau et al. (2022). DOI (product):https://doi.org/10.48670/moi-00223 https://doi.org/10.48670/moi-00223 606 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-ocean-wave-timeseries https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-ocean-wave-timeseries sis-ocean-wave-timeseries The dataset presents time series of the coastal wave climate based upon ocean surface wave parameters computed for a European-wide domain. This dataset provides an understanding of the wave climate under the impact of climate change for the Northwest European Shelf and Mediterranean Sea. It provides added value for various coastal sectors and studies such as port, shipping, and coastal management. The ocean surface wave fields are computed using the ECMWF's Wave Model (SAW) forced by surface wind and accounting for ice coverage in polar latitudes. The wave climate is defined by means of the integrated wave spectral parameters such as the significant wave height and the peak wave period. In order to assess the impact of climate change on the ocean's surface wave field, the SAW model is run for three different climate scenarios: the current climate (also termed historical), and two Representative Concentration Pathway (RCP) scenarios that correspond to an optimistic emission scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century often called the business-as-usual scenario (RCP8.5). The wave climate in these scenarios are simulated using wind forcing from a member of the EURO-CORDEX climate model ensemble - the HIRHAM5 regional climate model downscaled from the global climate model EC-EARTH. Given that the projections of these climate scenarios are based on a single combination of the regional and global climate models, users of these data should take in consideration that there is an inevitable underestimation of the uncertainty associated with this dataset. In addition to the three climate scenarios, the time series are also computed using ERA5 reanlysis wind forcing. This provides the recent historical wave climate that may be used, for example, to look at specific events in the past. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Point data Horizontal coverage European coastline along the 20 m bathymetric contour Horizontal resolution 30 km Vertical coverage Surface Vertical resolution Single level Temporal coverage Historical: from 1976 to 2005 ERA5 reanalysis: from 2001 to 2017 RCP8.5: from 2041 to 2100 RCP4.5: from 2041 to 2100 Temporal resolution Hourly File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Point data Data type Point data Horizontal coverage European coastline along the 20 m bathymetric contour Horizontal coverage European coastline along the 20 m bathymetric contour Horizontal resolution 30 km Horizontal resolution 30 km Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage Historical: from 1976 to 2005 ERA5 reanalysis: from 2001 to 2017 RCP8.5: from 2041 to 2100 RCP4.5: from 2041 to 2100 Temporal coverage Historical: from 1976 to 2005 ERA5 reanalysis: from 2001 to 2017 RCP8.5: from 2041 to 2100 RCP4.5: from 2041 to 2100 Historical: from 1976 to 2005 ERA5 reanalysis: from 2001 to 2017 RCP8.5: from 2041 to 2100 RCP4.5: from 2041 to 2100 Temporal resolution Hourly Temporal resolution Hourly File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Mean wave direction ° This parameter is the mean direction of ocean/sea surface waves generated by local winds and swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. Mean wave period s This parameter represents the mean period of the ocean waves generated by local winds and swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the mean value of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and swell. The wave period is the time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. Significant wave height m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. Wave spectral directional width Dimensionless This parameter indicates whether waves (generated by local winds and associated with swell) are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Mean wave direction ° This parameter is the mean direction of ocean/sea surface waves generated by local winds and swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. Mean wave direction ° This parameter is the mean direction of ocean/sea surface waves generated by local winds and swell. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum. Mean wave period s This parameter represents the mean period of the ocean waves generated by local winds and swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the mean value of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. Mean wave period s This parameter represents the mean period of the ocean waves generated by local winds and swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the mean value of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and swell. The wave period is the time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. Peak wave period s This parameter represents the period of the most energetic ocean waves generated by local winds and swell. The wave period is the time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is calculated from the reciprocal of the frequency corresponding to the largest value (peak) of the frequency wave spectrum. The frequency wave spectrum is obtained by integrating the two-dimensional wave spectrum over all directions. Significant wave height m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. Significant wave height m This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). The wave spectrum can be decomposed into wind-sea waves, which are directly affected by local winds, and swell, the waves that were generated by the wind at a different location and time. This parameter takes account of both. More strictly, this parameter is four times the square root of the integral over all directions and all frequencies of the two-dimensional wave spectrum. Wave spectral directional width Dimensionless This parameter indicates whether waves (generated by local winds and associated with swell) are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). Wave spectral directional width Dimensionless This parameter indicates whether waves (generated by local winds and associated with swell) are coming from similar directions or from a wide range of directions. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). Many ECMWF wave parameters (such as the mean wave period) give information averaged over all wave frequencies and directions, so do not give any information about the distribution of wave energy across frequencies and directions. This parameter gives more information about the nature of the two-dimensional wave spectrum. This parameter is a measure of the range of wave directions for each frequency integrated across the two-dimensional spectrum. This parameter takes values between 0 and the square root of 2. Where 0 corresponds to a uni-directional spectrum (i.e., all wave frequencies from the same direction) and the square root of 2 indicates a uniform spectrum (i.e., all wave frequencies from a different direction). 607 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-hydrology-variables-derived-seasonal-reforecast https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-hydrology-variables-derived-seasonal-reforecast sis-hydrology-variables-derived-seasonal-reforecast This dataset provides hydrological seasonal reforecasts of monthly mean river discharge across Europe for the period 1993 to 2016. The first is an E-HYPE multi-model system comprising eight model realisations using a catchment-based resolution. The second comprises the E-HYPEgrid, VIC-WUR and LISFLOOD-EFAS hydrological models at a 5km gridded resolution. The initialisation of the hydrological seasonal forecast uses the European Flood Awareness System (EFAS) daily gridded meteorological observations (EFAS-Meteo) up until the start of the reforecast, and the subsequent integration of the meteorological seasonal reforecasts using all 25 members of the ECMWF seasonal forecast system 5 (SEAS5) meteorological reforecasts for the period January 1993 to December 2016. Seasonal reforecasts are produced to past dates but using the same model as the operaional forecast. A bias adjustment step using quantile mapping for temperature and precipitation was used for the E-HYPE and ViC-WUR models to minimize drift in the forecasts caused by biases in SEAS5 compared to EFAS-Meteo. The final output is in the form of monthly mean river discharge with a lead time of seven months for each reforcast starting date. The context of the forecasts is provided by upper and lower terciles of the historical EFAS-Meteo driven simulation for each month of the year. This dataset is produced by the Swedish Meteorological and Hydrological Institute on behalf of the Copernicus Climate Change Service in collaboration with the Copernicus Emergency Management Service (CEMS). DATA DESCRIPTION Data type Gridded and catchments Projection Lambert azimuthal equal area grid for the 5km grid Horizontal coverage Europe Horizontal resolution 5km grid and catchments Vertical coverage Surface Vertical resolution Single level Temporal coverage January 1993 to December 2016 Temporal resolution Monthly Temporal gaps No gaps File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v.1.6 Versions The current version of the multimodel dataset is v1.0. The EFAS forecasts used are v4 released 2020-10-14. For more information on versions we refer to the documentation. Update frequency Monthly DATA DESCRIPTION DATA DESCRIPTION Data type Gridded and catchments Data type Gridded and catchments Projection Lambert azimuthal equal area grid for the 5km grid Projection Lambert azimuthal equal area grid for the 5km grid Horizontal coverage Europe Horizontal coverage Europe Horizontal resolution 5km grid and catchments Horizontal resolution 5km grid and catchments Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage January 1993 to December 2016 Temporal coverage January 1993 to December 2016 Temporal resolution Monthly Temporal resolution Monthly Temporal gaps No gaps Temporal gaps No gaps File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v.1.6 Conventions Climate and Forecast (CF) Metadata Convention v.1.6 Versions The current version of the multimodel dataset is v1.0. The EFAS forecasts used are v4 released 2020-10-14. For more information on versions we refer to the documentation. Versions The current version of the multimodel dataset is v1.0. The EFAS forecasts used are v4 released 2020-10-14. For more information on versions we refer to the documentation. Update frequency Monthly Update frequency Monthly MAIN VARIABLES Name Units Description Brier skill score above normal conditions Dimensionless Monthly skill metrics as fairBSSan (fair Brier skill score above the 66 percentile) against a climate reference over the reference period 1993-2016. The Brier skill score is a strictly proper score function that measures the accuracy of probabilistic forecasts. For more information on how the Brier skill score was calculated we refer to the documentation. Brier skill score below normal conditions Dimensionless Monthly skill metrics as fairBSSbn (fair Brier skill score below the 33 percentile) against a climate reference over the reference period 1993-2016. The Brier skill score is a strictly proper score function that measures the accuracy of probabilistic forecasts. For more information on how the Brier skill score was calculated we refer to the documentation. Continuous ranked probability skill score Dimensionless Monthly skill metrics as fairCRPSS (fair continuous ranked probability skill score) against a climate reference over the reference period 1993-2016. The fairCRPSS skill score is a proper score function that measures the performance of probabilistic forecasts. For more information on how the fairCRPSS was calculated we refer to the documentation. Fair ranked probability skill score Dimensionless Monthly skill metrics as fairRPSS (fair ranked probability skill score) against a climate reference over the reference period 1993-2016. The fairRPSS skill score is a proper score function that measures the performance of probabilistic forecasts. For more information on how the fairRPSS was calculated we refer to the documentation. Reference river discharge lower tercile m3 s-1 The lower tercile of the river discharge for the reference period. Reference river discharge upper tercile m3 s-1 The upper tercile of the river discharge for the reference period. River discharge m3 s-1 Volume rate of water flow, including sediments, chemical and biological material in the river channel averaged over a time step through a cross-section. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Brier skill score above normal conditions Dimensionless Monthly skill metrics as fairBSSan (fair Brier skill score above the 66 percentile) against a climate reference over the reference period 1993-2016. The Brier skill score is a strictly proper score function that measures the accuracy of probabilistic forecasts. For more information on how the Brier skill score was calculated we refer to the documentation. Brier skill score above normal conditions Dimensionless Monthly skill metrics as fairBSSan (fair Brier skill score above the 66 percentile) against a climate reference over the reference period 1993-2016. The Brier skill score is a strictly proper score function that measures the accuracy of probabilistic forecasts. For more information on how the Brier skill score was calculated we refer to the documentation. Brier skill score below normal conditions Dimensionless Monthly skill metrics as fairBSSbn (fair Brier skill score below the 33 percentile) against a climate reference over the reference period 1993-2016. The Brier skill score is a strictly proper score function that measures the accuracy of probabilistic forecasts. For more information on how the Brier skill score was calculated we refer to the documentation. Brier skill score below normal conditions Dimensionless Monthly skill metrics as fairBSSbn (fair Brier skill score below the 33 percentile) against a climate reference over the reference period 1993-2016. The Brier skill score is a strictly proper score function that measures the accuracy of probabilistic forecasts. For more information on how the Brier skill score was calculated we refer to the documentation. Continuous ranked probability skill score Dimensionless Monthly skill metrics as fairCRPSS (fair continuous ranked probability skill score) against a climate reference over the reference period 1993-2016. The fairCRPSS skill score is a proper score function that measures the performance of probabilistic forecasts. For more information on how the fairCRPSS was calculated we refer to the documentation. Continuous ranked probability skill score Dimensionless Monthly skill metrics as fairCRPSS (fair continuous ranked probability skill score) against a climate reference over the reference period 1993-2016. The fairCRPSS skill score is a proper score function that measures the performance of probabilistic forecasts. For more information on how the fairCRPSS was calculated we refer to the documentation. Fair ranked probability skill score Dimensionless Monthly skill metrics as fairRPSS (fair ranked probability skill score) against a climate reference over the reference period 1993-2016. The fairRPSS skill score is a proper score function that measures the performance of probabilistic forecasts. For more information on how the fairRPSS was calculated we refer to the documentation. Fair ranked probability skill score Dimensionless Monthly skill metrics as fairRPSS (fair ranked probability skill score) against a climate reference over the reference period 1993-2016. The fairRPSS skill score is a proper score function that measures the performance of probabilistic forecasts. For more information on how the fairRPSS was calculated we refer to the documentation. Reference river discharge lower tercile m3 s-1 The lower tercile of the river discharge for the reference period. Reference river discharge lower tercile m3 s-1 The lower tercile of the river discharge for the reference period. Reference river discharge upper tercile m3 s-1 The upper tercile of the river discharge for the reference period. Reference river discharge upper tercile m3 s-1 The upper tercile of the river discharge for the reference period. River discharge m3 s-1 Volume rate of water flow, including sediments, chemical and biological material in the river channel averaged over a time step through a cross-section. River discharge m3 s-1 Volume rate of water flow, including sediments, chemical and biological material in the river channel averaged over a time step through a cross-section. 608 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/vegetation-condition-index-2013-2020-raster-1-km-global http://land.copernicus.eu/global/products/vci Vegetation Condition Index 2013-2020 (raster 1 km), global continents, 10-daily - version 1 The Vegetation Condition Indicator (VCI) is a categorical type of difference vegetation index. The VCI compares the observed NDVI to the range of values observed in the same period in previous years. The VCI is expressed in % and gives an idea where the observed value is situated between the extreme values (minimum and maximum) in the previous years. Lower and higher values indicate bad and good vegetation state conditions, respectively. 609 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/vegetation-condition-index-2013-2020-raster-1-km-global-0 http://land.copernicus.eu/global/products/vci Vegetation Condition Index 2013-2020 (raster 1 km), global tiles, 10-daily - version 1 The Vegetation Condition Indicator (VCI) is a categorical type of difference vegetation index. The VCI compares the observed NDVI to the range of values observed in the same period in previous years. The VCI is expressed in % and gives an idea where the observed value is situated between the extreme values (minimum and maximum) in the previous years. Lower and higher values indicate bad and good vegetation state conditions, respectively. 610 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-situ-near-real-time-observations-ocean http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_GLO_PHY_UV_DISCRETE_NRT_013_048 Global Ocean- in-situ Near real time observations of ocean currents Short description This product is entirely dedicated to ocean current data observed in near-real time. Current data from 3 different types of instruments are distributed: * The near-surface zonal and meridional velocities calculated along the trajectories of the drifting buoys which are part of the DBCP’s Global Drifter Program. These data are delivered together with wind stress components and surface temperature. * The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency radars that are part of the European High Frequency radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata. * The zonal and meridional velocities, at parking depth and in surface, calculated along the trajectories of the floats which are part of the Argo Program. DOI (product) :https://doi.org/10.48670/moi-00041 https://doi.org/10.48670/moi-00041 611 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/normalised-difference-vegetation-index-statistics-1999-0 https://land.copernicus.eu/global/access Normalised Difference Vegetation Index Statistics 1999-2019 (raster 1 km), global, 10-daily - version 3 The Normalised Difference Vegetation Index (NDVI) is a widely used, dimensionless index that is indicative for vegetation density and is defined as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared bands, and Red to the reflectance in the red bands. The time series of 10-daily NDVI 1km version 3 observations is used to calculate Long Term Statistics (LTS), over the 20-year period 1999-2019, and Short Term Statistics (STS), over the 5-year period (2015-2019), for each of the 36 10-daily periods (dekads) of the year. The calculated statistics include the minimum, median, maximum, mean, standard deviation and the number of observations in the covered time series period. These statistics can be used as a reference for actual NDVI observations, which allows monitoring of anomalous vegetation conditions. 612 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-situ-near-real-time-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_MED_PHYBGCWAV_DISCRETE_MYNRT_013_035 Mediterranean Sea- In-Situ Near Real Time Observations Short description: Mediterranean Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average DOI (product) :https://doi.org/10.48670/moi-00044 https://doi.org/10.48670/moi-00044 613 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-subsurface-salinity-trend-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTIC_OMI_TEMPSAL_Stz_trend Baltic Sea Subsurface Salinity trend from Reanalysis DEFINITION The subsurface salinity trends have been derived from regional reanalysis and forecast modelling results of the CMS BAL MFC group for the Baltic Sea (product references BALTICSEA_REANALYSIS_PHY_003_011). The salinity trend has been obtained through a linear fit for each time series of horizontally averaged (13 °E - 31 °E and 53 °N - 66 °N; excluding the Skagerrak strait) annual salinity and at each depth level. CONTEXT The Baltic Sea is a brackish semi-enclosed sea in North-Eastern Europe. The surface salinity varies horizontally from ~10 near the Danish Straits down to ~2 at the northernmost and easternmost sub-basins of the Baltic Sea. The halocline, a vertical layer with rapid changes of salinity with depth that separates the well-mixed surface layer from the weakly stratified layer below, is located at the depth range of 60-80 metres (Matthäus, 1984). The bottom layer salinity below the halocline depth varies from 15 in the south down to 3 in the northern Baltic Sea (Väli et al., 2013). The long-term salinity is determined by net precipitation and river discharge as well as saline water inflows from the North Sea (BACCII Author Team, 2015). Long-term salinity decrease may reduce the occurrence and biomass of the Fucus vesiculosus - Idotea balthica association/symbiotic aggregations (Kotta et al., 2019). Changes in salinity and oxygen content affect the survival of the Baltic cod eggs (Raudsepp et al, 2019; von Dewitz et al., 2018). CMEMS KEY FINDINGS The subsurface salinity over the 1993-2021 period shows no trend in the surface layer of 40-m. In the intermediate layer between 50 to 80 m the salinity trend increases from 0.01 to 0.03 per year. This layer coincides with the upper halocline in the Baltic Sea. Maximum salinity trend of about 0.05 per year is at a depth of 150 metres. Within and below the halocline i.e., at the depth range between 60 and 180 metres the confidence interval of the trend values is wide, which indicates that this depth range is relatively dynamic. DOI (product):https://doi.org/10.48670/moi-00207 https://doi.org/10.48670/moi-00207 614 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-l4-significant-wave-height-reprocessed http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=WAVE_GLO_PHY_SWH_L4_MY_014_007 GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS Short description: Multi-Year gridded multi-mission merged satellite significant wave height. Only valid data are included. This Multi-Year product is processed by the WAVE-TAC multi-mission altimeter data processing system and is based on CMEMS Multi-Year level-3 SWH datasets (see the product WAVE_GLO_PHY_SWH_L3_MY_014_005). It merges along-track SWH data from the following missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa, Jason-3 and CFOSAT. The resulting gridded product has a 2° horizontal resolution and is produced daily. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity. DOI (product) :https://doi.org/10.48670/moi-00177 https://doi.org/10.48670/moi-00177 615 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-marine-properties https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-marine-properties sis-marine-properties The dataset contains model projections of changes in marine physics and biogeochemistry and the lower trophic levels of the marine food web across the Northwest European Shelf and Mediterranean Sea out to the year 2100. The dataset has been produced using the marine ecosystem model, ERSEM v15.06 (European Regional Seas Ecosystem Model), coupled to the regional ocean circulation models, POLCOMS (the Proudman Oceanographic Laboratory Coastal Ocean Modelling System) and NEMO (Nucleus for European Modelling of the Ocean) using the FABM (Framework for Aquatic Biogeochemical Models) coupler. ERSEM is designed to simulate the cycles of carbon and the major nutrient elements nitrogen, phosphorous and silicon within the marine environment. Organisms at the base of the marine food web – the microscopic aquatic plants known as phytoplankton – play an integral role in these cycles and are explicitly represented in the model. So are the microscopic marine organisms which feed upon them and provide a vital link to commercially exploited species of fish and shell fish higher up the food chain. POLCOMS and NEMO are well-established physical models with the ability to simulate regions that include both the deep ocean and the continental shelf. They track the movement of water and transfer of energy and momentum in three dimensions, enabling the water temperature, salinity (the salt content of sea water) and currents to be modelled. The coupled POLCOMS/NEMO-ERSEM system makes it possible to include the effects of physical transport and mixing processes on the spatiotemporal distribution of nutrients, phytoplankton, and other components of the marine ecosystem. The dataset includes physical variables, such as horizontal velocity components, temperature and salinity; and a range of biogeochemical variables, including the concentration of dissolved oxygen and the concentration of different nutrients. These variables were simulated under two future scenarios, based on different Representative Concentration Pathways (RCP) for future greenhouse gas emissions: the intermediate scenario, RCP4.5, in which greenhouse gas emissions peak around 2040 before declining; and the business as usual scenario, RCP8.5, in which greenhouse gas emissions continue to rise throughout the century. The hydrodynamic biogeochemical models were each driven by a global climate model generated for the Coupled Model Inter-comparison Project Phase 5 (CMIP5) at the open ocean boundaries, in combination with downscaled atmospheric data generated using the Swedish Meteorological and Hydrological Institute (SMHI) Rossby Centre Regional Atmospheric Model (RCA4). This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) Horizontal resolution POLCOMS-ERSEM: 0.1° x 0.1° (approx. 11km) NEMO-ERSEM: 0.06° x 0.06° (approx. 7km) Vertical coverage 0-5500 m below sea level Vertical resolution POLCOMS-ERSEM: 43 vertical levels NEMO-ERSEM: 43 vertical levels Temporal coverage POLCOMS-ERSEM: 2006 up to 2100 NEMO-ERSEM: 2006 up to 2050 Temporal resolution Daily and/or monthly. Variable dependent - the download form indicates the time aggregation available for each variable. File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) Horizontal coverage POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) POLCOMS-ERSEM: Northwest European Shelf and Mediterranean Sea (20W to 37E, 11N to 65N) NEMO-ERSEM: Northwest European Shelf (20W to 13E, 40N to 65N) Horizontal resolution POLCOMS-ERSEM: 0.1° x 0.1° (approx. 11km) NEMO-ERSEM: 0.06° x 0.06° (approx. 7km) Horizontal resolution POLCOMS-ERSEM: 0.1° x 0.1° (approx. 11km) NEMO-ERSEM: 0.06° x 0.06° (approx. 7km) POLCOMS-ERSEM: 0.1° x 0.1° (approx. 11km) NEMO-ERSEM: 0.06° x 0.06° (approx. 7km) Vertical coverage 0-5500 m below sea level Vertical coverage 0-5500 m below sea level Vertical resolution POLCOMS-ERSEM: 43 vertical levels NEMO-ERSEM: 43 vertical levels Vertical resolution POLCOMS-ERSEM: 43 vertical levels NEMO-ERSEM: 43 vertical levels POLCOMS-ERSEM: 43 vertical levels NEMO-ERSEM: 43 vertical levels Temporal coverage POLCOMS-ERSEM: 2006 up to 2100 NEMO-ERSEM: 2006 up to 2050 Temporal coverage POLCOMS-ERSEM: 2006 up to 2100 NEMO-ERSEM: 2006 up to 2050 POLCOMS-ERSEM: 2006 up to 2100 NEMO-ERSEM: 2006 up to 2050 Temporal resolution Daily and/or monthly. Variable dependent - the download form indicates the time aggregation available for each variable. Temporal resolution Daily and/or monthly. Variable dependent - the download form indicates the time aggregation available for each variable. File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. 616 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-hourly-reprocessed-sea-surface-wind-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=WIND_GLO_PHY_L4_MY_012_006 Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model Short description: For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 and 0.25 degrees horizontal spatial resolution. Scatterometer observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF ERA5 model fields. Bias corrections are based on scatterometer observations from Metop-A, Metop-B, Metop-C ASCAT (0.125 degrees) and QuikSCAT SeaWinds (0.25 degrees). The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product. DOI (product) :https://doi.org/10.48670/moi-00185 https://doi.org/10.48670/moi-00185 617 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-oxygen-trend-observations-reprocessing http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BLKSEA_OMI_HEALTH_oxygen_trend Black Sea Oxygen Trend from Observations Reprocessing DEFINITION The oxygenation status of the Black Sea open basin is described by three complementary indicators, derived from vertical profiles and spatially averaged over the Black Sea open basin (depth > 50m). (1) The oxygen penetration depth is the depth at which [O2] < 20µM, expressed in [m]. (2) The oxygen penetration density is the potential density anomaly at the oxygen penetration depth [kg/m³]. (3) The oxygen inventory is the vertically integrated oxygen content [mol O2/m²]. The 20µM threshold was chosen to minimize the indicator sensitivity to sensor’s precision. Those three metrics are complementary: Oxygen penetration depth is more easily understood, but present more spatial variability. Oxygen penetration density helps in dissociating biogeochemical processes from shifts in the physical structure. Although less intuitive, the oxygen inventory is a more integrative diagnostic and its definition is more easily transposed to other areas. CONTEXT The Black Sea is permanently stratified, due to the contrast in density between large riverine and Mediterranean inflows. This stratification restrains the ventilation of intermediate and deep waters and confines, within a restricted surface layer, the waters that are oxygenated by photosynthesis and exchanges with the atmosphere. The vertical extent of the oxic layer determines the volume of habitat available for pelagic populations (Ostrovskii and Zatsepin 2011, Sakınan and Gücü 2017) and present spatial and temporal variations (Murray et al. 1989; Tugrul et al. 1992; Konovalov and Murray 2001). At long and mid-term, these variations can be monitored with three metrics (Capet et al. 2016), derived from the vertical profiles that can obtained from traditional ship casts or autonomous Argo profilers (Stanev et al., 2013). A large source of uncertainty associated with the spatial and temporal average of those metrics stems from the small number of Argo floats, scarcely adequate to sample the known spatial variability of those metrics. CMEMS KEY FINDINGS During the past 60 years, the vertical extent of the Black Sea oxygenated layer has narrowed from 140m to 90m (Capet et al. 2016). The Argo profilers active for 2016 suggested an ongoing deoxygenation trend and indicated an average oxygen penetration depth of 72m at the end of 2016, the lowest value recorded during the past 60 years. The oxygenation of subsurface water is closely related to the intensity of cold water formation, an annual ventilation processes which has been recently limited by warmer-than-usual winter air temperature (Capet et al. 2020). In 2017, 2018 and 2020, cold waters formation resulted in a partial reoxygenation of the intermediate layer. Yet, such ventilation has been lacking in winter 2020-2021, and the updated 2021 indicators reveals the lowest oxygen inventory ever reported in this OMI time series. This results in significant detrimental trends now depicted also over the Argo period (2012-2021). DOI (product):https://doi.org/10.48670/moi-00213 https://doi.org/10.48670/moi-00213 618 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-physics-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MEDSEA_MULTIYEAR_PHY_006_004 Mediterranean Sea Physics Reanalysis Short description: The Med MFC physical multiyear product is generated by a numerical system composed of an hydrodynamic model, supplied by the Nucleous for European Modelling of the Ocean (NEMO) and a variational data assimilation scheme (OceanVAR) for temperature and salinity vertical profiles and satellite Sea Level Anomaly along track data. It contains a reanalysis dataset and an interim dataset which covers the period after the reanalysis until 1 month before present. The model horizontal grid resolution is 1/24˚ (ca. 4-5 km) and the unevenly spaced vertical levels are 141. Product Citation: Please refer to our Technical FAQ for citing products DOI (Product):https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1 https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1 DOI (Interim dataset):https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1I https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1I 619 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-wave-hindcast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=ARCTIC_MULTIYEAR_WAV_002_013 Arctic Ocean Wave Hindcast Short description: The Arctic Ocean Wave Hindcast system uses the WAM model at 3 km resolution forced with surface winds and boundary wave spectra from the ECMWF (European Centre for Medium-Range Weather Forecasts) ERA5 reanalysis together with ice from the ARC MFC reanalysis (Sea Ice concentration and thickness). Additionally, in the North Atlantic area, surface winds are used from a 2.5km atmospheric hindcast system. From the output variables the most commonly used are significant wave height, peak period and mean direction. DOI (product) :https://doi.org/10.48670/moi-00008 https://doi.org/10.48670/moi-00008 620 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-colour-plankton-reflectance-transparency-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=OCEANCOLOUR_ARC_BGC_L3_NRT_009_121 Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations Short description: For the Arctic Ocean Satellite Observations, Italian National Research Council (CNR – Rome, Italy) is providing Bio-Geo_Chemical (BGC) products. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the ""multi"" products, and S3A & S3B only for the ""olci"" products. * Variables: Chlorophyll-a (CHL), Suspended Matter (SPM), Diffuse Attenuation (KD490), Detrital and Dissolved Material Absorption Coef. (ADG443''), Phytoplankton Absorption Coef. (APH443''), Total Absorption Coef. (ATOT443'') and Reflectance (RRS). * Temporal resolutions: daily, monthly. * Spatial resolutions: 1 km (multi) or 300 meters (olci). * Recent products are organized in datasets called Near Real Time (NRT) and long time-series (from 1997) in datasets called Multi-Years (MY). DOI (product) :https://doi.org/10.48670/moi-00290 https://doi.org/10.48670/moi-00290 621 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/fractional-snow-cover-raster-20m-2016-present-europe https://cryo.land.copernicus.eu/finder/ Fractional Snow Cover (raster 20m) 2016-present, Europe, daily, Jul. 2020 The Copernicus Fractional Snow Cover (FSC) product is generated in near real-time for the entire EEA38 and the United Kingdom, based on optical satellite data from the Sentinel-2 constellation. The product provides the fraction of the surface covered by snow at the top of canopy (FSC-TOC) and on ground (FSC-OG) per pixel as a percentage (0% – 100%) with a spatial resolution of 20 m x 20 m. FSC is one of the products of the pan-European High-Resolution Snow & Ice service (HR-S&I), which are provided at high spatial resolution (20 m x 20 m and 60 m x 60 m), from the Sentinel-2 and Sentinel-1 constellations data from September 1, 2016 onwards. Visit https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… to get more information on the different HR-S&I products (Snow products : FSC, WDS, SWS, GFSC, and PSA. Ice products : RLIE and ARLIE) The FSC product is distributed in raster files covering an area of 110 km by 110 km with a pixel size of 20 m by 20 m in UTM/WGS84 projection, which corresponds to the Sentinel-2 input L1C product tile. Each product is composed of seven separate files corresponding to the different layers of the product, and another metadata file. https://land.copernicus.eu/pan-european/biophysical-parameters/high-res… 622 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/pan-european-high-resolution-image-mosaic-2006-false https://land.copernicus.eu/imagery-in-situ/european-image-mosaics/high-resolution/image-2006/coverage-2 Pan-European High Resolution Image Mosaic 2006 - False Colour, Coverage 2 (20 m), June 2015 The pan-European High Resolution (HR) Image Mosaic 2006 provides HR2 (High Resolution: 20 meter) coverage over Europe. The surface covered by the image dataset is 5.8 million square kilometres and has a spatial resolution of 20 meters. The imagery is composed during specific acquisition windows between 2005 and 2007. Images are derived from the following satellite sensors: Resourcesat-1 SPOT-4/-5 The mosaic primarily is used as input data in the production of various Copernicus Land Monitoring Service (CLMS) datasets and services, such as land cover maps and high resolution layers on land cover characteristic and can be also useful for CLMS users for visualizations and classifications on land. The input imagery for the creation of the mosaic is provided by ESA. Due to license restrictions, HR Image Mosaic 2006 is only available as a web service (WMS), and not for data download. 623 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/pan-european-high-resolution-image-mosaic-2006-false-0 https://land.copernicus.eu/imagery-in-situ/european-image-mosaics/high-resolution/image-2006/image-2006-cov1 Pan-European High Resolution Image Mosaic 2006 - False Colour, Coverage 1 (20 m), May 2015 The pan-European High Resolution (HR) Image Mosaic 2006 provides HR2 (High Resolution: 20 meter) coverage over Europe. The surface covered by the image dataset is 5.8 million square kilometres and has a spatial resolution of 20 meters. The imagery is composed during specific acquisition windows between 2005 and 2007. Images are derived from the following satellite sensors: Resourcesat-1 SPOT-4/-5 The mosaic primarily is used as input data in the production of various Copernicus Land Monitoring Service (CLMS) datasets and services, such as land cover maps and high resolution layers on land cover characteristic and can be also useful for CLMS users for visualizations and classifications on land. The input imagery for the creation of the mosaic is provided by ESA. Due to license restrictions, HR Image Mosaic 2006 is only available as a web service (WMS), and not for data download. 624 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/pan-european-high-resolution-image-mosaic-2009-false https://land.copernicus.eu/imagery-in-situ/european-image-mosaics/high-resolution/image-2009/image-2009-cov1 Pan-European High Resolution Image Mosaic 2009 - False Colour, Coverage 1 (20 m), May 2015 The pan-European High Resolution (HR) Image Mosaic 2009 provides HR2 (High Resolution: 20 meter) coverage over Europe. The surface covered by the image dataset is 5.8 million square kilometres and has a spatial resolution of 20 meters. The imagery is composed during specific acquisition windows between 2008 and 2010. Images are derived from the following satellite sensors: Resourcesat-1 SPOT-4/-5 The mosaic primarily is used as input data in the production of various Copernicus Land Monitoring Service (CLMS) datasets and services, such as land cover maps and high resolution layers on land cover characteristic and can be also useful for CLMS users for visualizations and classifications on land. The input imagery for the creation of the mosaic is provided by ESA. Due to license restrictions, HR Image Mosaic 2009 is only available as a web service (WMS), and not for data download. 625 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-mean-sea-level-time-series-and-trend http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_SL_area_averaged_anomalies Global Ocean Mean Sea Level time series and trend from Observations Reprocessing DEFINITION The ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2021 version) altimeter gridded maps of sea level anomalies based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and are also available in the Copernicus Marine Service catalogue (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The mean sea level evolution estimated in the global ocean (hereafter GMSL) is derived from the average of the gridded sea level maps weighted by the cosine of the latitude. The annual and semi-annual periodic signals are removed (least scare fit of sinusoidal function) and the time series is low-pass filtered (175 days cut-off). The time series is corrected for the effect of the Glacial Isostatic Adjustment using the ICE5G-VM2 GIA model (Peltier, 2004). During 1993-1998, the GMSL has been known to be affected by a TOPEX-A instrumental drift (WCRP Global Sea Level Budget Group, 2018; Legeais et al., 2020). This drift led to overestimate the trend of the GMSL during the first 6 years of the altimetry record. Accounting for this correction changes the shape of the time series, which is no more linear but quadratic, indicating mean sea level acceleration during the altimetry era. The trend uncertainty is provided in a 90% confidence interval (Prandi et al., 2021). This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not taken into account. CONTEXT The indicator on area averaged sea level is a crucial index of climate change, and individual components contribute to sea level rise, including expansion due to ocean warming and melting of glaciers and ice sheets (WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report, global mean sea level (GMSL) increased by 0.20 (0.15 to 0.25) m over the period 1901 to 2018 with a rate 25 of rise that has accelerated since the 1960s to 3.7 (3.2 to 4.2) mm yr-1 for the period 2006–2018. Human activity was very likely the main driver of observed GMSL rise since 1970 (IPCC WGII, 2021). The weight of the different contributions evolves with time and in the recent decades the mass change has increased, contributing to the on-going acceleration of the GMSL trend (IPCC, 2022a; Legeais et al., 2020; Horwath et al., 2022). Rising sea level can strongly affect population and infrastructures in coastal areas, increase their vulnerability and risks for food security, particularly in low lying areas and island states. Adverse impacts from floods, storms and tropical cyclones with related losses and damages have increased due to sea level rise, and increase their vulnerability, and increase risks for food security, particularly in low lying areas and island states (IPCC, 2022b). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022c). CMEMS KEY FINDINGS Over the [1993/01/01, 2021/08/02] period, global mean sea level rises at a rate of 3.3  0.4 mm/year. This trend estimation is based on the altimeter measurements corrected from the Topex-A drift at the beginning of the time series (Legeais et al., 2020) and global GIA (Peltier, 2004). The observed global trend agrees with other recent estimates (Oppenheimer et al., 2019; IPCC WGI, 2021). DOI (product):https://doi.org/10.48670/moi-00237 https://doi.org/10.48670/moi-00237 626 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/efas-seasonal-reforecast https://cds.climate.copernicus.eu/cdsapp#!/dataset/efas-seasonal-reforecast efas-seasonal-reforecast This dataset provides modelled daily hydrological time series forced with seasonal meteorological reforecasts. The dataset is a consistent representation of the most important hydrological variables across the European Flood Awareness (EFAS) domain. The temporal resolution is daily forecasts initialised once a month over the reforecast period 1991-2020 of: River discharge Soil moisture for three soil layers Snow water equivalent River discharge Soil moisture for three soil layers Snow water equivalent Also provided are auxiliary (time invariant) data to aid interpretation of river discharge and soil moisture data. These auxiliary data are the upstream area, elevation, soil depth, wilting capacity and field capacity. The latter three are provided at three soil levels, one for each of the three soil layers represented in LISFLOOD. This dataset was produced by forcing the open-source LISFLOOD hydrological model at a 5x5km gridded resolution with seasonal meteorological ensemble reforecasts. Reforecasts are forecasts run over past dates and are typically used to assess the skill of a forecast system or to develop tools for statistical error correction of the forecasts. The reforecasts are initialised on the first of each month with a lead time of 215 days at 24-hour time steps. The forcing meteorological data are seasonal reforecasts from the European Centre of Medium-range Weather Forecasts (ECMWF), consisting of 25 ensemble members up until December 2016, and after that 51 members. Hydrometeorological reforecasts are available from 1991-01-01 up until 2020-10-01. Companion datasets, also available through the Climate Data Store (CDS), are seasonal forecasts, for which the seasonal reforecasts can be useful for local skill assessment and post-processing of the seasonal forecasts. For users looking for shorter time ranges there are medium-range forecasts and reforecasts, as well as historical simulations which can be used to derive the hydrological climatology. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS). All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS), which is managed, technically implemented and developed by the European Commission’s Joint Research Centre. DATA DESCRIPTION Data type Gridded - The geographical projection is the INSPIRE compliant ETRS89 Lambert Azimuthal Equal Area Coordinate Reference System (ETRS-LAEA). Horizontal coverage Europe - The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east as far as to the Caspian Sea. Horizontal resolution 5x5km Vertical resolution 3 levels for soil moisture; surface level for river discharge, snow depth water equivalent. Temporal coverage 1 January 1991 to 1 October 2020. Temporal resolution Forecasts are initialized the first of each month at 00 UTC and run with a 24-hour time step with a lead time of 215 days. File format GRIB2 and NetCDF-4 Versions Current version - EFAS v4.0 released 2020-10-14. For more information on versions we refer to the documentation. Update frequency The reforecasts will be updated when there is a new version of the seasonal forecast or the EFAS system DATA DESCRIPTION DATA DESCRIPTION Data type Gridded - The geographical projection is the INSPIRE compliant ETRS89 Lambert Azimuthal Equal Area Coordinate Reference System (ETRS-LAEA). Data type Gridded - The geographical projection is the INSPIRE compliant ETRS89 Lambert Azimuthal Equal Area Coordinate Reference System (ETRS-LAEA). Horizontal coverage Europe - The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east as far as to the Caspian Sea. Horizontal coverage Europe - The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east as far as to the Caspian Sea. Horizontal resolution 5x5km Horizontal resolution 5x5km Vertical resolution 3 levels for soil moisture; surface level for river discharge, snow depth water equivalent. Vertical resolution 3 levels for soil moisture; surface level for river discharge, snow depth water equivalent. Temporal coverage 1 January 1991 to 1 October 2020. Temporal coverage 1 January 1991 to 1 October 2020. Temporal resolution Forecasts are initialized the first of each month at 00 UTC and run with a 24-hour time step with a lead time of 215 days. Temporal resolution Forecasts are initialized the first of each month at 00 UTC and run with a 24-hour time step with a lead time of 215 days. File format GRIB2 and NetCDF-4 File format GRIB2 and NetCDF-4 Versions Current version - EFAS v4.0 released 2020-10-14. For more information on versions we refer to the documentation. Versions Current version - EFAS v4.0 released 2020-10-14. For more information on versions we refer to the documentation. Update frequency The reforecasts will be updated when there is a new version of the seasonal forecast or the EFAS system Update frequency The reforecasts will be updated when there is a new version of the seasonal forecast or the EFAS system MAIN VARIABLES Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 24-hour time step. Snow depth water equivalent kg m-2 The value represents the mass of water per square meter if all the snow in the grid box would be melted. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter of soil valid for the cell grid at the corresponding soil layer. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. For more documentation on the calculation of the volumetric soil moisture we refer to the documentation. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 24-hour time step. River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 24-hour time step. Snow depth water equivalent kg m-2 The value represents the mass of water per square meter if all the snow in the grid box would be melted. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Snow depth water equivalent kg m-2 The value represents the mass of water per square meter if all the snow in the grid box would be melted. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter of soil valid for the cell grid at the corresponding soil layer. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. For more documentation on the calculation of the volumetric soil moisture we refer to the documentation. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter of soil valid for the cell grid at the corresponding soil layer. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. For more documentation on the calculation of the volumetric soil moisture we refer to the documentation. RELATED VARIABLES Name Units Description Elevation m The mean height elevation above sea level for each pixel in the EFAS domain. Field capacity mm The amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. Soil depth m Soil depth, positive downward for each of the three soil layers at each grid point. The value is relative from the top of the land surface to the bottom of each layer respectively. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Wilting point mm The minimal amount of water in the soil that the plant requires not to wilt. If the soil water content decreases to this or any lower point a plant wilts and can no longer recover its turgidity when placed in a saturated atmosphere for 12 hours. RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Elevation m The mean height elevation above sea level for each pixel in the EFAS domain. Elevation m The mean height elevation above sea level for each pixel in the EFAS domain. Field capacity mm The amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. Field capacity mm The amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. Soil depth m Soil depth, positive downward for each of the three soil layers at each grid point. The value is relative from the top of the land surface to the bottom of each layer respectively. Soil depth m Soil depth, positive downward for each of the three soil layers at each grid point. The value is relative from the top of the land surface to the bottom of each layer respectively. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Wilting point mm The minimal amount of water in the soil that the plant requires not to wilt. If the soil water content decreases to this or any lower point a plant wilts and can no longer recover its turgidity when placed in a saturated atmosphere for 12 hours. Wilting point mm The minimal amount of water in the soil that the plant requires not to wilt. If the soil water content decreases to this or any lower point a plant wilts and can no longer recover its turgidity when placed in a saturated atmosphere for 12 hours. 627 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-major-baltic-inflow-bottom-salinity-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm Baltic Sea Major Baltic Inflow: bottom salinity from Reanalysis DEFINITION Major Baltic Inflows bring large volumes of saline and oxygen-rich water into the bottom layers of the deep basins of the Baltic Sea- Bornholm basin, Gdansk basin and Gotland basin. The Major Baltic Inflows occur seldom, sometimes many years apart (Mohrholz, 2018). The Major Baltic Inflow OMI consists of the time series of the bottom layer salinity in the Arkona basin and in the Bornholm basin and the time-depth plot of temperature, salinity and dissolved oxygen concentration in the Gotland basin (CMEMS OMI Baltic Sea Major Baltic Inflow: time/depth evolution S, T, O2). Bottom salinity increase in the Arkona basin is the first indication of the saline water inflow, but not necessarily Major Baltic Inflow. Abrupt increase of bottom salinity of 2-3 units in the more downstream Bornholm basin is a solid indicator that Major Baltic Inflow has occurred. CONTEXT The Baltic Sea is a huge brackish water basin in Northern Europe whose salinity is controlled by its freshwater budget and by the water exchange with the North Sea (e.g. Neumann et al., 2017). The saline and oxygenated water inflows to the Baltic Sea through the Danish straits, especially the Major Baltic Inflows, occur only intermittently (e.g. Mohrholz, 2018). Long-lasting periods of oxygen depletion in the deep layers of the central Baltic Sea accompanied by a salinity decline and the overall weakening of vertical stratification are referred to as stagnation periods. Extensive stagnation periods occurred in the 1920s/1930s, in the 1950s/1960s and in the 1980s/beginning of 1990s (BACCII Author Team, 2015). Bottom salinity variations in the Arkona Basin represent water exchange between the Baltic Sea and Skagerrak-Kattegat area. The increasing salinity signal in that area does not indicate that a Major Baltic Inflow has occurred. The mean sea level of the Baltic Sea derived from satellite altimetry data can be used as a proxy for the detection of saline water inflows to the Baltic Sea from the North Sea (Raudsepp et al., 2018). The medium and strong inflow events increase oxygen concentration in the near-bottom layer of the Bornholm Basin while some medium size inflows have no impact on deep water salinity (Mohrholz, 2018). CMEMS KEY FINDINGS Time series of the bottom salinity variations in the Arkona basin allow a monitoring of the sporadic nature of the water inflow/outflow events. Bottom salinity in the Arkona basin varies in the range of 11 to 25 g/kg. The maximum bottom salinity value corresponds to the Major Baltic Inflow in 2014. The other peak salinity values correspond to the Major Baltic Inflows in 1993 and 2002. Episodes of low salinity in the Arkona Basin indicate the time instances of barotropic outflows of brackish water from the Baltic Sea. Since 2015, the peak salinities of the inflow events have decreased from 25 to 14 g/kg. The bottom salinity signal in the Bornholm basin shows three Major Baltic Inflow events, i.e. the first in 1993, then in 2002 and the last one in 2014. The bottom salinity of the Bornholm basin increased to 20 g/kg as a consequence of the last Major Baltic Inflow. Since then, the salinity in the Bornholm basin has decreased from 20 to the level of 16 g/kg in seven years. After the MBI in 2002, the salinity dropped to 16 g/kg much faster, i.e., in three years. DOI (product):https://doi.org/10.48670/moi-00209 https://doi.org/10.48670/moi-00209 628 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/grassland-2018-raster-10-m-europe-3-yearly-aug-2020 https://land.copernicus.eu/pan-european/high-resolution-layers/grassland/status-maps/grassland-2018 Grassland 2018 (raster 10 m), Europe, 3-yearly, Aug. 2020 The High Resolution Layer (HRL) Grassland 2018 raster product provides a basic land cover classification with 2 thematic classes (grassland / non-grassland) at 10m spatial resolution, covering the EEA38 area and the United Kingdom. The production of the High Resolution grassland layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. This is the main High Resolution grassland product. This grassy and non-woody vegetation baseline product includes all kinds of grasslands: managed grassland, semi-natural grassland and natural grassy vegetation. It is a binary status layer for the 2015 reference year mapping grassland and all non-grassland areas in 20m and (aggregated) 100m pixel size and, for the 2018 reference year, in 10m and (aggregated) 100m pixel size. The dataset for this product is provided as 10 meter rasters in 100 x 100 km tiles (fully conformant with EEA reference grid) grouped according to the EEA38 countries and the United Kingdom. 629 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-soil-erosion-what-if-analysis https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-soil-erosion-what-if-analysis app-soil-erosion-what-if-analysis The application provides a tool for end-users to explore how land use - related to vegetation cover, management and soil erosion protection practices - can impact soil loss under different climate scenarios. Rainfall-induced soil erosion is important as it is a cross-sectoral issue affecting many rural areas in Italy and causes significant physical damage to land and high economic costs. The soil loss indicator is obtained by elaborating, in combination with land-based datasets, rainfall time series from the ERA5-Land climate reanalysis (for the 30-year reference period 1981-2010) and an ensemble of bias-corrected EURO-CORDEX simulations for near future (2021-2050) and far future (2051-2080) 30-year climatological periods under alternative Representative Concentration Pathways for greenhouse gases (RCPs 2.6, 4.5 and 8.5). The main application window allows users to select from a drop-down menu a combination of time periods and/or greenhouse gas concentration forcing to display. An interactive map displays the spatial variability of the soil loss for the desired scenario. Additionally, the user can navigate and zoom over the interactive map and the NUTS level is automatically set during zooming. When the user clicks on a highlighted Italian NUTS a window will open on the right side of the livemap with data of a focused view on that NUTS. The focused view contains a drop-down menu with the selection of the land use to be analyzed/changed: land cover and management for arable lands or support practices. The spatial minimum, maximum and average within the selected NUTS is displayed for the original factor of the selected indicator. The user can fill the text input widget with a new value for the spatial average of the selected factor, as a numeric value in the range between 0 and 1. Thus, the application calculates the new values of the soil loss that correspond to the new spatial average of the selected factor and visualizes both original and recalculated Soil loss in terms of maps and interactive boxplots. List of user-selectable parameters List of user-selectable parameters Factor: Land cover and management for arable lands Support practices Land cover and management for arable lands Support practices Scenario: Reference Period 1981-2010 Near Future 2021-2050, ensemble mean under all RCPs Near Future 2021-2050, ensemble mean under RCP 2.6 Near Future 2021-2050, ensemble mean under RCP 4.5 Near Future 2021-2050, ensemble mean under RCP 8.5 Far Future 2051-2080, ensemble mean under all RCPs Far Future 2051-2080, ensemble mean under RCP 2.6 Far Future 2051-2080, ensemble mean under RCP 4.5 Far Future 2051-2080, ensemble mean under RCP 8.5 Reference Period 1981-2010 Near Future 2021-2050, ensemble mean under all RCPs Near Future 2021-2050, ensemble mean under RCP 2.6 Near Future 2021-2050, ensemble mean under RCP 4.5 Near Future 2021-2050, ensemble mean under RCP 8.5 Far Future 2051-2080, ensemble mean under all RCPs Far Future 2051-2080, ensemble mean under RCP 2.6 Far Future 2051-2080, ensemble mean under RCP 4.5 Far Future 2051-2080, ensemble mean under RCP 8.5 INPUT VARIABLES Name Units Description Source Soil loss t ha-1 yr-1 Potential soil loss by rainfall-related rill and inter-rill erosion, obtained by Revised Universal Soil Loss Equation (RUSLE) approach. Soil erosion INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Soil loss t ha-1 yr-1 Potential soil loss by rainfall-related rill and inter-rill erosion, obtained by Revised Universal Soil Loss Equation (RUSLE) approach. Soil erosion Soil loss t ha-1 yr-1 Potential soil loss by rainfall-related rill and inter-rill erosion, obtained by Revised Universal Soil Loss Equation (RUSLE) approach. Soil erosion Soil erosion OUTPUT VARIABLES Name Units Description Recalculated soil loss t ha-1 yr-1 30-year average of annual soil loss amount recalculated after changing user-selected factors on 'land cover and management for arable lands' or 'support practices'. OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Recalculated soil loss t ha-1 yr-1 30-year average of annual soil loss amount recalculated after changing user-selected factors on 'land cover and management for arable lands' or 'support practices'. Recalculated soil loss t ha-1 yr-1 30-year average of annual soil loss amount recalculated after changing user-selected factors on 'land cover and management for arable lands' or 'support practices'. 630 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-mean-dynamic-topography http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_MDT_008_063 GLOBAL OCEAN MEAN DYNAMIC TOPOGRAPHY Short description: Mean Dynamic Topography that combines the global CNES-CLS18 MDT, the Black Sea CMEMS2020 MDT and the Med Sea CMEMS2020 MDT. It is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid. This is consistent with the reference time period also used in the DUACS products DOI (product) :https://doi.org/10.48670/moi-00150 https://doi.org/10.48670/moi-00150 631 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/efas-historical https://cds.climate.copernicus.eu/cdsapp#!/dataset/efas-historical efas-historical This dataset provides gridded modelled sub-daily and daily hydrological time series forced with meteorological observations. The data set is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is up to 30 years modelled time series of: River discharge Soil moisture for three soil layers Snow water equivalent River discharge Soil moisture for three soil layers Snow water equivalent Also provided are auxiliary (time invariant) data to aid interpretation of river discharge and soil moisture data. These auxiliary data are the upstream area, elevation, soil depth, wilting capacity and field capacity. The latter three are provided at three soil levels, one for each of the three soil layers represented in LISFLOOD. This dataset was produced by forcing the open-source LISFLOOD hydrological model with gridded observational data of precipitation and temperature at a 1x1 arcminute resolution (~1.5 km at EFAS latitudes) across the EFAS domain. Previous versions of the data have a 5x5km resolution. For the latest version data is available from 1992-01-01 up until near-real time, with a delay of 6 days. The real-time data is only available to EFAS partners. Companion datasets, also available through the CDS, are forecasts for users who are looking medium-range forecasts, reforecasts for research, local skill assessment and post-processing, and seasonal forecasts and reforecasts for users looking for long-term forecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS), which is managed, technically implemented and developed by the European Commission’s Joint Research Centre. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid for version 5.0, and ETRS89 Lambert Azimuthal Equal Area (ETRS-LAEA) for version 4.0 and older. Horizontal coverage Europe - The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east as far as to the Caspian Sea. Horizontal resolution 1x1 arcminute for version 5.0, 5x5km for older versions Vertical resolution 3 levels for soil moisture; surface level for river discharge and snow depth water equivalent. Temporal coverage 1 January 1992 to near real time (6 day delay) for version 5.0, various dates for previous versions Temporal resolution 6-hourly from version 4, 24-hourly for previous versions. File format GRIB2 and NetCDF-4 Versions Operational version - EFAS version 5.0 released 2023-09-20. For older versions we refer to the documentation. Update frequency New data are added continuously with a minimum of 1 month lag with respect to the actual date for the latest version. Older versions will be discontinued when a new version is released. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid for version 5.0, and ETRS89 Lambert Azimuthal Equal Area (ETRS-LAEA) for version 4.0 and older. Projection Regular latitude-longitude grid for version 5.0, and ETRS89 Lambert Azimuthal Equal Area (ETRS-LAEA) for version 4.0 and older. Horizontal coverage Europe - The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east as far as to the Caspian Sea. Horizontal coverage Europe - The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east as far as to the Caspian Sea. Horizontal resolution 1x1 arcminute for version 5.0, 5x5km for older versions Horizontal resolution 1x1 arcminute for version 5.0, 5x5km for older versions Vertical resolution 3 levels for soil moisture; surface level for river discharge and snow depth water equivalent. Vertical resolution 3 levels for soil moisture; surface level for river discharge and snow depth water equivalent. Temporal coverage 1 January 1992 to near real time (6 day delay) for version 5.0, various dates for previous versions Temporal coverage 1 January 1992 to near real time (6 day delay) for version 5.0, various dates for previous versions Temporal resolution 6-hourly from version 4, 24-hourly for previous versions. Temporal resolution 6-hourly from version 4, 24-hourly for previous versions. File format GRIB2 and NetCDF-4 File format GRIB2 and NetCDF-4 Versions Operational version - EFAS version 5.0 released 2023-09-20. For older versions we refer to the documentation. Versions Operational version - EFAS version 5.0 released 2023-09-20. For older versions we refer to the documentation. Update frequency New data are added continuously with a minimum of 1 month lag with respect to the actual date for the latest version. Older versions will be discontinued when a new version is released. Update frequency New data are added continuously with a minimum of 1 month lag with respect to the actual date for the latest version. Older versions will be discontinued when a new version is released. MAIN VARIABLES Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 24-hour time step. River discharge in the last 6 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 6-hour time step. Snow depth water equivalent kg m-2 The value represent the mass of water per square meter if all the snow in the grid box would be melted. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter of soil valid for the cell grid at the corresponding soil layer. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. For more documentation on the calculation of the volumetric soil moisture we refer to the documentation. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 24-hour time step. River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 24-hour time step. River discharge in the last 6 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 6-hour time step. River discharge in the last 6 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 6-hour time step. Snow depth water equivalent kg m-2 The value represent the mass of water per square meter if all the snow in the grid box would be melted. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Snow depth water equivalent kg m-2 The value represent the mass of water per square meter if all the snow in the grid box would be melted. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter of soil valid for the cell grid at the corresponding soil layer. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. For more documentation on the calculation of the volumetric soil moisture we refer to the documentation. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter of soil valid for the cell grid at the corresponding soil layer. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. For more documentation on the calculation of the volumetric soil moisture we refer to the documentation. RELATED VARIABLES Name Units Description Elevation m The mean height elevation above sea level for each pixel in the EFAS domain. Field capacity mm The amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. Soil depth m Soil depth, positive downward for each of the three soil layers at each grid point. The value is relative from the top of the land surface to the bottom of each layer respectively. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Wilting point mm The minimal amount of water in the soil that the plant requires not to wilt. If the soil water content decreases to this or any lower point a plant wilts and can no longer recover its turgidity when placed in a saturated atmosphere for 12 hours. RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Elevation m The mean height elevation above sea level for each pixel in the EFAS domain. Elevation m The mean height elevation above sea level for each pixel in the EFAS domain. Field capacity mm The amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. Field capacity mm The amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. Soil depth m Soil depth, positive downward for each of the three soil layers at each grid point. The value is relative from the top of the land surface to the bottom of each layer respectively. Soil depth m Soil depth, positive downward for each of the three soil layers at each grid point. The value is relative from the top of the land surface to the bottom of each layer respectively. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Wilting point mm The minimal amount of water in the soil that the plant requires not to wilt. If the soil water content decreases to this or any lower point a plant wilts and can no longer recover its turgidity when placed in a saturated atmosphere for 12 hours. Wilting point mm The minimal amount of water in the soil that the plant requires not to wilt. If the soil water content decreases to this or any lower point a plant wilts and can no longer recover its turgidity when placed in a saturated atmosphere for 12 hours. 632 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-observed-ocean-physics-3d-quasi-geostrophic http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MULTIOBS_GLO_PHY_W_3D_REP_015_007 Global Observed Ocean Physics 3D Quasi-Geostrophic Currents (OMEGA3D) Short description: You can find here the OMEGA3D observation-based quasi-geostrophic vertical and horizontal ocean currents developed by the Consiglio Nazionale delle RIcerche. The data are provided weekly over a regular grid at 1/4° horizontal resolution, from the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_REP_015_002 which corresponds to former version of MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012) and ERA-Interim surface fluxes. DOI (product) :https://commons.datacite.org/doi.org/10.25423/cmcc/multiobs_glo_phy_w_r… https://commons.datacite.org/doi.org/10.25423/cmcc/multiobs_glo_phy_w_r… Product citation: Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… 633 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/soil-water-index-time-series-2007-present-discrete-global http://land.copernicus.eu/global/access Soil Water Index Time Series 2007-present (discrete global grid), global, daily - version 3 The SWI_TS product is a reformatting of the SWI daily product into a time series format which makes it easier to perform local, time series based analysis without having to download and convert data for the whole globe. 634 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sss-smos-l4-oi-lops-v2021 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 SSS SMOS L4 OI - LOPS-v2021 Short description: The product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called “SMOS-OI”. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses (Copernicus Marine Service products INSITU_GLO_PHY_TS_OA_NRT_013_002 and INSITU_GLO_PHY_TS_OA_MY_013_052) to reduce large scale and temporal variable bias and Soil Moisture Ocean Salinity (SMOS) satellite image with satellite SST information. DOI (product) :https://doi.org/10.1175/JTECH-D-20-0093.1 https://doi.org/10.1175/JTECH-D-20-0093.1 635 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/forest-type-2018-raster-100-m-europe-3-yearly-oct-2020 https://land.copernicus.eu/pan-european/high-resolution-layers/forests/forest-type-1/status-maps/forest-type-2018?tab=download Forest Type 2018 (raster 100 m), Europe, 3-yearly, Oct. 2020 The High Resolution Layer (HRL) Forest Type (FTY) 2018 at 100 m spatial resolution is an aggregated version of the FTY layer at 10 m spatial reoslution, fully aligned with the EEA reference grid, and provides a forest classification with 3 thematic classes (all non-forest areas / broadleaved forest / coniferous forest). The dataset is provided as a full mosaic covering EEA38 countries and the United Kingdom. The production of the High Resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. The high resolution forest product consists of three types of (status) products and additional change products. The status products are available for the 2012, 2015 and 2018 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes). 636 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-health-mortality https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-health-mortality sis-health-mortality No data or documentation in this Catalogue entry. 637 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-delayed-mode-biogeochemical-product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_GLO_BGC_DISCRETE_MY_013_046 Global Ocean - Delayed Mode Biogeochemical product Short description: For the Global Ocean- In-situ observation delivered in delayed mode. This In Situ delayed mode product integrates the best available version of in situ oxygen, chlorophyll / fluorescence and nutrients data. DOI (product) :https://doi.org/10.17882/86207 https://doi.org/10.17882/86207 638 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/high-resolution-vegetation-phenology-and-productivity-1 https://www.wekeo.eu/data?view=viewer&t=1566840390697&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1JTkRJQ0VTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVklfRkFQQVJfMTBNIiwiekluZGV4Ijo2MH0seyJpZCI6ImMxIiwibGF5ZXJJZCI6IkVPOkhSVlBQOkRBVDpWRUdFVEFUSU9OLUlORElDRVMvX19ERUZBVUxUX18vQ0xNU19IUlZQUF9WSV9RRkxBRzJfMTBNIiwiekluZGV4Ijo4MCwiaXNIaWRkZW4iOnRydWV9XQ%3D%3D&initial=1 High Resolution Vegetation Phenology and Productivity: Fraction of Absorbed Photosynthetically Active Radiation (raster 10m) version 1 revision 1, Sep. 2021 This metadata refers to the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset, one of the near real-time (NRT) Vegetation Index products of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP), component of the Copernicus Land Monitoring Service (CLMS). The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) quantifies the fraction of the solar radiation absorbed by live leaves for the photosynthesis activity. Then, it refers only to the green and alive elements of the canopy. The FAPAR depends on the canopy structure, vegetation element optical properties, and illumination conditions. The FAPAR dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for the period from October 2016 until today, with daily updates. Each file has an associated quality indicator (QFLAG2) to assist users with the screening of clouds, shadows from clouds and topography, snow and water surfaces. 639 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/viewer-satellite-carbon-dioxide https://cds.climate.copernicus.eu/cdsapp#!/dataset/viewer-satellite-carbon-dioxide viewer-satellite-carbon-dioxide Viewer application for dataset User-selectable parameters - year: 2003 to 2019 - month: 01 to 12 User-selectable parameters More details about the products are given in the Documentation section. MAIN VARIABLES Name Units Description Column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) ppm Average molar mixing ratio (or mole fraction in micro mole carbon dioxide (CO2) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CO2 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCO2 indicates that the reported quantity is a "mole fraction" Mid-tropospheric columns of atmospheric carbon dioxide (CO2) ppm Average CO2 mixing ratio of the mid-troposphere. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) ppm Average molar mixing ratio (or mole fraction in micro mole carbon dioxide (CO2) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CO2 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCO2 indicates that the reported quantity is a "mole fraction" Column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) ppm Average molar mixing ratio (or mole fraction in micro mole carbon dioxide (CO2) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CO2 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCO2 indicates that the reported quantity is a "mole fraction" Average molar mixing ratio (or mole fraction in micro mole carbon dioxide (CO2) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CO2 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCO2 indicates that the reported quantity is a "mole fraction" Mid-tropospheric columns of atmospheric carbon dioxide (CO2) ppm Average CO2 mixing ratio of the mid-troposphere. Mid-tropospheric columns of atmospheric carbon dioxide (CO2) ppm Average CO2 mixing ratio of the mid-troposphere. RELATED VARIABLES The optimal estimation inversion algorithms used to compute the column average CO2 are based on a number of atmospheric variables like pressure, temperature, water vapour, scattering by aerosols and clouds, spectral albedo, including initial a-priori values and averaging kernels as well as estimates of uncertainty on the values of XCO2. Depending on the sensor and algorithm, a number of these variables are also included in the files along the main variable XCO2. RELATED VARIABLES RELATED VARIABLES The optimal estimation inversion algorithms used to compute the column average CO2 are based on a number of atmospheric variables like pressure, temperature, water vapour, scattering by aerosols and clouds, spectral albedo, including initial a-priori values and averaging kernels as well as estimates of uncertainty on the values of XCO2. Depending on the sensor and algorithm, a number of these variables are also included in the files along the main variable XCO2. The optimal estimation inversion algorithms used to compute the column average CO2 are based on a number of atmospheric variables like pressure, temperature, water vapour, scattering by aerosols and clouds, spectral albedo, including initial a-priori values and averaging kernels as well as estimates of uncertainty on the values of XCO2. Depending on the sensor and algorithm, a number of these variables are also included in the files along the main variable XCO2. 640 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/viewer-satellite-ice-sheet-elevation-change https://cds.climate.copernicus.eu/cdsapp#!/dataset/viewer-satellite-ice-sheet-elevation-change viewer-satellite-ice-sheet-elevation-change Surface elevation change is a component of ice sheet response to changes in climate. It is related to the mass of ice lost or gained, and hence affects global sea level. This application allows the user to visualise the rates of surface elevation change spread across the Antarctic and Greenland ice sheets and shelves over any time period since 1992. Where the elevation change rate is negative, generally ice is being lost, and this is indicated by shades of red. Gain is shown in blue. Trends in change rate can be established by comparing different time periods. INPUT VARIABLES Name Units Description Source Rate of elevation change m year-1 Greenland: Surface elevation change for each pixel in the grid over a period of 5 years for ERS1 & 2 and EnviSat and 3 years for CryoSat-2 and Sentinel-3, centered on the 'time' timestamp. Radar altimeter measurements are corrected for instrument effects, dry and wet tropospheric effect, ionospheric delay and surface slope. Measurements of height change are derived from a combination of repeat-track, along-track, crossover and plane-fitting algorithms. The height change rate is derived from weighted averaging of height change maps. Antarctica: Surface elevation change for each pixel in the grid over a period of 5 years centered on the 'time' timestamp. Radar altimeter measurements over all surfaces are corrected for instrument effects, dry and wet troposheric effect, ionospheric delay, solid earth tide, geocentric pole tide, ocean loading tide and surface slope. Over ice shelves they are also corrected for ocean tide and the inverse barometer effect. Measurements of height change are derived from the crossover method, and altimeters from different missions cross-calibrated using a multiple regression method. The height change rate is derived from least-squares fitting. No filling is applied to surface or time gaps. Satellite ice sheets elevation change Rate of elevation change uncertainty m year-1 Fitting uncertainty of the rate of elevation change. Greenland: The total uncertainty on the surface elevation change is the sum of two components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The measurement component is the combination of the uncertainties on the original altimetry measurements that went into the surface elevation change calculation, including geolocation, radar penetration, volume scattering, radar speckle and atmospheric attenuation. Antarctica: The total uncertainty on the surface elevation change is the sum of three components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The cross-calibration component is the standard deviation of any mission-pair cross-calibrations used in the 5 year period. The epoch component is the RMS of the uncertainties on the original altimetry measurements, as for Greenland. Satellite ice sheets elevation change INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Rate of elevation change m year-1 Greenland: Surface elevation change for each pixel in the grid over a period of 5 years for ERS1 & 2 and EnviSat and 3 years for CryoSat-2 and Sentinel-3, centered on the 'time' timestamp. Radar altimeter measurements are corrected for instrument effects, dry and wet tropospheric effect, ionospheric delay and surface slope. Measurements of height change are derived from a combination of repeat-track, along-track, crossover and plane-fitting algorithms. The height change rate is derived from weighted averaging of height change maps. Antarctica: Surface elevation change for each pixel in the grid over a period of 5 years centered on the 'time' timestamp. Radar altimeter measurements over all surfaces are corrected for instrument effects, dry and wet troposheric effect, ionospheric delay, solid earth tide, geocentric pole tide, ocean loading tide and surface slope. Over ice shelves they are also corrected for ocean tide and the inverse barometer effect. Measurements of height change are derived from the crossover method, and altimeters from different missions cross-calibrated using a multiple regression method. The height change rate is derived from least-squares fitting. No filling is applied to surface or time gaps. Satellite ice sheets elevation change Rate of elevation change m year-1 Greenland: Surface elevation change for each pixel in the grid over a period of 5 years for ERS1 & 2 and EnviSat and 3 years for CryoSat-2 and Sentinel-3, centered on the 'time' timestamp. Radar altimeter measurements are corrected for instrument effects, dry and wet tropospheric effect, ionospheric delay and surface slope. Measurements of height change are derived from a combination of repeat-track, along-track, crossover and plane-fitting algorithms. The height change rate is derived from weighted averaging of height change maps. Antarctica: Surface elevation change for each pixel in the grid over a period of 5 years centered on the 'time' timestamp. Radar altimeter measurements over all surfaces are corrected for instrument effects, dry and wet troposheric effect, ionospheric delay, solid earth tide, geocentric pole tide, ocean loading tide and surface slope. Over ice shelves they are also corrected for ocean tide and the inverse barometer effect. Measurements of height change are derived from the crossover method, and altimeters from different missions cross-calibrated using a multiple regression method. The height change rate is derived from least-squares fitting. No filling is applied to surface or time gaps. Greenland: Surface elevation change for each pixel in the grid over a period of 5 years for ERS1 & 2 and EnviSat and 3 years for CryoSat-2 and Sentinel-3, centered on the 'time' timestamp. Radar altimeter measurements are corrected for instrument effects, dry and wet tropospheric effect, ionospheric delay and surface slope. Measurements of height change are derived from a combination of repeat-track, along-track, crossover and plane-fitting algorithms. The height change rate is derived from weighted averaging of height change maps. Greenland Antarctica: Surface elevation change for each pixel in the grid over a period of 5 years centered on the 'time' timestamp. Radar altimeter measurements over all surfaces are corrected for instrument effects, dry and wet troposheric effect, ionospheric delay, solid earth tide, geocentric pole tide, ocean loading tide and surface slope. Over ice shelves they are also corrected for ocean tide and the inverse barometer effect. Measurements of height change are derived from the crossover method, and altimeters from different missions cross-calibrated using a multiple regression method. The height change rate is derived from least-squares fitting. No filling is applied to surface or time gaps. Antarctica Satellite ice sheets elevation change Satellite ice sheets elevation change Rate of elevation change uncertainty m year-1 Fitting uncertainty of the rate of elevation change. Greenland: The total uncertainty on the surface elevation change is the sum of two components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The measurement component is the combination of the uncertainties on the original altimetry measurements that went into the surface elevation change calculation, including geolocation, radar penetration, volume scattering, radar speckle and atmospheric attenuation. Antarctica: The total uncertainty on the surface elevation change is the sum of three components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The cross-calibration component is the standard deviation of any mission-pair cross-calibrations used in the 5 year period. The epoch component is the RMS of the uncertainties on the original altimetry measurements, as for Greenland. Satellite ice sheets elevation change Rate of elevation change uncertainty m year-1 Fitting uncertainty of the rate of elevation change. Greenland: The total uncertainty on the surface elevation change is the sum of two components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The measurement component is the combination of the uncertainties on the original altimetry measurements that went into the surface elevation change calculation, including geolocation, radar penetration, volume scattering, radar speckle and atmospheric attenuation. Antarctica: The total uncertainty on the surface elevation change is the sum of three components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The cross-calibration component is the standard deviation of any mission-pair cross-calibrations used in the 5 year period. The epoch component is the RMS of the uncertainties on the original altimetry measurements, as for Greenland. Fitting uncertainty of the rate of elevation change. Greenland: The total uncertainty on the surface elevation change is the sum of two components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The measurement component is the combination of the uncertainties on the original altimetry measurements that went into the surface elevation change calculation, including geolocation, radar penetration, volume scattering, radar speckle and atmospheric attenuation. Greenland Antarctica: The total uncertainty on the surface elevation change is the sum of three components in quadrature. The modelling component is the standard deviation of the surface elevation change linear least-squares fit. The cross-calibration component is the standard deviation of any mission-pair cross-calibrations used in the 5 year period. The epoch component is the RMS of the uncertainties on the original altimetry measurements, as for Greenland. Antarctica Satellite ice sheets elevation change Satellite ice sheets elevation change 641 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/multi-observation-global-ocean-sea-surface-salinity-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013 Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density Short description: This product consits of global gap-free Level-4 (L4) analyses of the Sea Surface Salinity (SSS) and Sea Surface Density (SSD) obtained through a multivariate optimal interpolation algorithm that combines sea surface salinity images from multiple satellite sources as NASA’s Soil Moisture Active Passive (SMAP) and ESA’s Soil Moisture Ocean Salinity (SMOS) satellites with in situ salinity measurements and satellite SST information. The product was developed by the Consiglio Nazionale delle Ricerche (CNR) and includes 4 datasets: * dataset-sss-ssd-nrt-weekly, which delivers near-real-time (NRT) weekly data * dataset-sss-ssd-nrt-monthly, which delivers near-real-time (NRT) monthly data * dataset-sss-ssd-rep-weekly, which delivers multi-year reprocessed (REP) weekly data * dataset-sss-ssd-rep-monthly, which delivers multi-year reprocessed (REP) monthly data Product citation: Please refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag…. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (product) :https://doi.org/10.48670/moi-00051 https://doi.org/10.48670/moi-00051 642 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-ozone-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-ozone-v1 satellite-ozone-v1 This dataset provides monthly averaged ozone values derived from a large set of satellite sensors with global coverage. Ozone is naturally formed in the atmosphere through the interaction between molecular oxygen and solar radiation. In the stratosphere, it acts as a shield protecting us from the UV radiation emitted by the Sun. At ground level however, it is a human health irritant and a component of smog. Ozone and climate change are strongly related. The ozone abundance in the atmosphere can be measured from space using a number of different remote-sensing techniques relying on ozone absorption at UV, visible, infrared and millimeter wavelengths. Instruments use the solar light or the thermal radiation emitted by the Earth to derive the vertical distribution of ozone in nadir, limb and solar occultation observation geometries. In the measured signal, molecular absorption features characteristic of ozone are detected using appropriate retrieval algorithms and are used to quantify its abundance. Data are available as Level-3 and/or Level-4 data products. Level-3 data correspond to measurements provided on a regular latitude/longitude after re-gridding of level-2 data (satellite orbit tracks) and averaging in time (over a month). Level-4 products are generated from Level-2 data after assimilation in a three-dimensional chemistry-transport model. We distinguish between Climate Data Record (CDR) and intermediate-CDR (ICDR). For ozone datasets distributed here, both ICDR and CDR products are generated using common software and algorithms. The CDRs cover past time periods and include the sensors that are no longer in operation. They are meant to have sufficient length, consistency, and continuity to detect climate variability and change. The ICDRs are generated by satellite instruments still in operation, and are therefore regularly updated. Recent ICDR data are provided with a short-delay. They are expected to be consistent with the CDR baseline but have not undergone any extensive checking. Users are invited to read the documentation in order to determine the time coverage of each product. Zonal averages and combined products obtained by merging data from different satellites are also provided. Nadir column data products from individual sensors are updated on a quarterly basis. All other products are updated once or twice a year. This dataset is produced on behalf of C3S based on algorithm developments performed as part of the European Space Agency (ESA) Ozone Climate Change Initiative (CCI) project. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 1° x 1° for nadir products 10° latitude zones for limb products (zonal averages) 10° x 20° for the gridded limb merged product Vertical resolution Profiles and total column data depending on the product Temporal coverage 1970 to present, but shorter for some sensors Temporal resolution Monthly File format NetCDF-4 (respecting GHRSST 2.0 data specifications) Versions The different versions of ozone products indicate one or more of the following: A change or improvement in the algorithm has taken place. In this case, the use of the latest version is recommended. A product has been extended and this extension affects the whole dataset. This is the case for several of the merged sensor products. Here, different versions can coexist. A bug in the production process has been fixed. Note that the meaning of multiple versions of an ozone product is well documented in the product user guide and algorithm theoretical basis document. Update frequency The update frequency of ICDR's depends on the sensor and type of product: annually, semi-annually and quarterly DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 1° x 1° for nadir products 10° latitude zones for limb products (zonal averages) 10° x 20° for the gridded limb merged product Horizontal resolution 1° x 1° for nadir products 10° latitude zones for limb products (zonal averages) 10° x 20° for the gridded limb merged product 1° x 1° for nadir products 10° latitude zones for limb products (zonal averages) 10° x 20° for the gridded limb merged product Vertical resolution Profiles and total column data depending on the product Vertical resolution Profiles and total column data depending on the product Temporal coverage 1970 to present, but shorter for some sensors Temporal coverage 1970 to present, but shorter for some sensors Temporal resolution Monthly Temporal resolution Monthly File format NetCDF-4 (respecting GHRSST 2.0 data specifications) File format NetCDF-4 (respecting GHRSST 2.0 data specifications) Versions The different versions of ozone products indicate one or more of the following: A change or improvement in the algorithm has taken place. In this case, the use of the latest version is recommended. A product has been extended and this extension affects the whole dataset. This is the case for several of the merged sensor products. Here, different versions can coexist. A bug in the production process has been fixed. Note that the meaning of multiple versions of an ozone product is well documented in the product user guide and algorithm theoretical basis document. Versions The different versions of ozone products indicate one or more of the following: A change or improvement in the algorithm has taken place. In this case, the use of the latest version is recommended. A product has been extended and this extension affects the whole dataset. This is the case for several of the merged sensor products. Here, different versions can coexist. A bug in the production process has been fixed. Note that the meaning of multiple versions of an ozone product is well documented in the product user guide and algorithm theoretical basis document. The different versions of ozone products indicate one or more of the following: A change or improvement in the algorithm has taken place. In this case, the use of the latest version is recommended. A product has been extended and this extension affects the whole dataset. This is the case for several of the merged sensor products. Here, different versions can coexist. A bug in the production process has been fixed. A change or improvement in the algorithm has taken place. In this case, the use of the latest version is recommended. A change or improvement in the algorithm has taken place. In this case, the use of the latest version is recommended. A product has been extended and this extension affects the whole dataset. This is the case for several of the merged sensor products. Here, different versions can coexist. A product has been extended and this extension affects the whole dataset. This is the case for several of the merged sensor products. Here, different versions can coexist. A bug in the production process has been fixed. A bug in the production process has been fixed. Note that the meaning of multiple versions of an ozone product is well documented in the product user guide and algorithm theoretical basis document. Update frequency The update frequency of ICDR's depends on the sensor and type of product: annually, semi-annually and quarterly Update frequency The update frequency of ICDR's depends on the sensor and type of product: annually, semi-annually and quarterly MAIN VARIABLES Name Units Description Anomaly of mole concentration of ozone in air % Percent deviation with respect to reference monthly concentration value. If C(t) is the monthly ozone concentration at month t, and M(t) is the reference ozone concentration for the same month (derived by averaging all measurements for month t), then the percent anomaly is given by 100*(C(t)-M(t))/M(t). For more details, see the Algorithm Theoretical Background Document (ATBD) Atmosphere mole content of ozone Level 3: mol m-2 Level 4: Dobson units Vertical integration from the surface to the top of the atmosphere of the number of moles of ozone above a unit area Mole concentration of ozone in air mol m-3 Number of moles of ozone divided by the total volume of the air-ozone mixture Mole content of ozone in atmosphere layer mol m-2 Vertical integration between two specified levels in the atmosphere of the number of moles of ozone above a unit area Mole fraction of ozone in air m3 m-3 Fraction of the ozone volume to the total volume of the air-ozone mixture MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Anomaly of mole concentration of ozone in air % Percent deviation with respect to reference monthly concentration value. If C(t) is the monthly ozone concentration at month t, and M(t) is the reference ozone concentration for the same month (derived by averaging all measurements for month t), then the percent anomaly is given by 100*(C(t)-M(t))/M(t). For more details, see the Algorithm Theoretical Background Document (ATBD) Anomaly of mole concentration of ozone in air % Percent deviation with respect to reference monthly concentration value. If C(t) is the monthly ozone concentration at month t, and M(t) is the reference ozone concentration for the same month (derived by averaging all measurements for month t), then the percent anomaly is given by 100*(C(t)-M(t))/M(t). For more details, see the Algorithm Theoretical Background Document (ATBD) Atmosphere mole content of ozone Level 3: mol m-2 Level 4: Dobson units Vertical integration from the surface to the top of the atmosphere of the number of moles of ozone above a unit area Atmosphere mole content of ozone Level 3: mol m-2 Level 4: Dobson units Level 3: mol m-2 Level 4: Dobson units Vertical integration from the surface to the top of the atmosphere of the number of moles of ozone above a unit area Mole concentration of ozone in air mol m-3 Number of moles of ozone divided by the total volume of the air-ozone mixture Mole concentration of ozone in air mol m-3 Number of moles of ozone divided by the total volume of the air-ozone mixture Mole content of ozone in atmosphere layer mol m-2 Vertical integration between two specified levels in the atmosphere of the number of moles of ozone above a unit area Mole content of ozone in atmosphere layer mol m-2 Vertical integration between two specified levels in the atmosphere of the number of moles of ozone above a unit area Mole fraction of ozone in air m3 m-3 Fraction of the ozone volume to the total volume of the air-ozone mixture Mole fraction of ozone in air m3 m-3 Fraction of the ozone volume to the total volume of the air-ozone mixture 643 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/dominant-leaf-type-change-2015-2018-raster-20-m-europe-3 https://land.copernicus.eu/pan-european/high-resolution-layers/forests/dominant-leaf-type/change-maps/dominant-leaf-type-change-2015-2018?tab=download Dominant Leaf Type Change 2015-2018 (raster 20 m), Europe, 3-yearly, Dec. 2020 This metadata refers to the Copernicus High Resolution Layer Forest product Dominant Leaf Type Change (DLTC) 2015-2018. The DLTC raster product provides information on the change between the reference years 2015 and 2018 and consists of 7 thematic classes (unchanged areas with no tree cover / new broadleaved cover / new coniferous cover / loss of broadleaved cover / loss of coniferous cover / unchanged areas with tree cover / potential change among dominant leaf types) at 20m spatial resolution and covers the full of EEA38 area and the United Kingdom. The production of the High Resolution Forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. The High Resolution Forest product consists of three types of (status) products and additional change products. The status products are available for the 2012, 2015 and 2018 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. 644 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/efas-forecast https://cds.climate.copernicus.eu/cdsapp#!/dataset/efas-forecast efas-forecast This dataset provides gridded modelled hydrological time series forced with medium-range meteorological forecasts. The data is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is sub-daily high-resolution and ensemble forecasts of: River discharge Soil moisture for three soil layers Snow water equivalent River discharge Soil moisture for three soil layers Snow water equivalent Also provided are auxiliary (time invariant) data to aid interpretation of river discharge and soil moisture data. These auxiliary data are the upstream area, elevation, soil depth, wilting capacity and field capacity. The latter three are provided at three soil levels, one for each of the three soil layers represented in LISFLOOD. This data set was produced by forcing the open-source LISFLOOD hydrological model at a 1x1 arcminute resolution (~1.5 km at EFAS latitudes) with meteorological forecasts. For version 4.0 and older, the open-source LISFLOOD hydrological model was forced at a 5x5km resolution. The forecasts are initialised twice daily at 00 and 12 UTC with time steps of 6 or 24 hours (the latter for version 3.5 and older) and lead times between 5 and 15 days depending on the forcing numerical weather prediction model. The forcing meteorological data are high-resolution and ensemble forecasts from the European Centre of Medium-range Weather Forecasts (ECMWF) with 51 ensemble members, high-resolution forecasts from the Deutsches Wetter Dienst (DWD) and the ensemble forecasts from the COSMO Local Ensemble Prediction System (COSMO-LEPS) with 20 ensemble members. The hydrological forecasts are available from 2018-10-10 up until present with a 30-day delay. The real-time data is only available to EFAS partners. Companion datasets, also available through the CDS, are historical simulations which can be used to derive the hydrological climatology and for verification; reforecasts for research, local skill assessment and post-processing; and seasonal forecasts and reforecasts for users looking for longer leadtime forecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS), which is managed, technically implemented and developed by the European Commission’s Joint Research Centre. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid for version 5.0, and ETRS89 Lambert Azimuthal Equal Area (ETRS-LAEA) for version 4.0 and older. Horizontal coverage Europe - The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east as far as to the Caspian Sea. Horizontal resolution 1x1 arcminute for version 5.0, 5x5km for version 4.0 and older Vertical resolution 3 levels for soil moisture; surface level for river discharge and snow depth water equivalent. Temporal coverage 10 October 2018 to near real-time with a 30-day delay. Temporal resolution Forecasts are initialized daily at 00 and 12 UTC with a 6 or 24-hourly time step (the latter for version 3.5 and older) and lead times up to 15 days. File format GRIB2 and NetCDF-4 Versions Operational forecasts use the latest version of the EFAS system, hence the version will depend on the forecast initiation date. The version used to produce each individual forecast is included in the metadata, and the date in which it is valid for can be seen in Citation on the right hand side. For a full description of the versions we refer to the wiki pages in the Documentation. Update frequency The EFAS forecasts are published on CDS at regular intervals with a minimum of 1 month lag with respect to the actual date. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid for version 5.0, and ETRS89 Lambert Azimuthal Equal Area (ETRS-LAEA) for version 4.0 and older. Projection Regular latitude-longitude grid for version 5.0, and ETRS89 Lambert Azimuthal Equal Area (ETRS-LAEA) for version 4.0 and older. Horizontal coverage Europe - The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east as far as to the Caspian Sea. Horizontal coverage Europe - The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east as far as to the Caspian Sea. Horizontal resolution 1x1 arcminute for version 5.0, 5x5km for version 4.0 and older Horizontal resolution 1x1 arcminute for version 5.0, 5x5km for version 4.0 and older Vertical resolution 3 levels for soil moisture; surface level for river discharge and snow depth water equivalent. Vertical resolution 3 levels for soil moisture; surface level for river discharge and snow depth water equivalent. Temporal coverage 10 October 2018 to near real-time with a 30-day delay. Temporal coverage 10 October 2018 to near real-time with a 30-day delay. Temporal resolution Forecasts are initialized daily at 00 and 12 UTC with a 6 or 24-hourly time step (the latter for version 3.5 and older) and lead times up to 15 days. Temporal resolution Forecasts are initialized daily at 00 and 12 UTC with a 6 or 24-hourly time step (the latter for version 3.5 and older) and lead times up to 15 days. File format GRIB2 and NetCDF-4 File format GRIB2 and NetCDF-4 Versions Operational forecasts use the latest version of the EFAS system, hence the version will depend on the forecast initiation date. The version used to produce each individual forecast is included in the metadata, and the date in which it is valid for can be seen in Citation on the right hand side. For a full description of the versions we refer to the wiki pages in the Documentation. Versions Operational forecasts use the latest version of the EFAS system, hence the version will depend on the forecast initiation date. The version used to produce each individual forecast is included in the metadata, and the date in which it is valid for can be seen in Citation on the right hand side. For a full description of the versions we refer to the wiki pages in the Documentation. Update frequency The EFAS forecasts are published on CDS at regular intervals with a minimum of 1 month lag with respect to the actual date. Update frequency The EFAS forecasts are published on CDS at regular intervals with a minimum of 1 month lag with respect to the actual date. MAIN VARIABLES Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 24-hour time step. River discharge in the last 6 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 6-hour time step. Snow depth water equivalent kg m-2 The value represent the mass of water per square meter if all the snow in the grid box would be melted. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter of soil valid for the cell grid at the corresponding soil layer. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. For more documentation on the calculation of the volumetric soil moisture we refer to the documentation. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 24-hour time step. River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 24-hour time step. River discharge in the last 6 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 6-hour time step. River discharge in the last 6 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 6-hour time step. Snow depth water equivalent kg m-2 The value represent the mass of water per square meter if all the snow in the grid box would be melted. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Snow depth water equivalent kg m-2 The value represent the mass of water per square meter if all the snow in the grid box would be melted. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter of soil valid for the cell grid at the corresponding soil layer. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. For more documentation on the calculation of the volumetric soil moisture we refer to the documentation. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter of soil valid for the cell grid at the corresponding soil layer. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. For more documentation on the calculation of the volumetric soil moisture we refer to the documentation. RELATED VARIABLES Name Units Description Elevation m The mean height elevation above sea level for each pixel in the EFAS domain. Field capacity mm The amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. Soil depth m Soil depth, positive downward for each of the three soil layers at each grid point. The value is relative from the top of the land surface to the bottom of each layer respectively. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Wilting point mm The minimal amount of water in the soil that the plant requires not to wilt. If the soil water content decreases to this or any lower point a plant wilts and can no longer recover its turgidity when placed in a saturated atmosphere for 12 hours. RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Elevation m The mean height elevation above sea level for each pixel in the EFAS domain. Elevation m The mean height elevation above sea level for each pixel in the EFAS domain. Field capacity mm The amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. Field capacity mm The amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. Soil depth m Soil depth, positive downward for each of the three soil layers at each grid point. The value is relative from the top of the land surface to the bottom of each layer respectively. Soil depth m Soil depth, positive downward for each of the three soil layers at each grid point. The value is relative from the top of the land surface to the bottom of each layer respectively. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Wilting point mm The minimal amount of water in the soil that the plant requires not to wilt. If the soil water content decreases to this or any lower point a plant wilts and can no longer recover its turgidity when placed in a saturated atmosphere for 12 hours. Wilting point mm The minimal amount of water in the soil that the plant requires not to wilt. If the soil water content decreases to this or any lower point a plant wilts and can no longer recover its turgidity when placed in a saturated atmosphere for 12 hours. 645 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-hourly-sea-surface-wind-and-stress http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=WIND_GLO_PHY_L4_NRT_012_004 Global Ocean Hourly Sea Surface Wind and Stress from Scatterometer and Model Short description: For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 degrees horizontal spatial resolution. Scatterometer observations for Metop-B and Metop-C ASCAT and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) operational model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF operational model fields. The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product. DOI (product) :https://doi.org/10.48670/moi-00305 https://doi.org/10.48670/moi-00305 646 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-mosquito-map https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-mosquito-map hidden-app-mosquito-map Viewer application for dataset More details about the products are given in the Documentation section. MAIN VARIABLES Name Units Description Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. 647 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-mosquito-map-detailed https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-mosquito-map-detailed hidden-app-mosquito-map-detailed Viewer application for dataset More details about the products are given in the Documentation section. MAIN VARIABLES Name Units Description Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. 648 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-climatic-suitability-tiger-mosquito https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-climatic-suitability-of-tiger-mosquito hidden-app-climatic-suitability-of-tiger-mosquito Viewer application for dataset More details about the products are given in the Documentation section. MAIN VARIABLES Name Units Description Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. 649 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-climatic-suitability-tiger-mosquito-detailed https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-climatic-suitability-of-tiger-mosquito-detailed hidden-app-climatic-suitability-of-tiger-mosquito-detailed Viewer application for dataset More details about the products are given in the Documentation section. MAIN VARIABLES Name Units Description Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. 650 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-mean-tmax-july https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-mean-tmax-in-july hidden-app-mean-tmax-in-july Viewer application for dataset More details about the products are given in the Documentation section. MAIN VARIABLES Name Units Description Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. 651 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/viewer-sis-marine-properties https://cds.climate.copernicus.eu/cdsapp#!/dataset/viewer-sis-marine-properties viewer-sis-marine-properties Viewer application for dataset More details about the products are given in the Documentation section. MAIN VARIABLES Name Units Description Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Apparent oxygen utilisation mol m-3 Apparent Oxygen Utilization (AOU) is the difference between the dissolved oxygen concentration and its equilibrium saturation concentration in water with the same physical and chemical properties. Such differences typically occur when biological activity acts to change the ambient concentration of oxygen. For example, molecular oxygen is produced during photosynthesis, which increases its concentration, while oxygen is consumed during respiration which decreases its concentration. The AOU represents the sum of the biological activity that a sample has experienced since it was last in equilibrium with the atmosphere. It is a 4D field (time, location, depth) which has the same units as dissolved oxygen. In shallow waters, the full water column is generally in close contact with the atmosphere and AOU values are low, since oxygen is close to its saturation concentration. However, in deeper waters that are relatively isolated from the atmosphere, large AOU values are possible. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone chlorophyll-a kg m-3 This is a 4D field (time, location, depth), from which the surface chlorophyll-a field is calculated by taking the average concentration over one optical depth. Here, optical depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications, for example. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Euphotic zone depth m Euphotic zone depth is taken to be the depth at which downwelling irradiance has been reduced to 1% of that at the surface – a level that is often used to define the base of the euphotic zone. Note this measure of optical depth is different to the e-folding length scale which is commonly used in satellite applications. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of dissolved oxygen mol m-3 The presence of oxygen is an essential pre-requisite for all forms of complex marine life, including commercially exploited species of fish and shell fish. Its concentration is influenced by multiple processes, including air-sea gas exchange, production through oxygenic photosynthesis and consumption via respiration. Surface oxygen concentrations are strongly influenced by air-sea gas exchange, and oxygen is generally in plentiful supply in surface ocean waters. However, in deeper waters oxygen may become depleted due to the activity of bacteria and other organisms that consume oxygen during respiration; if the rate of consumption exceeds the rate of supply, then oxygen levels will fall. Oxygen concentrations above 190 mmol m-3 are considered to be sufficient to support healthy marine communities with minimal problems, while oxygen concentrations below 62.5 mmol m-3 are considered to be a source of serious concern. The concentration of dissolved molecular oxygen is a 4D field (time, location, depth), and is calculated by the coupled model. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of nitrate and nitrite mol m-3 Both nitrate (NO3-) and nitrite (NO2-) are taken up from sea water by marine phytoplankton, which incorporate the nitrogen into new biomass as they grow. Nitrogen is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. In the model, nitrate and nitrite are represented by a single state variable. The concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of nitrate and nitrite: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of nitrate and nitrite, include inputs from rivers, atmospheric deposition, and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of phosphate mol m-3 The mole concentration of phosphate in sea water. Phosphate (PO43-) is taken up from sea water by marine phytoplankton which incorporate the phosphorous into new biomass as they grow. Phosphorous is an essential element for phytoplankton, and the amount available strongly influences their potential for growth. The phosphate concentration field is 4D (time, location, depth). Across much of the model domain, there are large seasonal variations in the near surface concentration of phosphate: during the spring, phytoplankton draw the concentration down as they grow; concentrations can remain low for much of the summer, before being replenished via mixing with deeper, nutrient rich waters during the winter months. Several other factors influence the concentration of phosphate, include inputs from rivers and various biological processes involved in the break down or organic material by marine bacteria. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Mole concentration of silicate mol m-3 Silicate (SiO4) is taken up from sea water by a number of marine organisms, the most important of which in the context of marine biogeochemical cycles are the diatoms. Diatoms are a major group of phytoplankton that utilize silicic acid – the primary form of silicate in sea water – to build their cell wall. Diatoms often dominate the phytoplankton community and are major contributors to marine primary production. They also play a disproportionately important role in carbon export – the process by which carbon is transferred to the deep ocean from productive near surface waters – which in turn impacts the long-term ability of the ocean to take up carbon dioxide from the atmosphere. Their potential to become limited by the concentration of silicate in sea water has motivated the explicit inclusion of silicate in many marine biogeochemical models, including ERSEM. The silicate concentration field is 4D (time, location, depth), and is calculated by the coupled model. Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Net primary production mol m-3 s-1 Net primary production is the excess of gross primary production (rate of synthesis of biomass from inorganic precursors) by autotrophs (“producers”, which is this case are photosynthetic phytoplankton), over the rate at which the autotrophs themselves respire some of this biomass. In the oceans, carbon production per unit volume is often found at a number of depths at a given horizontal location. That quantity can then be integrated to calculate production per unit area at the location. Here, it is a 4D field (time, location, depth) that is made up of contributions from the four different phytoplankton groups included in ERSEM. Net primary production is a critical measure of marine ecosystem function, since it represents the amount of carbon and energy that is available to organisms higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in environmental conditions (e.g. the availability of light and nutrients). Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Organic carbon in the water column mol m-3 The mole concentration of non-living organic carbon in sea water. The total amount of non-living organic carbon is made up of dissolved and particulate components. The dissolved component includes unstable carbon rich compounds (e.g. sugars) which may have been secreted by phytoplankton cells, and more refractory forms which can persist in the oceans for many years. The particulate forms include contributions from dead cells and zooplankton faecal pellets. Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Phytoplankton carbon mol m-3 The mole concentration of phytoplankton carbon in sea water. Phytoplankton are autotrophic prokaryotic or eukaryotic organisms that live near the water surface where there is sufficient light to support photosynthesis. The mole concentration of phytoplankton carbon corresponds to the amount of carbon contained within the molecules that make up phytoplankton cells. In ERSEM, the total amount of phytoplankton carbon is made up of contributions from the four different phytoplankton groups: diatoms, and three groups that are primarily distinguished by their size, which are the pico-, nano- and micro-phytoplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different phytoplankton taxa that can be found in the ocean. The mole concentration of phytoplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Potential energy anomaly J m-3 Stratification indices describe the extent to which vertical mixing between bodies of water with distinct physical properties is suppressed. The potential energy anomaly is a quantitative measure of stratification that represents the work required to bring about complete mixing of a column of water. It is a 3D field (time, location). The higher the potential anomaly, the more stratified the water column. A potential energy anomaly of zero is indicative of a fully mixed water column. Temperate shelf seas are often seasonally stratified, with vertical mixing suppressed from the spring through to the autumn. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Saturation state of aragonite Dimensionless Aragonite is one of the more soluble forms of calcium carbonate. It is widely used by calcifying marine organisms, including corals and certain types of phytoplankton, to build physical structures. The saturation state of aragonite is dependent on the seawater chemistry of calcium and carbonate ions. When the saturation state of seawater is greater than one, it is said to be supersaturated with respect to aragonite, making it possible for marine organisms to generate physical structures made from aragonite. When the saturation state is less than one, the seawater is said to be under-saturated with respect to aragonite, meaning the aragonite mineral tends to dissolve in seawater. When carbon dioxide is added to the sea from the atmosphere the pH tends to fall, which in turn acts to reduce the saturation state of seawater with respect to aragonite. For this reason, the saturation state of aragonite is widely used to track ocean acidification. The aragonite saturation state is a 4D field (time, location, depth) and is calculated by ERSEM. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water pH Dimensionless pH is a measure of the concentration of hydrogen ions in a solution. The more hydrogen ions that are present, the more acidic is the solution. The pH scale ranges from zero (very acidic) to 14 (very basic). A pH of 7 is neutral, a pH less than 7 is acidic, and a pH greater than 7 is basic. Today, ocean water is normally slightly basic, with a surface-water pH of about 8.1. pH is measured on a logarithmic scale, meaning a single unit corresponds to a ten-fold difference. Ocean pH is a 4D field (time, location, depth) and is calculated by ERSEM. It is a function of several other variables, including the amount of inorganic carbon in sea water; the pH of sea water falls (i.e. it becomes more acidic) as more carbon dioxide is taken up from the atmosphere by the ocean. Changes in ocean pH can directly impact many marine organisms, including certain types of phytoplankton. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water potential temperature K The temperature a parcel of sea water would have if moved adiabatically to sea level pressure. The potential temperature field is 4D (time, location, depth), and is calculated by the physical circulation model. The area covered by the model domain is characterized by large latitudinal and seasonal variations in surface temperature, with the lowest surface temperatures found in the northern reaches of the domain, where typical values are a few degrees above zero in winter. In contrast, in the southern reaches of the domain, surface temperatures can exceed 25 deg. C in the summer. Away from the surface, and especially in deeper waters off the continental shelf, temperatures are generally more stable with a typical value being a few degrees above zero. The temperature of sea water influences ocean currents and mixing. It also influences many biological processes, and species are generally adapted to a specific range of temperatures. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Sea water salinity PSU The salt content of sea water as measured on the practical salinity scale. The sea water practical salinity field is 4D (time, location, depth), and is calculated by the physical circulation model. Within the model domain, low salinity values can be found near to sources of freshwater such as river mouths. The Mediterranean Sea is characterized by relatively high salinity values, which may approach 40 PSU (practical salinity units). The salinity of sea water influences ocean currents and mixing. Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Secondary production mol m-3 s-1 Secondary production is the difference between the organic carbon consumed by zooplankton and zooplankton respiration which produces inorganic carbon. It is a 4D field (time, location, depth) and is made up of contributions from the three different zooplankton groups included in ERSEM. Secondary production is a key measure of marine ecosystem function, in part because it determines the amount of carbon and energy available to commercially exploited species of fish and shell fish higher up the food chain. The area covered by the model domain is characterized by large spatial and seasonal variations in net primary production, which reflect differences in food availability and environmental conditions (e.g. temperature, which controls key metabolic rates) Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Total chlorophyll-a kg m-3 Chlorophylls are the green pigments found in most plants, algae and cyanobacteria; their presence is essential for photosynthesis to take place. In the ocean, chlorophyll-a is commonly used as an index for phytoplankton abundance. However, the relationship is not direct, in part due to a process called photoadaptation, in which cells down-regulate the synthesis of pigments in high light environments. A model of photoadaptation is included in ERSEM, meaning the intracellular ratio of phytoplankton Chlorophyll-a to carbon can change in response to changes in simulated environmental conditions. In ERSEM, the dynamics of four different phytoplankton groups are modelled. Each group independently regulates its concentration of chlorophyll-a, which is used to calculate photosynthetic rates. The total chlorophyll-a field is the sum of the contribution from the four different groups. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. Zooplankton carbon mol m-3 Zooplankton are heterotrophic organisms that feed on both living and non-living organic matter within the water column. The mole concentration of zooplankton carbon corresponds to the amount of carbon contained within the molecules that make up their bodies. In ERSEM, the total amount of zooplankton carbon is made up of contributions from three different zooplankton groups that are primarily distinguished by their size: heterotrophic nanoflagellates, microzooplankton and mesozooplankton. The classification reflects a choice made within the model regarding how to represent the multitude of different zooplankton taxa that can be found in the ocean. The mole concentration of zooplankton carbon is a 4D field (time, location, depth), which exhibits large spatial and temporal variations across the model domain. u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value u-velocity component m s-1 Eastward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. “Eastward” indicates a vector component which is positive when directed eastward (negative westward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean value v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. v-velocity component m s-1 Northward horizontal surface velocity of a water parcel, as calculated by the physical circulation model. The horizontal surface velocity is a vector quantity, which is broken up into Northward and Eastward components. "Northward" indicates a vector component which is positive when directed northward (negative southward). Several factors can influence the horizontal velocity field, and thus drive ocean currents. Across the continental shelf, and especially in near shore environments, the effect of tides is often evident and can drive ocean currents with speeds well in-excess of 1 m s-1. Due to the period of tides, the velocity components are computed as 25 hour mean values. 652 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/forest-type-2018-raster-10-m-europe-3-yearly-oct-2020 https://land.copernicus.eu/pan-european/high-resolution-layers/forests/forest-type-1/status-maps/forest-type-2018?tab=download Forest Type 2018 (raster 10 m), Europe, 3-yearly, Oct. 2020 The High Resolution Layer (HRL) Forest 2018 status layer Forest Type (FTY) provides a forest classification with 3 thematic classes (all non-forest areas / broadleaved forest / coniferous forest) at 10m spatial resolution and with a Minimum Mapping Unit (MMU) of 0.5 ha. This raster layer is largely following the FAO (Food and Agriculture Organisation of the United Nations) forest definition with tree covered areas in agricultural and urban context excluded and covers the full of EEA38 area and the United Kingdom. The dataset is provided as 10 meter rasters in 100 x 100 km tiles grouped according to the EEA38 countries and the United Kingdom (fully confirmat with the EEA reference grid). The production of the High Resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. The High Resolution forest product consists of three types of (status) products and additional change products. The status products are available for the 2012, 2015 and 2018 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes). 653 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-tourism-mountain-indicators-projections https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-tourism-mountain-indicators-projections app-tourism-mountain-indicators-projections This application presents comparisons between past and future snow conditions relevant to the tourism industry. Based on the Mountain tourism meteorological and snow indicators (MTMSI) C3S dataset, the application enables the exploration of 39 indicators characterising meteorological conditions in the mountain regions of Europe by elevation, and on the scale of NUTS level 3 regions. The application explores tourism indicators from the recent past based on reanalysis data for the period 1961 to 2015, along with future indicators based on an ensemble of adjusted climate projections for the near future, mid-century and end of century. Multi-model statistics (including the mean and a range of percentiles) are derived from multiple global climate model (GCM) and regional climate model (RCM) pairs for three climate change scenarios: RCP2.6 (2 GCM/RCM pairs) and RCP4.5 and 8.5 (9 GCM/RCM pairs each). These statistics are critical for the assessment of snow reliability of mountain regions on a multi-annual perspective. Comparisons can be drawn between multi-model mean indicators for multiple NUTS regions, or for a range of multi-model statistics at a single NUTS region. Description of the graphical output Description of the graphical output The application displays Mountain Tourism Meteorological and Snow Indicators (MTMSI) in an interactive map, averaged over NUTS level 3 regions. Comparisons between NUTS regions or multi-model percentiles can be made by clicking on one or more regions in the map, which will generate a vertical chart showing how the chosen indicator varies with altitude. OUTPUT VARIABLES Name Units Description Annual amount of machine made snow produced kg m-2 The total amount of machine made snow for the period August 1st of year N-1 to July 31st of year N, where N is the selected year. End of the longest period with groomed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a groomed snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. End of the longest period with managed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a managed (groomed and machine made) snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. End of the longest period with natural snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a natural snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Mean winter air temperature K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in November year N-1 to April of year N (inclusive). Monthly mean air temperature for April K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in April of year N. Monthly mean air temperature for December K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in December of year N-1. Monthly mean air temperature for February K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in February of year N. Monthly mean air temperature for January K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in January of year N. Monthly mean air temperature for March K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in March of year N. Monthly mean air temperature for November K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in November of year N-1. Period with high amount of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a groomed snow simulation. Period with high amount of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a managed (groomed and machine made) snow simulation. Period with high amount of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a natural snow simulation. Period with high height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a groomed snow simulation. Period with high height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a managed (groomed and machine made) snow simulation. Period with high height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a natural snow simulation. Period with low height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a groomed snow simulation. Period with low height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a managed (groomed and machine made) snow simulation. Period with low height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a natural snow simulation. Period with medium amount of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a groomed snow simulation. Period with medium amount of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a managed (groomed and machine made) snow simulation. Period with medium amount of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a natural snow simulation. Period with medium height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a groomed snow simulation. Period with medium height of groomed snow during Christmas day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a groomed snow simulation. Maximum value is 14. Period with medium height of groomed snow during Purisima day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a groomed snow simulation. Maximum value is 7. Note, Purisima refers to the time period between December 4 to December 10. Period with medium height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Period with medium height of managed snow during Christmas day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Maximum value is 14. Period with medium height of managed snow during Purisima day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Maximum value is 7. Note, Purisima refers to the time period between December 4 to December 10. Period with medium height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a natural snow simulation. Period with medium height of natural snow during Christmas day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a natural snow simulation. Maximum value is 14. Period with medium height of natural snow during Purisima day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a natural snow simulation. Maximum value is 7. Note, Purisima refers to the time period between December 4 to December 10. Snow making hours for WBT lower than -2°C hour Computed wet bulb temperature (WBT) from temperature and relative humidity every 6 hours and interpolated linearly to an hourly time resolution. Expressed as the number of hours, from November 1st of year N-1 to December 31st of year N-1, for which wet buld temperature is less than -2°C. Snow making hours for WBT lower than -5°C hour Computed wet bulb temperature (WBT) from temperature and relative humidity every 6 hours and interpolated linearly to an hourly time resolution. Expressed as the number of hours, from November 1st of year N-1 to December 31st of year N-1, for which wet buld temperature is less than -5°C. Start of the longest period with groomed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a groomed snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Start of the longest period with managed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a managed (groomed and machine made) snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Start of the longest period with natural snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a natural snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Total precipitation from November to April kg m-2 Cumulative value of snowfall and rain precipitation over the winter sports season (November year N-1 to April year N). Total snow precipitation from November to April kg m-2 Cumulative value of snowfall precipitation over the winter sports season (November year N-1 to April year N). OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Annual amount of machine made snow produced kg m-2 The total amount of machine made snow for the period August 1st of year N-1 to July 31st of year N, where N is the selected year. Annual amount of machine made snow produced kg m-2 The total amount of machine made snow for the period August 1st of year N-1 to July 31st of year N, where N is the selected year. End of the longest period with groomed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a groomed snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. End of the longest period with groomed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a groomed snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. End of the longest period with managed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a managed (groomed and machine made) snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. End of the longest period with managed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a managed (groomed and machine made) snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. End of the longest period with natural snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a natural snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. End of the longest period with natural snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a natural snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Mean winter air temperature K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in November year N-1 to April of year N (inclusive). Mean winter air temperature K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in November year N-1 to April of year N (inclusive). Monthly mean air temperature for April K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in April of year N. Monthly mean air temperature for April K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in April of year N. Monthly mean air temperature for December K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in December of year N-1. Monthly mean air temperature for December K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in December of year N-1. Monthly mean air temperature for February K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in February of year N. Monthly mean air temperature for February K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in February of year N. Monthly mean air temperature for January K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in January of year N. Monthly mean air temperature for January K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in January of year N. Monthly mean air temperature for March K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in March of year N. Monthly mean air temperature for March K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in March of year N. Monthly mean air temperature for November K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in November of year N-1. Monthly mean air temperature for November K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in November of year N-1. Period with high amount of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a groomed snow simulation. Period with high amount of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a groomed snow simulation. Period with high amount of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a managed (groomed and machine made) snow simulation. Period with high amount of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a managed (groomed and machine made) snow simulation. Period with high amount of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a natural snow simulation. Period with high amount of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a natural snow simulation. Period with high height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a groomed snow simulation. Period with high height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a groomed snow simulation. Period with high height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a managed (groomed and machine made) snow simulation. Period with high height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a managed (groomed and machine made) snow simulation. Period with high height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a natural snow simulation. Period with high height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a natural snow simulation. Period with low height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a groomed snow simulation. Period with low height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a groomed snow simulation. Period with low height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a managed (groomed and machine made) snow simulation. Period with low height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a managed (groomed and machine made) snow simulation. Period with low height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a natural snow simulation. Period with low height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a natural snow simulation. Period with medium amount of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a groomed snow simulation. Period with medium amount of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a groomed snow simulation. Period with medium amount of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a managed (groomed and machine made) snow simulation. Period with medium amount of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a managed (groomed and machine made) snow simulation. Period with medium amount of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a natural snow simulation. Period with medium amount of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a natural snow simulation. Period with medium height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a groomed snow simulation. Period with medium height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a groomed snow simulation. Period with medium height of groomed snow during Christmas day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a groomed snow simulation. Maximum value is 14. Period with medium height of groomed snow during Christmas day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a groomed snow simulation. Maximum value is 14. Period with medium height of groomed snow during Purisima day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a groomed snow simulation. Maximum value is 7. Note, Purisima refers to the time period between December 4 to December 10. Period with medium height of groomed snow during Purisima day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a groomed snow simulation. Maximum value is 7. Note, Purisima refers to the time period between December 4 to December 10. Period with medium height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Period with medium height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Period with medium height of managed snow during Christmas day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Maximum value is 14. Period with medium height of managed snow during Christmas day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Maximum value is 14. Period with medium height of managed snow during Purisima day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Maximum value is 7. Note, Purisima refers to the time period between December 4 to December 10. Period with medium height of managed snow during Purisima day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Maximum value is 7. Note, Purisima refers to the time period between December 4 to December 10. Period with medium height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a natural snow simulation. Period with medium height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a natural snow simulation. Period with medium height of natural snow during Christmas day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a natural snow simulation. Maximum value is 14. Period with medium height of natural snow during Christmas day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a natural snow simulation. Maximum value is 14. Period with medium height of natural snow during Purisima day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a natural snow simulation. Maximum value is 7. Note, Purisima refers to the time period between December 4 to December 10. Period with medium height of natural snow during Purisima day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a natural snow simulation. Maximum value is 7. Note, Purisima refers to the time period between December 4 to December 10. Snow making hours for WBT lower than -2°C hour Computed wet bulb temperature (WBT) from temperature and relative humidity every 6 hours and interpolated linearly to an hourly time resolution. Expressed as the number of hours, from November 1st of year N-1 to December 31st of year N-1, for which wet buld temperature is less than -2°C. Snow making hours for WBT lower than -2°C hour Computed wet bulb temperature (WBT) from temperature and relative humidity every 6 hours and interpolated linearly to an hourly time resolution. Expressed as the number of hours, from November 1st of year N-1 to December 31st of year N-1, for which wet buld temperature is less than -2°C. Snow making hours for WBT lower than -5°C hour Computed wet bulb temperature (WBT) from temperature and relative humidity every 6 hours and interpolated linearly to an hourly time resolution. Expressed as the number of hours, from November 1st of year N-1 to December 31st of year N-1, for which wet buld temperature is less than -5°C. Snow making hours for WBT lower than -5°C hour Computed wet bulb temperature (WBT) from temperature and relative humidity every 6 hours and interpolated linearly to an hourly time resolution. Expressed as the number of hours, from November 1st of year N-1 to December 31st of year N-1, for which wet buld temperature is less than -5°C. Start of the longest period with groomed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a groomed snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Start of the longest period with groomed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a groomed snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Start of the longest period with managed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a managed (groomed and machine made) snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Start of the longest period with managed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a managed (groomed and machine made) snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Start of the longest period with natural snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a natural snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Start of the longest period with natural snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a natural snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Total precipitation from November to April kg m-2 Cumulative value of snowfall and rain precipitation over the winter sports season (November year N-1 to April year N). Total precipitation from November to April kg m-2 Cumulative value of snowfall and rain precipitation over the winter sports season (November year N-1 to April year N). Total snow precipitation from November to April kg m-2 Cumulative value of snowfall precipitation over the winter sports season (November year N-1 to April year N). Total snow precipitation from November to April kg m-2 Cumulative value of snowfall precipitation over the winter sports season (November year N-1 to April year N). 654 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-extreme-precipitation-statistics-europe-explorer https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-extreme-precipitation-statistics-europe-explorer app-extreme-precipitation-statistics-europe-explorer This application allows users to access, analyse and compare extreme precipitation indicators within the Extreme precipitation indicators for Europe and European cities from 1950 to 2019 dataset available in the CDS catalogue. These indicators include: (i) a sub-set of ETCCDI (Expert Team on Climate Change Detection and Indices) indicators with fixed and percentile thresholds and indicators for the length of wet spells and (ii) “hard extremes” from extreme value analysis, related to return periods up to 100-years. Extreme precipitation indicators for Europe and European cities from 1950 to 2019 Once the user selects the source dataset, the indicator, the temporal aggregation and the temporal period of interest, the interactive map (livemap) shows the average value of the indicator computed over the selected period. Users can pan and zoom around the data domain (Europe), and select one of the Nomenclature of Territorial Units for Statistics (NUTS) regions (national to level 2) to open a focussed view of that region. The focused view includes a series of map plots and an interactive box-and-whisker plot. The series of maps covers the different years or months, according to the temporal period selected. The box-and-whisker plot displays the minimum, maximum, median, the 25th and 75th percentiles and any data outliers as points for the selected region. The user can zoom on a box-and-whisker plot to highlight statistics about a specific month/year. User-selectable parameters User-selectable parameters Source dataset used for computing the indicators ERA5 E-OBS Extreme Precipitation Indicators Total precipitation amount (mm) Wet days (day) Maximum 1-day & 5-day precipitation amount (mm) Consecutive wet days (day) Number of very heavy precipitation events (day) Precipitation amount due to precipitation on the 1%, 5%, & 10% wettest days (mm) Frequency of rainy days exceeding the 90th, 95th, & 99th percentile (day) Precipitation amount for a 5, 10, 25, 50, & 100-year return period (mm) Temporal aggregation Yearly Monthly 30-years Temporal periods for the selected indicator (from 01-1950 to 12-2019). For yearly indicators, users can select a period by selecting "year from" and "year to" drop-down menus. For monthly indicators, users can select a period by selecting "month and year from" and "month and year to" drop-down menus. According to the source dataset selected, the drop-down menu will display only the years available for that dataset. Source dataset used for computing the indicators ERA5 E-OBS ERA5 E-OBS ERA5 E-OBS Extreme Precipitation Indicators Total precipitation amount (mm) Wet days (day) Maximum 1-day & 5-day precipitation amount (mm) Consecutive wet days (day) Number of very heavy precipitation events (day) Precipitation amount due to precipitation on the 1%, 5%, & 10% wettest days (mm) Frequency of rainy days exceeding the 90th, 95th, & 99th percentile (day) Precipitation amount for a 5, 10, 25, 50, & 100-year return period (mm) Total precipitation amount (mm) Wet days (day) Maximum 1-day & 5-day precipitation amount (mm) Consecutive wet days (day) Number of very heavy precipitation events (day) Precipitation amount due to precipitation on the 1%, 5%, & 10% wettest days (mm) Frequency of rainy days exceeding the 90th, 95th, & 99th percentile (day) Precipitation amount for a 5, 10, 25, 50, & 100-year return period (mm) Total precipitation amount (mm) Wet days (day) Maximum 1-day & 5-day precipitation amount (mm) Consecutive wet days (day) Number of very heavy precipitation events (day) Precipitation amount due to precipitation on the 1%, 5%, & 10% wettest days (mm) Frequency of rainy days exceeding the 90th, 95th, & 99th percentile (day) Precipitation amount for a 5, 10, 25, 50, & 100-year return period (mm) Temporal aggregation Yearly Monthly 30-years Yearly Monthly 30-years Yearly Monthly 30-years Temporal periods for the selected indicator (from 01-1950 to 12-2019). For yearly indicators, users can select a period by selecting "year from" and "year to" drop-down menus. For monthly indicators, users can select a period by selecting "month and year from" and "month and year to" drop-down menus. According to the source dataset selected, the drop-down menu will display only the years available for that dataset. INPUT VARIABLES Name Units Description Source Maximum 1-day precipitation kg m-2 The maximum amount of precipitation in one day for the user-selected month or year period. Extreme precipitation indicators for Europe Maximum 5-day precipitation kg m-2 The maximum amount of precipitation in 5 consecutive days for the user-selected month or year period. Extreme precipitation indicators for Europe Number of consecutive wet days Count The maximum number of consecutive days with the daily precipitation amount greater than 1 mm for the user-selected month or year period. Extreme precipitation indicators for Europe Number of precipitation days exceeding 20mm Count The number of days with at least 20 mm of daily precipitation for the user-selected month or year period. Extreme precipitation indicators for Europe Number of precipitation days exceeding fixed percentiles Count The number of days with daily precipitation exceeding the 90th, 95th, or 99th percentile of wet days (daily precipitation ≥ 1 mm) for the user-selected month or year. Extreme precipitation indicators for Europe Number of wet days Count The number of days with the daily precipitation amount greater than 1 mm for the user-selected month or year period. Extreme precipitation indicators for Europe Precipitation at fixed percentiles kg m-2 Total precipitation when daily precipitation amounts exceed the 90th, 95th, or 99th percentiles in wet days (daily precipitation ≥ 1 mm) computed over the 30-year period (1989-2018). Extreme precipitation indicators for Europe Precipitation at fixed return periods kg m-2 Daily precipitation amount characterised by the 5, 10, 25, 50, or 100 year return period computed over the 30-year period (1989-2018). Extreme precipitation indicators for Europe Standardised precipitation exceeding fixed percentiles Dimensionless Standardised daily precipitation amount over the grid point's 95th or 99th percentile of wet days (daily precipitation ≥ 1 mm). Values are decimal, ranging between 0-17 (95th percentile) or 0-10 (99th percentile). These values may be used to detect and rank extreme precipitation events. Extreme precipitation indicators for Europe INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Maximum 1-day precipitation kg m-2 The maximum amount of precipitation in one day for the user-selected month or year period. Extreme precipitation indicators for Europe Maximum 1-day precipitation kg m-2 The maximum amount of precipitation in one day for the user-selected month or year period. Extreme precipitation indicators for Europe Extreme precipitation indicators for Europe Maximum 5-day precipitation kg m-2 The maximum amount of precipitation in 5 consecutive days for the user-selected month or year period. Extreme precipitation indicators for Europe Maximum 5-day precipitation kg m-2 The maximum amount of precipitation in 5 consecutive days for the user-selected month or year period. Extreme precipitation indicators for Europe Extreme precipitation indicators for Europe Number of consecutive wet days Count The maximum number of consecutive days with the daily precipitation amount greater than 1 mm for the user-selected month or year period. Extreme precipitation indicators for Europe Number of consecutive wet days Count The maximum number of consecutive days with the daily precipitation amount greater than 1 mm for the user-selected month or year period. Extreme precipitation indicators for Europe Extreme precipitation indicators for Europe Number of precipitation days exceeding 20mm Count The number of days with at least 20 mm of daily precipitation for the user-selected month or year period. Extreme precipitation indicators for Europe Number of precipitation days exceeding 20mm Count The number of days with at least 20 mm of daily precipitation for the user-selected month or year period. Extreme precipitation indicators for Europe Extreme precipitation indicators for Europe Number of precipitation days exceeding fixed percentiles Count The number of days with daily precipitation exceeding the 90th, 95th, or 99th percentile of wet days (daily precipitation ≥ 1 mm) for the user-selected month or year. Extreme precipitation indicators for Europe Number of precipitation days exceeding fixed percentiles Count The number of days with daily precipitation exceeding the 90th, 95th, or 99th percentile of wet days (daily precipitation ≥ 1 mm) for the user-selected month or year. Extreme precipitation indicators for Europe Extreme precipitation indicators for Europe Number of wet days Count The number of days with the daily precipitation amount greater than 1 mm for the user-selected month or year period. Extreme precipitation indicators for Europe Number of wet days Count The number of days with the daily precipitation amount greater than 1 mm for the user-selected month or year period. Extreme precipitation indicators for Europe Extreme precipitation indicators for Europe Precipitation at fixed percentiles kg m-2 Total precipitation when daily precipitation amounts exceed the 90th, 95th, or 99th percentiles in wet days (daily precipitation ≥ 1 mm) computed over the 30-year period (1989-2018). Extreme precipitation indicators for Europe Precipitation at fixed percentiles kg m-2 Total precipitation when daily precipitation amounts exceed the 90th, 95th, or 99th percentiles in wet days (daily precipitation ≥ 1 mm) computed over the 30-year period (1989-2018). Extreme precipitation indicators for Europe Extreme precipitation indicators for Europe Precipitation at fixed return periods kg m-2 Daily precipitation amount characterised by the 5, 10, 25, 50, or 100 year return period computed over the 30-year period (1989-2018). Extreme precipitation indicators for Europe Precipitation at fixed return periods kg m-2 Daily precipitation amount characterised by the 5, 10, 25, 50, or 100 year return period computed over the 30-year period (1989-2018). Extreme precipitation indicators for Europe Extreme precipitation indicators for Europe Standardised precipitation exceeding fixed percentiles Dimensionless Standardised daily precipitation amount over the grid point's 95th or 99th percentile of wet days (daily precipitation ≥ 1 mm). Values are decimal, ranging between 0-17 (95th percentile) or 0-10 (99th percentile). These values may be used to detect and rank extreme precipitation events. Extreme precipitation indicators for Europe Standardised precipitation exceeding fixed percentiles Dimensionless Standardised daily precipitation amount over the grid point's 95th or 99th percentile of wet days (daily precipitation ≥ 1 mm). Values are decimal, ranging between 0-17 (95th percentile) or 0-10 (99th percentile). These values may be used to detect and rank extreme precipitation events. Extreme precipitation indicators for Europe Extreme precipitation indicators for Europe 655 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-delayed-mode-gridded-cora-situ-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_GLO_PHY_TS_OA_MY_013_052 Global Ocean- Delayed Mode gridded CORA- In-situ Observations objective analysis in Delayed Mode Short description:' Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the reprocessed in-situ global product CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b) using the ISAS software. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a validation source for operational models, observing seasonal cycle and inter-annual variability. DOI (product) :https://doi.org/10.17882/46219 https://doi.org/10.17882/46219 656 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-emission-inventories https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-emission-inventories cams-global-emission-inventories This data set contains gridded distributions of global anthropogenic and natural emissions. Natural and anthropogenic emissions of atmospheric pollutants and greenhouse gases are key drivers of the evolution of the composition of the atmosphere, so an accurate representation of them in forecast models of atmospheric composition is essential. CAMS compiles inventories of emission data that serve as input to its own forecast models, but which can also be used by other atmospheric chemical transport models. These inventories are based on a combination of existing data sets and new information, describing anthropogenic emissions from fossil fuel use on land, shipping, and aviation, and natural emissions from vegetation, soil, the ocean and termites. The anthropogenic emissions on land are further separated in specific activity sectors (e.g., power generation, road traffic, industry). The CAMS emission data sets provide good consistency between the emissions of greenhouse gases, reactive gases, and aerosol particles and their precursors. Because most inventory-based data sets are only available with a delay of several years, the CAMS emission inventories also extend these existing data sets forward in time by using the trends from the most recent available years, producing timely input data for real-time forecast models. Most of the data sets are updated once or twice per year adding the most recent year to the data record, while re-processing the original data record for consistency, when needed. This is reflected by the different version numbers. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution Anthropogenic: 0.1°x0.1°, biogenic/shipping: 0.25°x0.25°, aviation/oceanic/soil/termite: 0.5°x0.5° Vertical coverage Surface Temporal coverage Anthropogenic: 2000 - 2020, aviation: 2000 - 2020, biogenic/oceanic/shipping: 2000 - 2018, soil: 2000 - 2015, termite: 2000 Temporal resolution Monthly File format NetCDF Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions Aviation, soil, termite: 1.1. Anthropogenic, biogenic, oceanic, shipping: 2.1 Update frequency Once or twice a year DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution Anthropogenic: 0.1°x0.1°, biogenic/shipping: 0.25°x0.25°, aviation/oceanic/soil/termite: 0.5°x0.5° Horizontal resolution Anthropogenic: 0.1°x0.1°, biogenic/shipping: 0.25°x0.25°, aviation/oceanic/soil/termite: 0.5°x0.5° Vertical coverage Surface Vertical coverage Surface Temporal coverage Anthropogenic: 2000 - 2020, aviation: 2000 - 2020, biogenic/oceanic/shipping: 2000 - 2018, soil: 2000 - 2015, termite: 2000 Temporal coverage Anthropogenic: 2000 - 2020, aviation: 2000 - 2020, biogenic/oceanic/shipping: 2000 - 2018, soil: 2000 - 2015, termite: 2000 Temporal resolution Monthly Temporal resolution Monthly File format NetCDF File format NetCDF Conventions Climate and Forecast (CF) Metadata Convention v1.6 Conventions Climate and Forecast (CF) Metadata Convention v1.6 Versions Aviation, soil, termite: 1.1. Anthropogenic, biogenic, oceanic, shipping: 2.1 Versions Aviation, soil, termite: 1.1. Anthropogenic, biogenic, oceanic, shipping: 2.1 Update frequency Once or twice a year Update frequency Once or twice a year MAIN VARIABLES Name Units Anthropogenic emissions of CO2 (excluding short-cycle organic carbon) (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of acetylene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of acids (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of alcohols (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of ammonia (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of benzene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of black carbon (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of butanes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of carbon dioxide (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of carbon monoxide (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of chlorinated hydrocarbons (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of esters (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of ethane (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of ethene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of ethers (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of formaldehyde (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of hexanes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of isoprene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of ketones (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of methane (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of monoterpenes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of nitrogen oxides (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of non-methane VOCs (volatile organic chemicals) (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of organic carbon (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of other VOCs (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of other aldehydes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of other alkenes/alkynes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of other aromatics (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of pentanes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of propane (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of propene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of sulphur dioxide (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of toluene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of trimethylbenzenes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of xylenes (from 12 different sectors) kg m-2 s-1 Aviation emissions of acetylene kg m-2 s-1 Aviation emissions of alcohols kg m-2 s-1 Aviation emissions of ammonia kg m-2 s-1 Aviation emissions of benzene kg m-2 s-1 Aviation emissions of black carbon kg m-2 s-1 Aviation emissions of carbon dioxide kg m-2 s-1 Aviation emissions of carbon monoxide kg m-2 s-1 Aviation emissions of ethane kg m-2 s-1 Aviation emissions of ethene kg m-2 s-1 Aviation emissions of formaldehyde kg m-2 s-1 Aviation emissions of hexanes kg m-2 s-1 Aviation emissions of ketones kg m-2 s-1 Aviation emissions of nitrogen oxides kg m-2 s-1 Aviation emissions of non-methane VOCs kg m-2 s-1 Aviation emissions of organic carbon kg m-2 s-1 Aviation emissions of other VOCs kg m-2 s-1 Aviation emissions of other aldehydes kg m-2 s-1 Aviation emissions of other alkenes/alkynes kg m-2 s-1 Aviation emissions of other aromatics kg m-2 s-1 Aviation emissions of pentanes kg m-2 s-1 Aviation emissions of propane kg m-2 s-1 Aviation emissions of propene kg m-2 s-1 Aviation emissions of sulphur dioxide kg m-2 s-1 Aviation emissions of toluene kg m-2 s-1 Aviation emissions of trimethylbenzenes kg m-2 s-1 Aviation emissions of xylenes kg m-2 s-1 Biogenic emissions of acetaldehyde kg m-2 s-1 Biogenic emissions of acetic acid kg m-2 s-1 Biogenic emissions of acetone kg m-2 s-1 Biogenic emissions of butanes and higher alkanes kg m-2 s-1 Biogenic emissions of butenes and higher alkenes kg m-2 s-1 Biogenic emissions of carbon monoxide kg m-2 s-1 Biogenic emissions of ethane kg m-2 s-1 Biogenic emissions of ethanol kg m-2 s-1 Biogenic emissions of ethene kg m-2 s-1 Biogenic emissions of formaldehyde kg m-2 s-1 Biogenic emissions of formic acid kg m-2 s-1 Biogenic emissions of hydrogen cyanide kg m-2 s-1 Biogenic emissions of isoprene kg m-2 s-1 Biogenic emissions of methane kg m-2 s-1 Biogenic emissions of methanol kg m-2 s-1 Biogenic emissions of other aldehydes kg m-2 s-1 Biogenic emissions of other ketones kg m-2 s-1 Biogenic emissions of other monoterpenes kg m-2 s-1 Biogenic emissions of pinene kg m-2 s-1 Biogenic emissions of propane kg m-2 s-1 Biogenic emissions of propene kg m-2 s-1 Biogenic emissions of sesquiterpenes kg m-2 s-1 Biogenic emissions of toluene kg m-2 s-1 Oceanic emissions of bromoform kg m-2 s-1 Oceanic emissions of dibromomethane kg m-2 s-1 Oceanic emissions of dimethyl sulphide kg m-2 s-1 Oceanic emissions of iodomethane kg m-2 s-1 Shipping emissions of VOCs (all) kg m-2 s-1 Shipping emissions of ash kg m-2 s-1 Shipping emissions of carbon dioxide kg m-2 s-1 Shipping emissions of carbon monoxide kg m-2 s-1 Shipping emissions of elemental carbon kg m-2 s-1 Shipping emissions of nitrogen oxides kg m-2 s-1 Shipping emissions of organic carbon kg m-2 s-1 Shipping emissions of sulphate kg m-2 s-1 Shipping emissions of sulphur oxides kg m-2 s-1 Soil emissions of nitrogen oxides (NOx) from biome kg m-2 s-1 Soil emissions of nitrogen oxides (NOx) from fertiliser kg m-2 s-1 Soil emissions of nitrogen oxides (NOx) from nitrogen deposition kg m-2 s-1 Termite emissions of methane kg m-2 s-1 MAIN VARIABLES MAIN VARIABLES Name Units Name Units Anthropogenic emissions of CO2 (excluding short-cycle organic carbon) (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of CO2 (excluding short-cycle organic carbon) (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of acetylene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of acetylene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of acids (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of acids (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of alcohols (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of alcohols (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of ammonia (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of ammonia (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of benzene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of benzene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of black carbon (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of black carbon (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of butanes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of butanes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of carbon dioxide (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of carbon dioxide (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of carbon monoxide (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of carbon monoxide (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of chlorinated hydrocarbons (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of chlorinated hydrocarbons (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of esters (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of esters (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of ethane (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of ethane (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of ethene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of ethene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of ethers (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of ethers (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of formaldehyde (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of formaldehyde (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of hexanes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of hexanes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of isoprene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of isoprene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of ketones (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of ketones (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of methane (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of methane (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of monoterpenes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of monoterpenes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of nitrogen oxides (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of nitrogen oxides (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of non-methane VOCs (volatile organic chemicals) (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of non-methane VOCs (volatile organic chemicals) (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of organic carbon (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of organic carbon (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of other VOCs (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of other VOCs (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of other aldehydes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of other aldehydes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of other alkenes/alkynes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of other alkenes/alkynes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of other aromatics (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of other aromatics (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of pentanes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of pentanes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of propane (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of propane (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of propene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of propene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of sulphur dioxide (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of sulphur dioxide (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of toluene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of toluene (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of trimethylbenzenes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of trimethylbenzenes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of xylenes (from 12 different sectors) kg m-2 s-1 Anthropogenic emissions of xylenes (from 12 different sectors) kg m-2 s-1 Aviation emissions of acetylene kg m-2 s-1 Aviation emissions of acetylene kg m-2 s-1 Aviation emissions of alcohols kg m-2 s-1 Aviation emissions of alcohols kg m-2 s-1 Aviation emissions of ammonia kg m-2 s-1 Aviation emissions of ammonia kg m-2 s-1 Aviation emissions of benzene kg m-2 s-1 Aviation emissions of benzene kg m-2 s-1 Aviation emissions of black carbon kg m-2 s-1 Aviation emissions of black carbon kg m-2 s-1 Aviation emissions of carbon dioxide kg m-2 s-1 Aviation emissions of carbon dioxide kg m-2 s-1 Aviation emissions of carbon monoxide kg m-2 s-1 Aviation emissions of carbon monoxide kg m-2 s-1 Aviation emissions of ethane kg m-2 s-1 Aviation emissions of ethane kg m-2 s-1 Aviation emissions of ethene kg m-2 s-1 Aviation emissions of ethene kg m-2 s-1 Aviation emissions of formaldehyde kg m-2 s-1 Aviation emissions of formaldehyde kg m-2 s-1 Aviation emissions of hexanes kg m-2 s-1 Aviation emissions of hexanes kg m-2 s-1 Aviation emissions of ketones kg m-2 s-1 Aviation emissions of ketones kg m-2 s-1 Aviation emissions of nitrogen oxides kg m-2 s-1 Aviation emissions of nitrogen oxides kg m-2 s-1 Aviation emissions of non-methane VOCs kg m-2 s-1 Aviation emissions of non-methane VOCs kg m-2 s-1 Aviation emissions of organic carbon kg m-2 s-1 Aviation emissions of organic carbon kg m-2 s-1 Aviation emissions of other VOCs kg m-2 s-1 Aviation emissions of other VOCs kg m-2 s-1 Aviation emissions of other aldehydes kg m-2 s-1 Aviation emissions of other aldehydes kg m-2 s-1 Aviation emissions of other alkenes/alkynes kg m-2 s-1 Aviation emissions of other alkenes/alkynes kg m-2 s-1 Aviation emissions of other aromatics kg m-2 s-1 Aviation emissions of other aromatics kg m-2 s-1 Aviation emissions of pentanes kg m-2 s-1 Aviation emissions of pentanes kg m-2 s-1 Aviation emissions of propane kg m-2 s-1 Aviation emissions of propane kg m-2 s-1 Aviation emissions of propene kg m-2 s-1 Aviation emissions of propene kg m-2 s-1 Aviation emissions of sulphur dioxide kg m-2 s-1 Aviation emissions of sulphur dioxide kg m-2 s-1 Aviation emissions of toluene kg m-2 s-1 Aviation emissions of toluene kg m-2 s-1 Aviation emissions of trimethylbenzenes kg m-2 s-1 Aviation emissions of trimethylbenzenes kg m-2 s-1 Aviation emissions of xylenes kg m-2 s-1 Aviation emissions of xylenes kg m-2 s-1 Biogenic emissions of acetaldehyde kg m-2 s-1 Biogenic emissions of acetaldehyde kg m-2 s-1 Biogenic emissions of acetic acid kg m-2 s-1 Biogenic emissions of acetic acid kg m-2 s-1 Biogenic emissions of acetone kg m-2 s-1 Biogenic emissions of acetone kg m-2 s-1 Biogenic emissions of butanes and higher alkanes kg m-2 s-1 Biogenic emissions of butanes and higher alkanes kg m-2 s-1 Biogenic emissions of butenes and higher alkenes kg m-2 s-1 Biogenic emissions of butenes and higher alkenes kg m-2 s-1 Biogenic emissions of carbon monoxide kg m-2 s-1 Biogenic emissions of carbon monoxide kg m-2 s-1 Biogenic emissions of ethane kg m-2 s-1 Biogenic emissions of ethane kg m-2 s-1 Biogenic emissions of ethanol kg m-2 s-1 Biogenic emissions of ethanol kg m-2 s-1 Biogenic emissions of ethene kg m-2 s-1 Biogenic emissions of ethene kg m-2 s-1 Biogenic emissions of formaldehyde kg m-2 s-1 Biogenic emissions of formaldehyde kg m-2 s-1 Biogenic emissions of formic acid kg m-2 s-1 Biogenic emissions of formic acid kg m-2 s-1 Biogenic emissions of hydrogen cyanide kg m-2 s-1 Biogenic emissions of hydrogen cyanide kg m-2 s-1 Biogenic emissions of isoprene kg m-2 s-1 Biogenic emissions of isoprene kg m-2 s-1 Biogenic emissions of methane kg m-2 s-1 Biogenic emissions of methane kg m-2 s-1 Biogenic emissions of methanol kg m-2 s-1 Biogenic emissions of methanol kg m-2 s-1 Biogenic emissions of other aldehydes kg m-2 s-1 Biogenic emissions of other aldehydes kg m-2 s-1 Biogenic emissions of other ketones kg m-2 s-1 Biogenic emissions of other ketones kg m-2 s-1 Biogenic emissions of other monoterpenes kg m-2 s-1 Biogenic emissions of other monoterpenes kg m-2 s-1 Biogenic emissions of pinene kg m-2 s-1 Biogenic emissions of pinene kg m-2 s-1 Biogenic emissions of propane kg m-2 s-1 Biogenic emissions of propane kg m-2 s-1 Biogenic emissions of propene kg m-2 s-1 Biogenic emissions of propene kg m-2 s-1 Biogenic emissions of sesquiterpenes kg m-2 s-1 Biogenic emissions of sesquiterpenes kg m-2 s-1 Biogenic emissions of toluene kg m-2 s-1 Biogenic emissions of toluene kg m-2 s-1 Oceanic emissions of bromoform kg m-2 s-1 Oceanic emissions of bromoform kg m-2 s-1 Oceanic emissions of dibromomethane kg m-2 s-1 Oceanic emissions of dibromomethane kg m-2 s-1 Oceanic emissions of dimethyl sulphide kg m-2 s-1 Oceanic emissions of dimethyl sulphide kg m-2 s-1 Oceanic emissions of iodomethane kg m-2 s-1 Oceanic emissions of iodomethane kg m-2 s-1 Shipping emissions of VOCs (all) kg m-2 s-1 Shipping emissions of VOCs (all) kg m-2 s-1 Shipping emissions of ash kg m-2 s-1 Shipping emissions of ash kg m-2 s-1 Shipping emissions of carbon dioxide kg m-2 s-1 Shipping emissions of carbon dioxide kg m-2 s-1 Shipping emissions of carbon monoxide kg m-2 s-1 Shipping emissions of carbon monoxide kg m-2 s-1 Shipping emissions of elemental carbon kg m-2 s-1 Shipping emissions of elemental carbon kg m-2 s-1 Shipping emissions of nitrogen oxides kg m-2 s-1 Shipping emissions of nitrogen oxides kg m-2 s-1 Shipping emissions of organic carbon kg m-2 s-1 Shipping emissions of organic carbon kg m-2 s-1 Shipping emissions of sulphate kg m-2 s-1 Shipping emissions of sulphate kg m-2 s-1 Shipping emissions of sulphur oxides kg m-2 s-1 Shipping emissions of sulphur oxides kg m-2 s-1 Soil emissions of nitrogen oxides (NOx) from biome kg m-2 s-1 Soil emissions of nitrogen oxides (NOx) from biome kg m-2 s-1 Soil emissions of nitrogen oxides (NOx) from fertiliser kg m-2 s-1 Soil emissions of nitrogen oxides (NOx) from fertiliser kg m-2 s-1 Soil emissions of nitrogen oxides (NOx) from nitrogen deposition kg m-2 s-1 Soil emissions of nitrogen oxides (NOx) from nitrogen deposition kg m-2 s-1 Termite emissions of methane kg m-2 s-1 Termite emissions of methane kg m-2 s-1 657 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-situ-near-real-time-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_BAL_PHYBGCWAV_DISCRETE_MYNRT_013_032 Baltic Sea- In Situ Near Real Time Observations Short description: Baltic Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average DOI (product) :https://doi.org/10.48670/moi-00032 https://doi.org/10.48670/moi-00032 658 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-monthly-mean-sea-surface-wind-and-stress http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=WIND_GLO_PHY_CLIMATE_L4_MY_012_003 Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model Short description: For the Global Ocean - The product contains monthly Level-4 sea surface wind and stress fields at 0.25 degrees horizontal spatial resolution. The monthly averaged wind and stress fields are based on monthly average ECMWF ERA5 reanalysis fields, corrected for persistent biases using all available Level-3 scatterometer observations from the Metop-A, Metop-B and Metop-C ASCAT satellite instruments. The product provides monthly mean stress-equivalent wind and stress variables as well as their standard deviation. The number of observations used to calculate the monthly averages are included in the product." DOI (product) :https://doi.org/10.48670/moi-00181 https://doi.org/10.48670/moi-00181 659 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-tourism-snow-indicators https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-tourism-snow-indicators sis-tourism-snow-indicators This dataset provides meteorological and snow indicators for Europe, characterizing operating conditions of winter ski resorts under past and future climate scenarios. The dataset consists of 39 indicators of atmospheric and snow conditions computed in a similar manner for all mountain regions in Europe at the scale of NUTS-3 regions (Nomenclature of Territorial Units for Statistics) and by steps of 100 m elevation. The snow indicators are generated using the Crocus snowpack model, a multi-layer snowpack model embedded in the land surface model, SURFEX (Surface Externalisée). In order to assess the impact of climate change, the model is run for four different climate scenarios: the present climate (labelled 'historical'), and three Representative Concentration Pathway (RCP) scenarios that correspond to an optimistic emission scenario where emissions start declining beyond 2020 (RCP2.6), a further optimistic emission scenario where emissions start declining beyond 2040 (RCP4.5) and a pessimistic scenario where emissions continue to rise throughout the century, often called the high emission scenario (RCP8.5). In order to simulate these climate scenarios the SURFEX model is forced with atmospheric fields provided by adjusted EURO-CORDEX ensemble climate projections (European branch of the Coordinated Downscaling Experiment). Regional climate models downscaled from global climate models are used to provide the high resolution, pan-European, indicators required to assess the snow reliability for all mountainous regions across Europe. In addition to the climate scenarios, a reanalysis dataset is computed using UERRA reanalysis. A total of 39 indicators are made available in this dataset, divided into seven distinct groups: Start and end date of snow season Annual amount of machine made snow produced Precipitation Snow depth Snow water equivalent Air temperature Potential snow making hours Start and end date of snow season Annual amount of machine made snow produced Precipitation Snow depth Snow water equivalent Air temperature Potential snow making hours The Crocus model makes it possible to account for both snow grooming and mechanical snow-making based upon the physical representation of these snow management practices, adding further value to the end-user by providing indicated snow management requirements under future climate conditions. However, it is not designed to replace higher resolution products available in some European regions that provide a more detailed view of ski conditions; for example, accounting for slope, local meteorological phenomena and local snow management practices. Instead this dataset presents a homogenous product at a pan-European level and hence its main goal is to compare the main features of past and future snow conditions across Europe or to compare distant destinations; for example, Scandinavia and Eastern Europe (for a given elevation and time horizon). This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Time series Horizontal coverage Europe Horizontal resolution NUTS-3 regions Vertical coverage Variables are provided on a single level which may differ among variables Vertical resolution 100 m Temporal coverage From 1950 to 2100 Temporal resolution Annual data: yearly Climatology: 20-30 year aggregation File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Time series Data type Time series Horizontal coverage Europe Horizontal coverage Europe Horizontal resolution NUTS-3 regions Horizontal resolution NUTS-3 regions Vertical coverage Variables are provided on a single level which may differ among variables Vertical coverage Variables are provided on a single level which may differ among variables Vertical resolution 100 m Vertical resolution 100 m Temporal coverage From 1950 to 2100 Temporal coverage From 1950 to 2100 Temporal resolution Annual data: yearly Climatology: 20-30 year aggregation Temporal resolution Annual data: yearly Climatology: 20-30 year aggregation Annual data: yearly Climatology: 20-30 year aggregation File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Conventions Climate and Forecast (CF) Metadata Convention v1.7 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Annual amount of machine made snow produced kg m-2 The total amount of machine made snow for the period August 1st of year N-1 to July 31st of year N, where N is the selected year. End of the longest period with groomed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a groomed snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. End of the longest period with managed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a managed (groomed and machine made) snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. End of the longest period with natural snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a natural snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Mean winter air temperature K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in November year N-1 to April of year N (inclusive). Monthly mean air temperature for April K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in April of year N. Monthly mean air temperature for December K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in December of year N-1. Monthly mean air temperature for February K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in February of year N. Monthly mean air temperature for January K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in January of year N. Monthly mean air temperature for March K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in March of year N. Monthly mean air temperature for November K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in November of year N-1. Period with high amount of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a groomed snow simulation. Period with high amount of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a managed (groomed and machine made) snow simulation. Period with high amount of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a natural snow simulation. Period with high height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a groomed snow simulation. Period with high height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a managed (groomed and machine made) snow simulation. Period with high height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a natural snow simulation. Period with low height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a groomed snow simulation. Period with low height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a managed (groomed and machine made) snow simulation. Period with low height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a natural snow simulation. Period with medium amount of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a groomed snow simulation. Period with medium amount of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a managed (groomed and machine made) snow simulation. Period with medium amount of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a natural snow simulation. Period with medium height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a groomed snow simulation. Period with medium height of groomed snow between the fourth and tenth December day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a groomed snow simulation. Maximum value is 7. Period with medium height of groomed snow between twenty second December and fourth January day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a groomed snow simulation. Maximum value is 14. Period with medium height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Period with medium height of managed snow between the fourth and tenth December day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Maximum value is 7. Period with medium height of managed snow between twenty second December and fourth January day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Maximum value is 14. Period with medium height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a natural snow simulation. Period with medium height of natural snow between the fourth and tenth December day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a natural snow simulation. Maximum value is 7. Period with medium height of natural snow between twenty second December and fourth January day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a natural snow simulation. Maximum value is 14. Snow making hours for WBT lower than -2°C hour Computed wet bulb temperature (WBT) from temperature and relative humidity every 6 hours and interpolated linearly to an hourly time resolution. Expressed as the number of hours, from November 1st of year N-1 to December 31st of year N-1, for which wet buld temperature is less than -2°C. Snow making hours for WBT lower than -5°C hour Computed wet bulb temperature (WBT) from temperature and relative humidity every 6 hours and interpolated linearly to an hourly time resolution. Expressed as the number of hours, from November 1st of year N-1 to December 31st of year N-1, for which wet buld temperature is less than -5°C. Start of the longest period with groomed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a groomed snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Start of the longest period with managed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a managed (groomed and machine made) snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Start of the longest period with natural snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a natural snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Total precipitation from November to April kg m-2 Cumulative value of snowfall and rain precipitation over the winter sports season (November year N-1 to April year N). Total snow precipitation from November to April kg m-2 Cumulative value of snowfall precipitation over the winter sports season (November year N-1 to April year N). MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Annual amount of machine made snow produced kg m-2 The total amount of machine made snow for the period August 1st of year N-1 to July 31st of year N, where N is the selected year. Annual amount of machine made snow produced kg m-2 The total amount of machine made snow for the period August 1st of year N-1 to July 31st of year N, where N is the selected year. End of the longest period with groomed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a groomed snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. End of the longest period with groomed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a groomed snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. End of the longest period with managed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a managed (groomed and machine made) snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. End of the longest period with managed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a managed (groomed and machine made) snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. End of the longest period with natural snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a natural snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. End of the longest period with natural snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a natural snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Mean winter air temperature K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in November year N-1 to April of year N (inclusive). Mean winter air temperature K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in November year N-1 to April of year N (inclusive). Monthly mean air temperature for April K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in April of year N. Monthly mean air temperature for April K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in April of year N. Monthly mean air temperature for December K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in December of year N-1. Monthly mean air temperature for December K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in December of year N-1. Monthly mean air temperature for February K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in February of year N. Monthly mean air temperature for February K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in February of year N. Monthly mean air temperature for January K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in January of year N. Monthly mean air temperature for January K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in January of year N. Monthly mean air temperature for March K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in March of year N. Monthly mean air temperature for March K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in March of year N. Monthly mean air temperature for November K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in November of year N-1. Monthly mean air temperature for November K Average of 6-hourly temperature of air at 2 m above the surface of land, sea or in-land waters for all dates in November of year N-1. Period with high amount of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a groomed snow simulation. Period with high amount of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a groomed snow simulation. Period with high amount of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a managed (groomed and machine made) snow simulation. Period with high amount of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a managed (groomed and machine made) snow simulation. Period with high amount of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a natural snow simulation. Period with high amount of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 120 kg m-2" using a natural snow simulation. Period with high height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a groomed snow simulation. Period with high height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a groomed snow simulation. Period with high height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a managed (groomed and machine made) snow simulation. Period with high height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a managed (groomed and machine made) snow simulation. Period with high height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a natural snow simulation. Period with high height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 50 cm" using a natural snow simulation. Period with low height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a groomed snow simulation. Period with low height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a groomed snow simulation. Period with low height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a managed (groomed and machine made) snow simulation. Period with low height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a managed (groomed and machine made) snow simulation. Period with low height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a natural snow simulation. Period with low height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 5 cm" using a natural snow simulation. Period with medium amount of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a groomed snow simulation. Period with medium amount of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a groomed snow simulation. Period with medium amount of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a managed (groomed and machine made) snow simulation. Period with medium amount of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a managed (groomed and machine made) snow simulation. Period with medium amount of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a natural snow simulation. Period with medium amount of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow water equivalent >= 100 kg m-2" using a natural snow simulation. Period with medium height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a groomed snow simulation. Period with medium height of groomed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a groomed snow simulation. Period with medium height of groomed snow between the fourth and tenth December day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a groomed snow simulation. Maximum value is 7. Period with medium height of groomed snow between the fourth and tenth December day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a groomed snow simulation. Maximum value is 7. Period with medium height of groomed snow between twenty second December and fourth January day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a groomed snow simulation. Maximum value is 14. Period with medium height of groomed snow between twenty second December and fourth January day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a groomed snow simulation. Maximum value is 14. Period with medium height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Period with medium height of managed snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Period with medium height of managed snow between the fourth and tenth December day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Maximum value is 7. Period with medium height of managed snow between the fourth and tenth December day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Maximum value is 7. Period with medium height of managed snow between twenty second December and fourth January day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Maximum value is 14. Period with medium height of managed snow between twenty second December and fourth January day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a managed (groomed and machine made) snow simulation. Maximum value is 14. Period with medium height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a natural snow simulation. Period with medium height of natural snow day The number of days from August 1st of year N-1 to July 31st of year N fulfilling the conditions "Snow depth >= 30 cm" using a natural snow simulation. Period with medium height of natural snow between the fourth and tenth December day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a natural snow simulation. Maximum value is 7. Period with medium height of natural snow between the fourth and tenth December day The number of days from December 4 of year N to December 10 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a natural snow simulation. Maximum value is 7. Period with medium height of natural snow between twenty second December and fourth January day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a natural snow simulation. Maximum value is 14. Period with medium height of natural snow between twenty second December and fourth January day The number of days from December 22 of year N-1 to January 4 of year N (included) fulfilling the condition "Snow depth >= 30 cm" using a natural snow simulation. Maximum value is 14. Snow making hours for WBT lower than -2°C hour Computed wet bulb temperature (WBT) from temperature and relative humidity every 6 hours and interpolated linearly to an hourly time resolution. Expressed as the number of hours, from November 1st of year N-1 to December 31st of year N-1, for which wet buld temperature is less than -2°C. Snow making hours for WBT lower than -2°C hour Computed wet bulb temperature (WBT) from temperature and relative humidity every 6 hours and interpolated linearly to an hourly time resolution. Expressed as the number of hours, from November 1st of year N-1 to December 31st of year N-1, for which wet buld temperature is less than -2°C. Snow making hours for WBT lower than -5°C hour Computed wet bulb temperature (WBT) from temperature and relative humidity every 6 hours and interpolated linearly to an hourly time resolution. Expressed as the number of hours, from November 1st of year N-1 to December 31st of year N-1, for which wet buld temperature is less than -5°C. Snow making hours for WBT lower than -5°C hour Computed wet bulb temperature (WBT) from temperature and relative humidity every 6 hours and interpolated linearly to an hourly time resolution. Expressed as the number of hours, from November 1st of year N-1 to December 31st of year N-1, for which wet buld temperature is less than -5°C. Start of the longest period with groomed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a groomed snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Start of the longest period with groomed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a groomed snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Start of the longest period with managed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a managed (groomed and machine made) snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Start of the longest period with managed snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a managed (groomed and machine made) snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Start of the longest period with natural snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a natural snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Start of the longest period with natural snow day Identified longest continuous period from August 1st of year N-1 to July 31st of year N where the snow depth is continuously above 30 cm using a natural snow simulation. The first date, within this continuous period, meeting the condition "Snow depth >= 30 cm" is the beginning of the season. The last date within this continuous period, meeting this condition, is the end of the season. In case only one date meets the condition, then beginning of season and end of season are attributed this value. In case no date meets the condition (i.e., Snow depth is lower than 30 cm for the entire year), no date is attributed (value of 0). The value assigned is interpreted as the number of days after August 1st of year N-1. Total precipitation from November to April kg m-2 Cumulative value of snowfall and rain precipitation over the winter sports season (November year N-1 to April year N). Total precipitation from November to April kg m-2 Cumulative value of snowfall and rain precipitation over the winter sports season (November year N-1 to April year N). Total snow precipitation from November to April kg m-2 Cumulative value of snowfall precipitation over the winter sports season (November year N-1 to April year N). Total snow precipitation from November to April kg m-2 Cumulative value of snowfall precipitation over the winter sports season (November year N-1 to April year N). 660 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/lake-water-quality-2002-2012-raster-300-m-global-10-daily https://land.copernicus.eu/global/products/ Lake Water Quality 2002-2012 (raster 300 m), global, 10-daily - version 1 Monitoring water quality in lakes and reservoirs is key in maintaining safe water for drinking, bathing, fishing and agriculture and aquaculture activities. Long-term trends and short-term changes are indicators of environmental health and changes in the water catchment area. Directives such as the EU's Water Framework Directive or the US EPA Clean Water Act request information about the ecological status of all lakes larger than 50 ha. Satellite monitoring helps to systematically cover a large number of lakes and reservoirs, reducing needs for monitoring infrastructure (e.g. vessels) and efforts. The Lake Water Quality products provide a semi-continuous observation record for a large number of medium and large-sized lakes, according to the Global Lakes and Wetlands Database (GLWD) or otherwise of specific environmental monitoring interest. They consist of three water quality parameters: (i) the turbidity of a lake, that describes water clarity or whether sunlight can penetrate deeper parts of the lake. Turbidity often varies seasonally, both with the discharge of rivers and growth of phytoplankton; (ii) the trophic state index that is an indicator of the productivity of a lake in terms of phytoplankton, and indirectly, over longer time scales, reflects the eutrophication status of a water body and (iii) the lake surface reflectances that describe the apparent colour of the water body and are intended for scientific users interested in further development of algorithms. The visual reflectance bands can also be combined into true-colour images. 661 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/lake-water-quality-2002-2012-raster-1-km-global-10-daily https://land.copernicus.eu/global/products/ Lake Water Quality 2002-2012 (raster 1 km), global, 10-daily - version 1 Monitoring water quality in lakes and reservoirs is key in maintaining safe water for drinking, bathing, fishing and agriculture and aquaculture activities. Long-term trends and short-term changes are indicators of environmental health and changes in the water catchment area. Directives such as the EU's Water Framework Directive or the US EPA Clean Water Act request information about the ecological status of all lakes larger than 50 ha. Satellite monitoring helps to systematically cover a large number of lakes and reservoirs, reducing needs for monitoring infrastructure (e.g. vessels) and efforts. The Lake Water Quality products provide a semi-continuous observation record for a large number of medium and large-sized lakes, according to the Global Lakes and Wetlands Database (GLWD) or otherwise of specific environmental monitoring interest. They consist of three water quality parameters: (i) the turbidity of a lake, that describes water clarity or whether sunlight can penetrate deeper parts of the lake. Turbidity often varies seasonally, both with the discharge of rivers and growth of phytoplankton; (ii) the trophic state index that is an indicator of the productivity of a lake in terms of phytoplankton, and indirectly, over longer time scales, reflects the eutrophication status of a water body and (iii) the lake surface reflectances that describe the apparent colour of the water body and are intended for scientific users interested in further development of algorithms. The visual reflectance bands can also be combined into true-colour images. 662 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/lake-water-quality-2016-2018-raster-1-km-global-10-daily https://land.copernicus.eu/global/products/ Lake Water Quality 2016-2018 (raster 1 km), global, 10-daily - version 1 Monitoring water quality in lakes and reservoirs is key in maintaining safe water for drinking, bathing, fishing and agriculture and aquaculture activities. Long-term trends and short-term changes are indicators of environmental health and changes in the water catchment area. Directives such as the EU's Water Framework Directive or the US EPA Clean Water Act request information about the ecological status of all lakes larger than 50 ha. Satellite monitoring helps to systematically cover a large number of lakes and reservoirs, reducing needs for monitoring infrastructure (e.g. vessels) and efforts. The Lake Water Quality products provide a semi-continuous observation record for a large number of medium and large-sized lakes, according to the Global Lakes and Wetlands Database (GLWD) or otherwise of specific environmental monitoring interest. They consist of three water quality parameters: (i) the turbidity of a lake, that describes water clarity or whether sunlight can penetrate deeper parts of the lake. Turbidity often varies seasonally, both with the discharge of rivers and growth of phytoplankton; (ii) the trophic state index that is an indicator of the productivity of a lake in terms of phytoplankton, and indirectly, over longer time scales, reflects the eutrophication status of a water body and (iii) the lake surface reflectances that describe the apparent colour of the water body and are intended for scientific users interested in further development of algorithms. The visual reflectance bands can also be combined into true-colour images. 663 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/lake-water-quality-2019-present-raster-100-m-global-10 https://land.copernicus.eu/global/products/lwq Lake Water Quality 2019-present (raster 100 m), global, 10-daily - version 1 Monitoring water quality in lakes and reservoirs is key in maintaining safe water for drinking, bathing, fishing and agriculture and aquaculture activities. Long-term trends and short-term changes are indicators of environmental health and changes in the water catchment area. Directives such as the EU's Water Framework Directive or the US EPA Clean Water Act request information about the ecological status of all lakes larger than 50 ha. Satellite monitoring helps to systematically cover a large number of lakes and reservoirs, reducing needs for monitoring infrastructure (e.g. vessels) and efforts. The Lake Water Quality products provide a semi-continuous observation record for a large number of medium and large-sized lakes, according to the Global Lakes and Wetlands Database (GLWD) or otherwise of specific environmental monitoring interest. They consist of three water quality parameters: (i) the turbidity of a lake, that describes water clarity or whether sunlight can penetrate deeper parts of the lake. Turbidity often varies seasonally, both with the discharge of rivers and growth of phytoplankton; (ii) the trophic state index that is an indicator of the productivity of a lake in terms of phytoplankton, and indirectly, over longer time scales, reflects the eutrophication status of a water body and (iii) the lake surface reflectances that describe the apparent colour of the water body and are intended for scientific users interested in further development of algorithms. The visual reflectance bands can also be combined into true-colour images. 664 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/lake-water-quality-2016-present-raster-300-m-global-10 https://land.copernicus.eu/global/products/ Lake Water Quality 2016-present (raster 300 m), global, 10-daily - version 1 Monitoring water quality in lakes and reservoirs is key in maintaining safe water for drinking, bathing, fishing and agriculture and aquaculture activities. Long-term trends and short-term changes are indicators of environmental health and changes in the water catchment area. Directives such as the EU's Water Framework Directive or the US EPA Clean Water Act request information about the ecological status of all lakes larger than 50 ha. Satellite monitoring helps to systematically cover a large number of lakes and reservoirs, reducing needs for monitoring infrastructure (e.g. vessels) and efforts. The Lake Water Quality products provide a semi-continuous observation record for a large number of medium and large-sized lakes, according to the Global Lakes and Wetlands Database (GLWD) or otherwise of specific environmental monitoring interest. They consist of three water quality parameters: (i) the turbidity of a lake, that describes water clarity or whether sunlight can penetrate deeper parts of the lake. Turbidity often varies seasonally, both with the discharge of rivers and growth of phytoplankton; (ii) the trophic state index that is an indicator of the productivity of a lake in terms of phytoplankton, and indirectly, over longer time scales, reflects the eutrophication status of a water body and (iii) the lake surface reflectances that describe the apparent colour of the water body and are intended for scientific users interested in further development of algorithms. The visual reflectance bands can also be combined into true-colour images. 665 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/smos-catds-qualified-l2q-sea-surface-salinity-product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014 SMOS CATDS Qualified (L2Q) Sea Surface Salinity product Short description: The product MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014 is a reformatting and a simplified version of the CATDS L3 product called “2Q” or “L2Q”. it is an intermediate product, that provides, in daily files, SSS corrected from land-sea contamination and latitudinal bias, with/without rain freshening correction. DOI (product) :https://doi.org/10.1016/j.rse.2016.02.061 https://doi.org/10.1016/j.rse.2016.02.061 666 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-sea-ice-concentration-time-series http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_GLO_SEAICE_L4_REP_OBSERVATIONS_011_009 Global Ocean Sea Ice Concentration Time Series REPROCESSED (OSI-SAF) Short description: The CDR and ICDR sea ice concentration dataset of the EUMETSAT OSI SAF (OSI-450-a and OSI-430-a), covering the period from October 1978 to present, with 16 days delay. It used passive microwave data from SMMR, SSM/I and SSMIS. Sea ice concentration is computed from atmospherically corrected PMW brightness temperatures, using a combination of state-of-the-art algorithms and dynamic tie points. It includes error bars for each grid cell (uncertainties). This version 3.0 of the CDR (OSI-450-a, 1978-2020) and ICDR (OSI-430-a, 2021-present with 16 days latency) was released in November 2022 DOI (product) :https://doi.org/10.48670/moi-00136 https://doi.org/10.48670/moi-00136 667 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-acidification-mean-sea-water-ph-trend-map http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_HEALTH_carbon_ph_trend Global ocean acidification - mean sea water pH trend map from Multi-Observations Reprocessing DEFINITION This ocean monitoring indicator (OMI) consists of annual mean rates of changes in surface ocean pH (yr-1) computed at 1°×1° resolution from 1985 until the last year. This indicator is derived from monthly pH time series distributed with the Copernicus Marine product MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008 (Chau et al., 2022a). For each grid cell, a linear least-squares regression was used to fit a linear function of pH versus time, where the slope (μ) and residual standard deviation (σ) are defined as estimates of the long-term trend and associated uncertainty. Finally, the estimates of pH associated with the highest uncertainty, i.e., σ-to-µ ratio over a threshold of 1 0%, are excluded from the global trend map (see QUID document for detailed description and method illustrations). This threshold is chosen at the 90th confidence level of all ratio values computed across the global ocean. CONTEXT A decrease in surface ocean pH (i.e., ocean acidification) is primarily a consequence of an increase in ocean uptake of atmospheric carbon dioxide (CO2) concentrations that have been augmented by anthropogenic emissions (Bates et al, 2014; Gattuso et al, 2015; Pérez et al, 2021). As projected in Gattuso et al (2015), “under our current rate of emissions, most marine organisms evaluated will have very high risk of impacts by 2100 and many by 2050”. Ocean acidification is thus an ongoing source of concern due to its strong influence on marine ecosystems (e.g., Doney et al., 2009; Gehlen et al., 2011; Pörtner et al. 2019). Tracking changes in yearly mean values of surface ocean pH at the global scale has become an important indicator of both ocean acidification and global change (Gehlen et al., 2020; Chau et al., 2022b). In line with a sustained establishment of ocean measuring stations and thus a rapid increase in observations of ocean pH and other carbonate variables (e.g. dissolved inorganic carbon, total alkalinity, and CO2 fugacity) since the last decades (Bakker et al., 2016; Lauvset et al., 2021), recent studies including Bates et al (2014), Lauvset et al (2015), and Pérez et al (2021) put attention on analyzing secular trends of pH and their drivers from time-series stations to ocean basins. This OMI consists of the global maps of long-term pH trends and associated 1σ-uncertainty derived from the Copernicus Marine data-based product of monthly surface water pH (Chau et al., 2022a) at 1°×1° grid cells over the global ocean. CMEMS KEY FINDINGS Since 1985, pH has been decreasing at a rate between -0.0008 yr-1 and -0.0022 yr-1 over most of the global ocean basins. Tropical and subtropical regions, the eastern equatorial Pacific excepted, show pH trends falling in the interquartile range of all the trend estimates (between -0.0012 yr-1 and -0.0018 yr-1). pH over the eastern equatorial Pacific decreases much faster, reaching a growth rate larger than -0.0024 yr-1. Such a high rate of change in pH is also observed over a sector south of the Indian Ocean. Part of the polar and subpolar North Atlantic and the Southern Ocean has no significant trend. DOI (product):https://doi.org/10.48670/moi-00277 https://doi.org/10.48670/moi-00277 668 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/normalised-difference-vegetation-index-statistics-1999 https://land.copernicus.eu/global/access Normalised Difference Vegetation Index Statistics 1999-2017 (raster 1 km), global, 10-daily - version 2 The Normalised Difference Vegetation Index (NDVI) is a proxy to quantify the vegetation amount. It is defined as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared band, and Red to the reflectance in the red band. The time series of dekadal (10-daily) NDVI 1km version 2 observations over the period 1999-2017 is used to calculate Long Term Statistics (LTS) for each of the 36 10-daily periods (dekads) of the year. The calculated LTS include the minimum, median, maximum, average, standard deviation and the number of observations in the covered time series period. These LTS can be used as a reference for actual NDVI observations, which allows evaluating whether vegetation conditions deviate from a ?normal? situation. 669 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-reanalysis-eac4-monthly https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4-monthly cams-global-reanalysis-eac4-monthly EAC4 (ECMWF Atmospheric Composition Reanalysis 4) is the fourth generation ECMWF global reanalysis of atmospheric composition. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. The assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available. The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with. The observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. For this reason, EAC4 is only available from 2003 onwards. Although the analysis procedure considers chunks of data in a window of 12 hours in one go, EAC4 provides estimates every 3 hours, worldwide. This is made possible by the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 0.75°x0.75° Vertical coverage Surface, total column, 1 model level and 25 pressure levels. Vertical resolution The lowest model level, Pressure levels: 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa Temporal coverage 2003 to 2022 Temporal resolution monthly File format GRIB (optional conversion to netCDF) Versions Only one version Update frequency Twice a year with 4-6 month delay DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.75°x0.75° Horizontal resolution 0.75°x0.75° Vertical coverage Surface, total column, 1 model level and 25 pressure levels. Vertical coverage Surface, total column, 1 model level and 25 pressure levels. Vertical resolution The lowest model level, Pressure levels: 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa Vertical resolution The lowest model level, Pressure levels: 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa Temporal coverage 2003 to 2022 Temporal coverage 2003 to 2022 Temporal resolution monthly Temporal resolution monthly File format GRIB (optional conversion to netCDF) File format GRIB (optional conversion to netCDF) Versions Only one version Versions Only one version Update frequency Twice a year with 4-6 month delay Update frequency Twice a year with 4-6 month delay MAIN VARIABLES Name Units 2m dewpoint temperature K 2m temperature K Black carbon aerosol optical depth at 550 nm dimensionless Carbon monoxide kg kg-1 Charnock ~ Dust aerosol (0.03 - 0.55 µm) mixing ratio kg kg-1 Dust aerosol (0.55 - 0.9 µm) mixing ratio kg kg-1 Dust aerosol (0.9 - 20 µm) mixing ratio kg kg-1 Dust aerosol optical depth at 550 nm dimensionless Ethane kg kg-1 Formaldehyde kg kg-1 Geopotential m2 s-2 Hydrophilic black carbon aerosol mixing ratio kg kg-1 Hydrophilic organic matter aerosol mixing ratio kg kg-1 Hydrophobic black carbon aerosol mixing ratio kg kg-1 Hydrophobic organic matter aerosol mixing ratio kg kg-1 Hydroxyl radical kg kg-1 Ice temperature layer 1 K Isoprene kg kg-1 Leaf area index, high vegetation m2 m-2 Leaf area index, low vegetation m2 m-2 Mean sea level pressure Pa Methane (chemistry) kg kg-1 Nitric acid kg kg-1 Nitrogen dioxide kg kg-1 Nitrogen monoxide kg kg-1 Organic matter aerosol optical depth at 550 nm dimensionless Ozone kg kg-1 Particulate matter d < 10 µm (PM10) kg m-3 Particulate matter d < 2.5 µm (PM2.5) kg m-3 Peroxyacetyl nitrate kg kg-1 Potential vorticity K m2 kg-1 s-1 Propane kg kg-1 Relative humidity % SO2 precursor mixing ratio kg kg-1 Sea salt aerosol (0.03 - 0.5 µm) mixing ratio kg kg-1 Sea salt aerosol (0.5 - 5 µm) mixing ratio kg kg-1 Sea salt aerosol (5 - 20 µm) mixing ratio kg kg-1 Sea salt aerosol optical depth at 550 nm dimensionless Sea surface temperature K Sea-ice cover (0 - 1) Snow albedo (0 - 1) Snow density kg m-3 Snow depth m of water equivalent Soil temperature level 1 K Specific humidity kg kg-1 Sulphate aerosol mixing ratio kg kg-1 Sulphate aerosol optical depth at 550 nm dimensionless Sulphur dioxide kg kg-1 Surface pressure Pa Temperature K Temperature of snow layer K Total aerosol optical depth at 550 nm dimensionless Total column carbon monoxide kg m-2 Total column ethane kg m-2 Total column formaldehyde kg m-2 Total column hydroxyl radical kg m-2 Total column isoprene kg m-2 Total column methane kg m-2 Total column nitric acid kg m-2 Total column nitrogen dioxide kg m-2 Total column nitrogen monoxide kg m-2 Total column ozone kg m-2 Total column peroxyacetyl nitrate kg m-2 Total column propane kg m-2 Total column sulphur dioxide kg m-2 Total column water kg m-2 Total column water vapour kg m-2 Vertical velocity Pa s-1 Vertically integrated mass of dust aerosol (0.03 - 0.55 µm) kg m-2 Vertically integrated mass of dust aerosol (0.55 - 9 µm) kg m-2 Vertically integrated mass of dust aerosol (9 - 20 µm) kg m-2 Vertically integrated mass of hydrophilic black carbon aerosol kg m-2 Vertically integrated mass of hydrophilic organic matter aerosol kg m-2 Vertically integrated mass of hydrophobic black carbon aerosol kg m-2 Vertically integrated mass of hydrophobic organic matter aerosol kg m-2 Vertically integrated mass of sea salt aerosol (0.03 - 0.5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (0.5 - 5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (5 - 20 µm) kg m-2 Vertically integrated mass of sulphate aerosol kg m-2 Vertically integrated mass of sulphur dioxide kg m-2 MAIN VARIABLES MAIN VARIABLES Name Units Name Units 2m dewpoint temperature K 2m dewpoint temperature K 2m temperature K 2m temperature K Black carbon aerosol optical depth at 550 nm dimensionless Black carbon aerosol optical depth at 550 nm dimensionless Carbon monoxide kg kg-1 Carbon monoxide kg kg-1 Charnock ~ Charnock ~ Dust aerosol (0.03 - 0.55 µm) mixing ratio kg kg-1 Dust aerosol (0.03 - 0.55 µm) mixing ratio kg kg-1 Dust aerosol (0.55 - 0.9 µm) mixing ratio kg kg-1 Dust aerosol (0.55 - 0.9 µm) mixing ratio kg kg-1 Dust aerosol (0.9 - 20 µm) mixing ratio kg kg-1 Dust aerosol (0.9 - 20 µm) mixing ratio kg kg-1 Dust aerosol optical depth at 550 nm dimensionless Dust aerosol optical depth at 550 nm dimensionless Ethane kg kg-1 Ethane kg kg-1 Formaldehyde kg kg-1 Formaldehyde kg kg-1 Geopotential m2 s-2 Geopotential m2 s-2 Hydrophilic black carbon aerosol mixing ratio kg kg-1 Hydrophilic black carbon aerosol mixing ratio kg kg-1 Hydrophilic organic matter aerosol mixing ratio kg kg-1 Hydrophilic organic matter aerosol mixing ratio kg kg-1 Hydrophobic black carbon aerosol mixing ratio kg kg-1 Hydrophobic black carbon aerosol mixing ratio kg kg-1 Hydrophobic organic matter aerosol mixing ratio kg kg-1 Hydrophobic organic matter aerosol mixing ratio kg kg-1 Hydroxyl radical kg kg-1 Hydroxyl radical kg kg-1 Ice temperature layer 1 K Ice temperature layer 1 K Isoprene kg kg-1 Isoprene kg kg-1 Leaf area index, high vegetation m2 m-2 Leaf area index, high vegetation m2 m-2 Leaf area index, low vegetation m2 m-2 Leaf area index, low vegetation m2 m-2 Mean sea level pressure Pa Mean sea level pressure Pa Methane (chemistry) kg kg-1 Methane (chemistry) kg kg-1 Nitric acid kg kg-1 Nitric acid kg kg-1 Nitrogen dioxide kg kg-1 Nitrogen dioxide kg kg-1 Nitrogen monoxide kg kg-1 Nitrogen monoxide kg kg-1 Organic matter aerosol optical depth at 550 nm dimensionless Organic matter aerosol optical depth at 550 nm dimensionless Ozone kg kg-1 Ozone kg kg-1 Particulate matter d < 10 µm (PM10) kg m-3 Particulate matter d < 10 µm (PM10) kg m-3 Particulate matter d < 2.5 µm (PM2.5) kg m-3 Particulate matter d < 2.5 µm (PM2.5) kg m-3 Peroxyacetyl nitrate kg kg-1 Peroxyacetyl nitrate kg kg-1 Potential vorticity K m2 kg-1 s-1 Potential vorticity K m2 kg-1 s-1 Propane kg kg-1 Propane kg kg-1 Relative humidity % Relative humidity % SO2 precursor mixing ratio kg kg-1 SO2 precursor mixing ratio kg kg-1 Sea salt aerosol (0.03 - 0.5 µm) mixing ratio kg kg-1 Sea salt aerosol (0.03 - 0.5 µm) mixing ratio kg kg-1 Sea salt aerosol (0.5 - 5 µm) mixing ratio kg kg-1 Sea salt aerosol (0.5 - 5 µm) mixing ratio kg kg-1 Sea salt aerosol (5 - 20 µm) mixing ratio kg kg-1 Sea salt aerosol (5 - 20 µm) mixing ratio kg kg-1 Sea salt aerosol optical depth at 550 nm dimensionless Sea salt aerosol optical depth at 550 nm dimensionless Sea surface temperature K Sea surface temperature K Sea-ice cover (0 - 1) Sea-ice cover (0 - 1) Snow albedo (0 - 1) Snow albedo (0 - 1) Snow density kg m-3 Snow density kg m-3 Snow depth m of water equivalent Snow depth m of water equivalent Soil temperature level 1 K Soil temperature level 1 K Specific humidity kg kg-1 Specific humidity kg kg-1 Sulphate aerosol mixing ratio kg kg-1 Sulphate aerosol mixing ratio kg kg-1 Sulphate aerosol optical depth at 550 nm dimensionless Sulphate aerosol optical depth at 550 nm dimensionless Sulphur dioxide kg kg-1 Sulphur dioxide kg kg-1 Surface pressure Pa Surface pressure Pa Temperature K Temperature K Temperature of snow layer K Temperature of snow layer K Total aerosol optical depth at 550 nm dimensionless Total aerosol optical depth at 550 nm dimensionless Total column carbon monoxide kg m-2 Total column carbon monoxide kg m-2 Total column ethane kg m-2 Total column ethane kg m-2 Total column formaldehyde kg m-2 Total column formaldehyde kg m-2 Total column hydroxyl radical kg m-2 Total column hydroxyl radical kg m-2 Total column isoprene kg m-2 Total column isoprene kg m-2 Total column methane kg m-2 Total column methane kg m-2 Total column nitric acid kg m-2 Total column nitric acid kg m-2 Total column nitrogen dioxide kg m-2 Total column nitrogen dioxide kg m-2 Total column nitrogen monoxide kg m-2 Total column nitrogen monoxide kg m-2 Total column ozone kg m-2 Total column ozone kg m-2 Total column peroxyacetyl nitrate kg m-2 Total column peroxyacetyl nitrate kg m-2 Total column propane kg m-2 Total column propane kg m-2 Total column sulphur dioxide kg m-2 Total column sulphur dioxide kg m-2 Total column water kg m-2 Total column water kg m-2 Total column water vapour kg m-2 Total column water vapour kg m-2 Vertical velocity Pa s-1 Vertical velocity Pa s-1 Vertically integrated mass of dust aerosol (0.03 - 0.55 µm) kg m-2 Vertically integrated mass of dust aerosol (0.03 - 0.55 µm) kg m-2 Vertically integrated mass of dust aerosol (0.55 - 9 µm) kg m-2 Vertically integrated mass of dust aerosol (0.55 - 9 µm) kg m-2 Vertically integrated mass of dust aerosol (9 - 20 µm) kg m-2 Vertically integrated mass of dust aerosol (9 - 20 µm) kg m-2 Vertically integrated mass of hydrophilic black carbon aerosol kg m-2 Vertically integrated mass of hydrophilic black carbon aerosol kg m-2 Vertically integrated mass of hydrophilic organic matter aerosol kg m-2 Vertically integrated mass of hydrophilic organic matter aerosol kg m-2 Vertically integrated mass of hydrophobic black carbon aerosol kg m-2 Vertically integrated mass of hydrophobic black carbon aerosol kg m-2 Vertically integrated mass of hydrophobic organic matter aerosol kg m-2 Vertically integrated mass of hydrophobic organic matter aerosol kg m-2 Vertically integrated mass of sea salt aerosol (0.03 - 0.5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (0.03 - 0.5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (0.5 - 5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (0.5 - 5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (5 - 20 µm) kg m-2 Vertically integrated mass of sea salt aerosol (5 - 20 µm) kg m-2 Vertically integrated mass of sulphate aerosol kg m-2 Vertically integrated mass of sulphate aerosol kg m-2 Vertically integrated mass of sulphur dioxide kg m-2 Vertically integrated mass of sulphur dioxide kg m-2 670 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-biogeochemistry-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_ANALYSIS_FORECAST_BIO_001_028 Global Ocean Biogeochemistry Analysis and Forecast Short description: The Operational Mercator Ocean biogeochemical global ocean analysis and forecast system at 1/4 degree is providing 10 days of 3D global ocean forecasts updated weekly. The time series is aggregated in time, in order to reach a two full year’s time series sliding window. This product includes daily and monthly mean files of biogeochemical parameters (chlorophyll, nitrate, phosphate, silicate, dissolved oxygen, dissolved iron, primary production, phytoplankton, PH, and surface partial pressure of carbon dioxyde) over the global ocean. The global ocean output files are displayed with a 1/4 degree horizontal resolution with regular longitude/latitude equirectangular projection. 50 vertical levels are ranging from 0 to 5700 meters. * NEMO version (v3.6_STABLE) * Forcings: GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 at daily frequency. * Outputs mean fields are interpolated on a standard regular grid in NetCDF format. * Initial conditions: World Ocean Atlas 2013 for nitrate, phosphate, silicate and dissolved oxygen, GLODAPv2 for DIC and Alkalinity, and climatological model outputs for Iron and DOC * Quality/Accuracy/Calibration information: See the related QuID[http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-028.pdf] http://marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-028.pdf DOI (product) :https://doi.org/10.48670/moi-00015 https://doi.org/10.48670/moi-00015 671 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-acidification-mean-sea-water-ph-time-series http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_OMI_HEALTH_carbon_ph_area_averaged Global Ocean acidification - mean sea water pH time series and trend from Multi-Observations Reprocessing DEFINITION Ocean acidification is quantified by decreases in pH, which is a measure of acidity: a decrease in pH value means an increase in acidity, that is, acidification. The observed decrease in ocean pH resulting from increasing concentrations of CO2 is an important indicator of global change. The estimate of global mean pH builds on a reconstruction methodology, * Obtain values for alkalinity based on the so called “locally interpolated alkalinity regression (LIAR)” method after Carter et al., 2016; 2018. * Build on surface ocean partial pressure of carbon dioxide (CMEMS product: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) obtained from an ensemble of Feed-Forward Neural Networks (Chau et al. 2022) which exploit sampling data gathered in the Surface Ocean CO2 Atlas (SOCAT) (https://www.socat.info/) * Derive a gridded field of ocean surface pH based on the van Heuven et al., (2011) CO2 system calculations using reconstructed pCO2 (MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) and alkalinity. The global mean average of pH at yearly time steps is then calculated from the gridded ocean surface pH field. It is expressed in pH unit on total hydrogen ion scale. In the figure, the amplitude of the uncertainty (1σ ) of yearly mean surface sea water pH varies at a range of (0.0023, 0.0029) pH unit (see Quality Information Document for more details). The trend and uncertainty estimates amount to -0.0017±0.0004e-1 pH units per year. The indicator is derived from in situ observations of CO2 fugacity (SOCAT data base, www.socat.info, Bakker et al., 2016). These observations are still sparse in space and time. Monitoring pH at higher space and time resolutions, as well as in coastal regions will require a denser network of observations and preferably direct pH measurements. A full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020). https://www.socat.info/ www.socat.info CONTEXT The decrease in surface ocean pH is a direct consequence of the uptake by the ocean of carbon dioxide. It is referred to as ocean acidification. The International Panel on Climate Change (IPCC) Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems (2011) defined Ocean Acidification as “a reduction in the pH of the ocean over an extended period, typically decades or longer, which is caused primarily by uptake of carbon dioxide from the atmosphere, but can also be caused by other chemical additions or subtractions from the ocean”. The pH of contemporary surface ocean waters is already 0.1 lower than at pre-industrial times and an additional decrease by 0.33 pH units is projected over the 21st century in response to the high concentration pathway RCP8.5 (Bopp et al., 2013). Ocean acidification will put marine ecosystems at risk (e.g. Orr et al., 2005; Gehlen et al., 2011; Kroeker et al., 2013). The monitoring of surface ocean pH has become a focus of many international scientific initiatives (http://goa-on.org/) and constitutes one target for SDG14 (https://sustainabledevelopment.un.org/sdg14). http://goa-on.org/ https://sustainabledevelopment.un.org/sdg14 CMEMS KEY FINDINGS Since the year 1985, global ocean surface pH is decreasing at a rate of -0.0017±0.0004e-1 per year. DOI (product):https://doi.org/10.48670/moi-00224 https://doi.org/10.48670/moi-00224 672 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-hydrology-seasonal-forecast-explorer https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-hydrology-seasonal-forecast-explorer app-hydrology-seasonal-forecast-explorer This application presents European hydrological seasonal forecasts of monthly mean river discharge from a multi-model ensemble of hydrological models. Information from seasonal-range forecasts are of value for many parts of the water sector, for example in planning management of rivers for hydropower and environmental flows. The seasonal forecasts are based on the SEAS5 meteorological forecasts from ECMWF, which are bias adjusted with a quantile mapping method and used to produce forecasts in a hydrological multi-model system. The hydrological models consist of an E-HYPE multi-model at catchment resolution, as well as the gridded models E-HYPEgrid, VIC-WUR, and the addition of the separate EFAS forecast system. The interactive map explores the most probable forecasts from the selected hydrological models, in terms of above, near or below normal conditions. Clicking on a specific grid point or catchment displays a more detailed visualisation of the spread between ensemble members for each model. User-selectable parameters User-selectable parameters Year: The year the forecast was issued Month: The month the forecast was issued Lead time: The number of months ahead for which the forecast is valid Forecast model: The hydrological model used to generate the forecast Year: The year the forecast was issued Month: The month the forecast was issued Lead time: The number of months ahead for which the forecast is valid Forecast model: The hydrological model used to generate the forecast INPUT VARIABLES Name Units Description Source Fair ranked probability skill score Dimensionless Monthly skill metrics as fairRPSS (fair ranked probability skill score) against a climate reference over the reference period 1993-2016. The fairRPSS skill score is a proper score function that measures the performance of probabilistic forecasts. For more information on how the fairRPSS was calculated we refer to the documentation. Seasonal forecasts of river discharge Reference river discharge lower tercile m3 s-1 The lower tercile of the river discharge for the reference period. Seasonal forecasts of river discharge Reference river discharge upper tercile m3 s-1 The upper tercile of the river discharge for the reference period. Seasonal forecasts of river discharge River discharge m3 s-1 Volume rate of water flow, including sediments, chemical and biological material in the river channel averaged over a time step through a cross-section. Seasonal forecasts of river discharge INPUT VARIABLES INPUT VARIABLES Name Units Description Source Name Units Description Source Fair ranked probability skill score Dimensionless Monthly skill metrics as fairRPSS (fair ranked probability skill score) against a climate reference over the reference period 1993-2016. The fairRPSS skill score is a proper score function that measures the performance of probabilistic forecasts. For more information on how the fairRPSS was calculated we refer to the documentation. Seasonal forecasts of river discharge Fair ranked probability skill score Dimensionless Monthly skill metrics as fairRPSS (fair ranked probability skill score) against a climate reference over the reference period 1993-2016. The fairRPSS skill score is a proper score function that measures the performance of probabilistic forecasts. For more information on how the fairRPSS was calculated we refer to the documentation. Seasonal forecasts of river discharge Seasonal forecasts of river discharge Reference river discharge lower tercile m3 s-1 The lower tercile of the river discharge for the reference period. Seasonal forecasts of river discharge Reference river discharge lower tercile m3 s-1 The lower tercile of the river discharge for the reference period. Seasonal forecasts of river discharge Seasonal forecasts of river discharge Reference river discharge upper tercile m3 s-1 The upper tercile of the river discharge for the reference period. Seasonal forecasts of river discharge Reference river discharge upper tercile m3 s-1 The upper tercile of the river discharge for the reference period. Seasonal forecasts of river discharge Seasonal forecasts of river discharge River discharge m3 s-1 Volume rate of water flow, including sediments, chemical and biological material in the river channel averaged over a time step through a cross-section. Seasonal forecasts of river discharge River discharge m3 s-1 Volume rate of water flow, including sediments, chemical and biological material in the river channel averaged over a time step through a cross-section. Seasonal forecasts of river discharge Seasonal forecasts of river discharge OUTPUT VARIABLES Name Units Description Most probable forecast Dimensionless Categorical index describing whether the forecasted river discharge is above, near or below normal. The categories are: above normal (forecasted discharge above the upper tercile); near normal (forecasted discharge below or equal to the upper tercile AND above or equal to the lower tercile); below normal (forecasted discharge below the lower tercile); or inconclusive (no dominating category, or ambiguous forecast between above and below normal conditions). OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Most probable forecast Dimensionless Categorical index describing whether the forecasted river discharge is above, near or below normal. The categories are: above normal (forecasted discharge above the upper tercile); near normal (forecasted discharge below or equal to the upper tercile AND above or equal to the lower tercile); below normal (forecasted discharge below the lower tercile); or inconclusive (no dominating category, or ambiguous forecast between above and below normal conditions). Most probable forecast Dimensionless Categorical index describing whether the forecasted river discharge is above, near or below normal. The categories are: above normal (forecasted discharge above the upper tercile); near normal (forecasted discharge below or equal to the upper tercile AND above or equal to the lower tercile); below normal (forecasted discharge below the lower tercile); or inconclusive (no dominating category, or ambiguous forecast between above and below normal conditions). 673 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/grassland-2018-raster-100-m-europe-3-yearly-aug-2020 https://land.copernicus.eu/pan-european/high-resolution-layers/grassland/status-maps/grassland-2018 Grassland 2018 (raster 100 m), Europe, 3-yearly, Aug. 2020 The HRL Grassland 2018 100 m aggregate raster product provides a basic land cover classification with two thematic classes (grassland / non-grassland) at 100m spatial resolution, covering the EEA38 area and the United Kingdom. The production of the High Resolution Grassland layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. The main High Resolution Grassland product is the Grassland layer. This grassy and non-woody vegetation baseline product includes all kinds of grasslands: managed grassland, semi-natural grassland and natural grassy vegetation. It is a binary status layer for the 2015 reference year mapping grassland and all non-grassland areas in 20m and (aggregated) 100m pixel size and, for the 2018 reference year, in 10m and (aggregated) 100m pixel size. The 100 meter aggregate raster is provided as a full EEA38 and United Kingdom mosaic (fully conformant with the EEA reference grid). 674 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/pan-european-very-high-resolution-image-mosaic-2012-true https://land.copernicus.eu/imagery-in-situ/european-image-mosaics/very-high-resolution/vhr-2012 Pan-European Very High Resolution Image Mosaic 2012 - True Colour (2,5 m), June 2015 The pan-European Very High Resolution (VHR) Image Mosaic 2012 provides optical VHR2 (Very High Resolution: > 1m and <= 4m) coverage over Europe. The surface covered by the image dataset is 7,3 million square kilometres and has a spatial resolution of 2,5 m. The acquisition window of the imagery is January 2011 to December 2013. Images are derived from the following satellite sensors: • SPOT-5 at 2.5m pan-sharpened • SPOT-6 at 1.5 m pan-sharpened • FORMOSAT-2 at 2m pan-sharpened The mosaic primarily is used as input data in the production of various Copernicus Land Monitoring Service (CLMS) datasets and services, such as land cover maps and high resolution layers on land cover characteristic and can be also useful for CLMS users for visualizations and classifications on land. The input imagery for the creation of the mosaic is provided by ESA. Due to license restrictions, VHR Image Mosaic 2012 is only available as a web service (WMS), and not for data download. 675 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/app-c3s-daily-era5-statistics https://cds.climate.copernicus.eu/cdsapp#!/dataset/app-c3s-daily-era5-statistics app-c3s-daily-era5-statistics This application allows users to compute and download selected daily statistics of variables from a number of hourly ERA5 datasets. It provides users with a simple tool to obtain ERA5 data aggregated at daily frequency without having to download the original sub-daily resolution data. The ERA5 data is subset to the selected rectangular spatial region of interest and sampled at the selected frequency. Time coordinates can optionally be shifted to a selected time zone. The data is then aggregated to a daily frequency using the selected statistic and returned to the user in a single netCDF file. User-selectable parameters User-selectable parameters Dataset: the ERA5 dataset to which the variable to be processed belongs. Product type: the type of the data product to be processed, as defined in the original dataset download form. Variable: the variable to be processed. Pressure level: the pressure level of the variable to be processed, if applicable. Statistic: the statistic to use for the daily aggregations of the original data. Year: the year of the data to be processed, ranging from 1979 to the last available year. Month: the month of the data to be processed, up to the last available complete month. Time zone: The time-shift to apply prior to sampling and calculating the daily statistics. The daily aggregation is always calculated over the 24 hours starting from 00:00:00 in the selected time zone. Frequency: The frequency to sample the source data prior to calculating the daily statistics. The first selected time step of each day is always 00:00:00 in the selected timezone. Grid: longitude/latitude grid resolution of the input (and output) data. Geographical area: bounds of the rectangular area of the data to be processed. Dataset: the ERA5 dataset to which the variable to be processed belongs. Dataset Product type: the type of the data product to be processed, as defined in the original dataset download form. Product type Variable: the variable to be processed. Variable Pressure level: the pressure level of the variable to be processed, if applicable. Pressure level Statistic: the statistic to use for the daily aggregations of the original data. Statistic Year: the year of the data to be processed, ranging from 1979 to the last available year. Year Month: the month of the data to be processed, up to the last available complete month. Month Time zone: The time-shift to apply prior to sampling and calculating the daily statistics. The daily aggregation is always calculated over the 24 hours starting from 00:00:00 in the selected time zone. Time zone Frequency: The frequency to sample the source data prior to calculating the daily statistics. The first selected time step of each day is always 00:00:00 in the selected timezone. Frequency Grid: longitude/latitude grid resolution of the input (and output) data. Grid Geographical area: bounds of the rectangular area of the data to be processed. Geographical area INPUT VARIABLES Name Description Source ERA5 pressure levels variables All variables listed in the "ERA5 hourly data on pressure levels from 1979 to present" dataset. ERA5 pressure levels ERA5 single levels variables All variables listed in the "ERA5 hourly data on single levels from 1979 to present" dataset. ERA5 single levels ERA5-Land variables All variables listed in the "ERA5-Land hourly data from 1981 to present" dataset. ERA5-Land INPUT VARIABLES INPUT VARIABLES Name Description Source Name Description Source ERA5 pressure levels variables All variables listed in the "ERA5 hourly data on pressure levels from 1979 to present" dataset. ERA5 pressure levels ERA5 pressure levels variables All variables listed in the "ERA5 hourly data on pressure levels from 1979 to present" dataset. ERA5 pressure levels ERA5 pressure levels ERA5 single levels variables All variables listed in the "ERA5 hourly data on single levels from 1979 to present" dataset. ERA5 single levels ERA5 single levels variables All variables listed in the "ERA5 hourly data on single levels from 1979 to present" dataset. ERA5 single levels ERA5 single levels ERA5-Land variables All variables listed in the "ERA5-Land hourly data from 1981 to present" dataset. ERA5-Land ERA5-Land variables All variables listed in the "ERA5-Land hourly data from 1981 to present" dataset. ERA5-Land ERA5-Land OUTPUT VARIABLES Name Units Description Daily aggregated variables As original variable Daily statistics of variables from "ERA5 hourly data on single levels from 1979 to present" dataset OUTPUT VARIABLES OUTPUT VARIABLES Name Units Description Name Units Description Daily aggregated variables As original variable Daily statistics of variables from "ERA5 hourly data on single levels from 1979 to present" dataset Daily aggregated variables As original variable Daily statistics of variables from "ERA5 hourly data on single levels from 1979 to present" dataset 676 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-total-surface-and-15m-current-copernicus http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MULTIOBS_GLO_PHY_REP_015_004 Global Total Surface and 15m Current (COPERNICUS-GLOBCURRENT) from Altimetric Geostrophic Current  and Modeled Ekman Current Reprocessing Short description: This product is a REP L4 global total velocity field at 0m and 15m. It consists of the zonal and meridional velocity at a 3h frequency and at 1/4 degree regular grid. These total velocity fields are obtained by combining CMEMS REP satellite Geostrophic surface currents and modelled Ekman currents at the surface and 15m depth (using ECMWF ERA5 wind stress). 3 hourly product, daily and monthly means are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been consolidated during the Globcurrent project (funded by the ESA User Element Program). DOI (product) :https://doi.org/10.48670/moi-00050 https://doi.org/10.48670/moi-00050 Product Citation: Please refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag…. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… 677 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-sea-ice-drift-reprocessed http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_ARC_SEAICE_L3_REP_OBSERVATIONS_011_010 Arctic Ocean Sea Ice Drift REPROCESSED Short description: Arctic sea ice drift dataset at 3, 6 and 30 day lag during winter. The Arctic low resolution sea ice drift products provided from IFREMER have a 62.5 km grid resolution. They are delivered as daily products at 3, 6 and 30 days for the cold season extended at fall and spring: from September until May, it is updated on a monthly basis. The data are Merged product from radiometer and scatterometer : * SSM/I 85 GHz V & H Merged product (1992-1999) DOI (product) :https://doi.org/10.48670/moi-00126 https://doi.org/10.48670/moi-00126 678 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/fraction-vegetation-cover-1999-2020-raster-1-km-global-0 http://land.copernicus.eu/global/products/fcover Fraction of Vegetation Cover 1999-2020 (raster 1 km), global, 10-daily - version 1 Fraction of vegetation Cover (FCOVER) corresponds to the gap fraction for nadir direction. It is used to separate vegetation and soil in energy balance processes, including temperature and evapotranspiration. It is computed from the leaf area index and other canopy structural variables and does not depend on variables such as the geometry of illumination as compared to FAPAR. For this reason, it is a very good candidate for the replacement of classical vegetation indices for the monitoring of green vegetation. Because of the linear relationship with radiometric signal, FCOVER will be only marginally scale dependent. Note that similarly to LAI and FAPAR, only the green elements will be considered, either belonging both to the overstorey and understorey. 679 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-situ-near-real-time-observations http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_BLK_PHYBGCWAV_DISCRETE_MYNRT_013_034 Black Sea- In-Situ Near Real Time Observations Short description: Black Sea - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average DOI (product) :https://doi.org/10.48670/moi-00033 https://doi.org/10.48670/moi-00033 680 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/efas-seasonal https://cds.climate.copernicus.eu/cdsapp#!/dataset/efas-seasonal efas-seasonal This dataset provides gridded modelled daily hydrological time series forced with seasonal meteorological forecasts. The dataset is a consistent representation of the most important hydrological variables across the European Flood Awareness (EFAS) domain. The temporal resolution is daily forecasts initialised once a month consisting of: River discharge Soil moisture for three soil layers Snow water equivalent River discharge Soil moisture for three soil layers Snow water equivalent Also provided are auxiliary (time invariant) data to aid interpretation of river discharge and soil moisture data. These auxiliary data are the upstream area, elevation, soil depth, wilting capacity and field capacity. The latter three are provided at three soil levels, one for each of the three soil layers represented in LISFLOOD. This data set was produced by forcing the open-source LISFLOOD hydrological model at a 1x1 arcminute resolution (~1.5 km at EFAS latitudes) with seasonal meteorological ensemble forecasts. For version 4.0 and older, the open-source LISFLOOD hydrological model was forced at a 5x5km resolution. The forecasts are initialised on the first of each month with a lead time of 215 days at 24-hour time steps. The meteorological data are seasonal forecasts (SEAS5) from the European Centre of Medium-range Weather Forecasts (ECMWF) with 51 ensemble members. The forecasts are available from November 2020. Companion datasets, also available through the Climate Data Store (CDS), are seasonal reforecasts for research, local skill assessment and post-processing of the seasonal forecasts. There are also medium-range forecasts for users who want to look at shorter time ranges. These are accompanied by historical simulations which can be used to derive the hydrological climatology, and medium-range reforecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS), which is managed, technically implemented and developed by the European Commission’s Joint Research Centre. DATA DESCRIPTION Data type Gridded Projection Regular latitude-longitude grid for version 5.0, and ETRS89 Lambert Azimuthal Equal Area (ETRS-LAEA) for version 4.0 and older. Horizontal coverage Europe - The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east as far as to the Caspian Sea. Horizontal resolution 1x1 arcminute for version 5.0, 5x5km for version 4.0 and older Vertical resolution 3 levels for soil moisture; surface level for river discharge, snow depth water equivalent. Temporal coverage 1 November 2020 to near real-time. Temporal resolution Forecasts are initialized the first of each month at 00 UTC with a 24-hour time step and lead time of 215 days. File format GRIB2 and NetCDF-4 Conventions WMO standards for GRIB2. The NetCDF-4 files inherit the WMO GRIB2 conventions Versions Operational forecasts use the latest version of the EFAS system, hence the version will depend on the forecast initiation date. The version used to produce each individual forecast is included in the metadata, and the date in which it is valid for can be seen in Citation on the right hand side. For a full description of the versions we refer to the wiki pages in the Documentation. Update frequency The EFAS forecasts are published on CDS every month. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Projection Regular latitude-longitude grid for version 5.0, and ETRS89 Lambert Azimuthal Equal Area (ETRS-LAEA) for version 4.0 and older. Projection Regular latitude-longitude grid for version 5.0, and ETRS89 Lambert Azimuthal Equal Area (ETRS-LAEA) for version 4.0 and older. Horizontal coverage Europe - The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east as far as to the Caspian Sea. Horizontal coverage Europe - The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east as far as to the Caspian Sea. Horizontal resolution 1x1 arcminute for version 5.0, 5x5km for version 4.0 and older Horizontal resolution 1x1 arcminute for version 5.0, 5x5km for version 4.0 and older Vertical resolution 3 levels for soil moisture; surface level for river discharge, snow depth water equivalent. Vertical resolution 3 levels for soil moisture; surface level for river discharge, snow depth water equivalent. Temporal coverage 1 November 2020 to near real-time. Temporal coverage 1 November 2020 to near real-time. Temporal resolution Forecasts are initialized the first of each month at 00 UTC with a 24-hour time step and lead time of 215 days. Temporal resolution Forecasts are initialized the first of each month at 00 UTC with a 24-hour time step and lead time of 215 days. File format GRIB2 and NetCDF-4 File format GRIB2 and NetCDF-4 Conventions WMO standards for GRIB2. The NetCDF-4 files inherit the WMO GRIB2 conventions Conventions WMO standards for GRIB2. The NetCDF-4 files inherit the WMO GRIB2 conventions Versions Operational forecasts use the latest version of the EFAS system, hence the version will depend on the forecast initiation date. The version used to produce each individual forecast is included in the metadata, and the date in which it is valid for can be seen in Citation on the right hand side. For a full description of the versions we refer to the wiki pages in the Documentation. Versions Operational forecasts use the latest version of the EFAS system, hence the version will depend on the forecast initiation date. The version used to produce each individual forecast is included in the metadata, and the date in which it is valid for can be seen in Citation on the right hand side. For a full description of the versions we refer to the wiki pages in the Documentation. Update frequency The EFAS forecasts are published on CDS every month. Update frequency The EFAS forecasts are published on CDS every month. MAIN VARIABLES Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 24-hour time step. Snow depth water equivalent kg m-2 The value represents the mass of water per square meter if all the snow in the grid box would be melted. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter of soil valid for the cell grid at the corresponding soil layer. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. For more documentation on the calculation of the volumetric soil moisture we refer to the documentation. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 24-hour time step. River discharge in the last 24 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 24-hour time step. Snow depth water equivalent kg m-2 The value represents the mass of water per square meter if all the snow in the grid box would be melted. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Snow depth water equivalent kg m-2 The value represents the mass of water per square meter if all the snow in the grid box would be melted. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter of soil valid for the cell grid at the corresponding soil layer. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. For more documentation on the calculation of the volumetric soil moisture we refer to the documentation. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter of soil valid for the cell grid at the corresponding soil layer. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. For more documentation on the calculation of the volumetric soil moisture we refer to the documentation. RELATED VARIABLES Name Units Description Elevation m The mean height elevation above sea level for each pixel in the EFAS domain. Field capacity mm The amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. Soil depth m Soil depth, positive downward for each of the three soil layers at each grid point. The value is relative from the top of the land surface to the bottom of each layer respectively. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Wilting point mm The minimal amount of water in the soil that the plant requires not to wilt. If the soil water content decreases to this or any lower point a plant wilts and can no longer recover its turgidity when placed in a saturated atmosphere for 12 hours. RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Elevation m The mean height elevation above sea level for each pixel in the EFAS domain. Elevation m The mean height elevation above sea level for each pixel in the EFAS domain. Field capacity mm The amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. Field capacity mm The amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. Soil depth m Soil depth, positive downward for each of the three soil layers at each grid point. The value is relative from the top of the land surface to the bottom of each layer respectively. Soil depth m Soil depth, positive downward for each of the three soil layers at each grid point. The value is relative from the top of the land surface to the bottom of each layer respectively. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Wilting point mm The minimal amount of water in the soil that the plant requires not to wilt. If the soil water content decreases to this or any lower point a plant wilts and can no longer recover its turgidity when placed in a saturated atmosphere for 12 hours. Wilting point mm The minimal amount of water in the soil that the plant requires not to wilt. If the soil water content decreases to this or any lower point a plant wilts and can no longer recover its turgidity when placed in a saturated atmosphere for 12 hours. 681 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/pan-european-high-resolution-image-mosaic-2012-false https://land.copernicus.eu/imagery-in-situ/european-image-mosaics/high-resolution/Image%202012/COV%201 Pan-European High Resolution Image Mosaic 2012 - False Colour, Coverage 1 (20 m), July 2015 The pan-European High Resolution (HR) Image Mosaic 2012 provides HR2 (High Resolution: 20 meter) coverage over Europe. The surface covered by the image dataset is 5.8 million square kilometres and has a spatial resolution of 20 meters. The imagery is composed during specific acquisition windows between 2011 and 2013. Images are derived from the following satellite sensors: Resourcesat-1/-2 SPOT-4/-5 The mosaic primarily is used as input data in the production of various Copernicus Land Monitoring Service (CLMS) datasets and services, such as land cover maps and high resolution layers on land cover characteristic and can be also useful for CLMS users for visualizations and classifications on land. The input imagery for the creation of the mosaic is provided by ESA. Due to license restrictions, HR Image Mosaic 2012 is only available as a web service (WMS), and not for data download. 682 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/high-resolution-vegetation-phenology-and-productivity-0 https://www.wekeo.eu/data?view=viewer&t=1566840390697&z=0¢er=13.08408%2C48.33915&zoom=12.34&layers=W3siaWQiOiJjMCIsImxheWVySWQiOiJFTzpIUlZQUDpEQVQ6VkVHRVRBVElPTi1JTkRJQ0VTL19fREVGQVVMVF9fL0NMTVNfSFJWUFBfVklfTkRWSV8xME0iLCJ6SW5kZXgiOjYwfSx7ImlkIjoiYzEiLCJsYXllcklkIjoiRU86SFJWUFA6REFUOlZFR0VUQVRJT04tSU5ESUNFUy9fX0RFRkFVTFRfXy9DTE1TX0hSVlBQX1ZJX1FGTEFHMl8xME0iLCJ6SW5kZXgiOjgwLCJpc0hpZGRlbiI6dHJ1ZX1d&initial=1 High Resolution Vegetation Phenology and Productivity: Normalized Difference Vegetation Index (raster 10m) - version 1 revision 1, Sep. 2021 This metadata refers to the Normalized Difference Vegetation Index (NDVI) dataset, one of the near real-time (NRT) Vegetation Index products of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP), component of the Copernicus Land Monitoring Service (CLMS). The Normalized Difference Vegetation Index (NDVI) is a widely used, dimensionless vegetation index that is indicative for vegetation density. It is defined as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared band, and Red to the reflectance in the red bands. The NDVI dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for the period from October 2016 until today, with daily updates. Each file has an associated quality indicator (QFLAG2) to assist users with the screening of clouds, shadows from clouds and topography, snow and water surfaces. 683 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-european-north-west-shelf-ocean-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=NWSHELF_ANALYSISFORECAST_BGC_004_002 Atlantic - European North West Shelf - Ocean Biogeochemistry Analysis and Forecast Short description The ocean biogeochemistry analysis and forecast for the North-West European Shelf is produced using the European Regional Seas Ecosystem Model (ERSEM), coupled online to the forecasting ocean assimilation model at 7 km horizontal resolution, NEMO-NEMOVAR. ERSEM (Butenschön et al. 2016) is developed and maintained at Plymouth Marine Laboratory. NEMOVAR system was used to assimilate observations of sea surface chlorophyll concentration from ocean colour satellite data and all the physical variables described in [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NWSHELF_ANALYSISFORECAST_PHY_LR_004_001]. The description of the model and its configuration, including the products validation is provided in the [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004… CMEMS-NWS-QUID-004-002]. Products are provided as daily 25-hour, de-tided, averages. The datasets available are concentration of chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, net primary production, light attenuation coefficient, pH and the partial pressure of CO2. All, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated daily, providing a 6-day forecast and the previous 2-day assimilative hindcast. See [http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-0… CMEMS-NWS-PUM-004-001_002] for details. https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-NWS-QUID-004… http://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-NWS-PUM-004-0… Associated products: This model is coupled with a hydrodynamic model (NEMO) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NWSHELF_ANALYSISFORECAST_PHY_LR_004_001] A reanalysis product is available from: [https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… NWSHELF_MULTIYEAR_BIO_004_011]. https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… https://resources.marine.copernicus.eu/?option=com_csw&view=details&pro… DOI (product) :https://doi.org/10.48670/moi-00056 https://doi.org/10.48670/moi-00056 684 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/grassland-2015-raster-20-m-europe-3-yearly-apr-2018 https://land.copernicus.eu/pan-european/high-resolution-layers/grassland/status-maps/2015/view Grassland 2015 (raster 20 m), Europe, 3-yearly, Apr. 2018 The main high resolution grassland product is the Grassland layer, a grassland/non-grassland mask for the EEA39. This grassy and non-woody vegetation baseline product includes all kinds of grasslands: managed grassland, semi-natural grassland and natural grassy vegetation. It is a binary status layer for the 2015 reference year mapping grassland and all non-grassland areas in 20m and (aggregated) 100m pixel size and for the 2018 reference year - in 10m and (aggregated) 100m pixel size. The production of the high resolution grassland layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 685 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/grassland-2015-raster-100-m-europe-3-yearly-apr-2018 https://land.copernicus.eu/pan-european/high-resolution-layers/grassland/status-maps/2015/view Grassland 2015 (raster 100 m), Europe, 3-yearly, Apr. 2018 The main high resolution grassland product is the Grassland layer, a grassland/non-grassland mask for the EEA39. This grassy and non-woody vegetation baseline product includes all kinds of grasslands: managed grassland, semi-natural grassland and natural grassy vegetation. It is a binary status layer for the 2015 reference year mapping grassland and all non-grassland areas in 20m and (aggregated) 100m pixel size and for the 2018 reference year - in 10m and (aggregated) 100m pixel size. The production of the high resolution grassland layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 686 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/efas-reforecast https://cds.climate.copernicus.eu/cdsapp#!/dataset/efas-reforecast efas-reforecast This dataset provides gridded modelled hydrological time series forced with medium- to sub-seasonal range meteorological reforecasts. The data is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is 20 years of sub-daily reforecasts initialised twice weekly (Mondays and Thursdays) of: River discharge Soil moisture for three soil layers Snow water equivalent River discharge Soil moisture for three soil layers Snow water equivalent Also provided are auxiliary (time invariant) data to aid interpretation of river discharge and soil moisture data. These auxiliary data are the upstream area, elevation, soil depth, wilting capacity and field capacity. The latter three are provided at three soil levels, one for each of the three soil layers represented in LISFLOOD. This dataset was produced by forcing the open-source LISFLOOD hydrological model at a 5x5km resolution with ensemble meteorological reforecasts from the European Centre of Medium-range Weather Forecasts (ECMWF). Reforecasts are forecasts run over past dates and are typically used to assess the skill of a forecast system or to develop tools for statistical error correction of the forecasts. The reforecasts are initialised twice weekly with lead times up to 46 days, at 6-hourly time steps for 20 years. For more specific information on the how the reforecast dataset is produced we refer to the documentation. Companion datasets, also available through the Climate Data Store (CDS), are the operational forecasts, historical simulations which can be used to derive the hydrological climatology, and seasonal forecasts and reforecasts for users looking for long term forecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS), which is managed, technically implemented and developed by the European Commission’s Joint Research Centre. DATA DESCRIPTION Data type Gridded - The geographical projection is the INSPIRE compliant ETRS89 Lambert Azimuthal Equal Area Coordinate Reference System (ETRS-LAEA). Horizontal coverage Europe - The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east as far as to the Caspian Sea. Horizontal resolution 5x5km Vertical resolution 3 levels for soil moisture; surface level for river discharge and snow depth water equivalent. Temporal coverage 3 January 1999 to 30 December 2018. Temporal resolution Reforecasts are initialized at 00 UTC on Mondays and Thursdays with a 6-hourly time step and 46-day lead time. File format GRIB2 and NetCDF-4 Conventions WMO standards for GRIB2. The NetCDF-4 files inherit the WMO GRIB2 conventions. Versions Current version - EFAS v4.0 released 2020-10-14. For more information on versions we refer to the documentation. Update frequency EFAS reforecasts are published at regular intervals on CDS, typically with every major EFAS upgrade. DATA DESCRIPTION DATA DESCRIPTION Data type Gridded - The geographical projection is the INSPIRE compliant ETRS89 Lambert Azimuthal Equal Area Coordinate Reference System (ETRS-LAEA). Data type Gridded - The geographical projection is the INSPIRE compliant ETRS89 Lambert Azimuthal Equal Area Coordinate Reference System (ETRS-LAEA). Horizontal coverage Europe - The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east as far as to the Caspian Sea. Horizontal coverage Europe - The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east as far as to the Caspian Sea. Horizontal resolution 5x5km Horizontal resolution 5x5km Vertical resolution 3 levels for soil moisture; surface level for river discharge and snow depth water equivalent. Vertical resolution 3 levels for soil moisture; surface level for river discharge and snow depth water equivalent. Temporal coverage 3 January 1999 to 30 December 2018. Temporal coverage 3 January 1999 to 30 December 2018. Temporal resolution Reforecasts are initialized at 00 UTC on Mondays and Thursdays with a 6-hourly time step and 46-day lead time. Temporal resolution Reforecasts are initialized at 00 UTC on Mondays and Thursdays with a 6-hourly time step and 46-day lead time. File format GRIB2 and NetCDF-4 File format GRIB2 and NetCDF-4 Conventions WMO standards for GRIB2. The NetCDF-4 files inherit the WMO GRIB2 conventions. Conventions WMO standards for GRIB2. The NetCDF-4 files inherit the WMO GRIB2 conventions. Versions Current version - EFAS v4.0 released 2020-10-14. For more information on versions we refer to the documentation. Versions Current version - EFAS v4.0 released 2020-10-14. For more information on versions we refer to the documentation. Update frequency EFAS reforecasts are published at regular intervals on CDS, typically with every major EFAS upgrade. Update frequency EFAS reforecasts are published at regular intervals on CDS, typically with every major EFAS upgrade. MAIN VARIABLES Name Units Description River discharge in the last 6 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 6-hour time step. Snow depth water equivalent kg m-2 The value represents the mass of water per square meter if all the snow in the grid box would be melted. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter of soil valid for the cell grid at the corresponding soil layer. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. For more documentation on the calculation of the volumetric soil moisture we refer to the documentation. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description River discharge in the last 6 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 6-hour time step. River discharge in the last 6 hours m3 s-1 Volume rate of water flow, including sediments, chemical and biological material, in the river channel averaged over a time step through a cross-section. The value is an average over each 6-hour time step. Snow depth water equivalent kg m-2 The value represents the mass of water per square meter if all the snow in the grid box would be melted. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Snow depth water equivalent kg m-2 The value represents the mass of water per square meter if all the snow in the grid box would be melted. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter of soil valid for the cell grid at the corresponding soil layer. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. For more documentation on the calculation of the volumetric soil moisture we refer to the documentation. Volumetric soil moisture m3 m-3 Amount of water in a cubic meter of soil valid for the cell grid at the corresponding soil layer. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued model time step. For more documentation on the calculation of the volumetric soil moisture we refer to the documentation. RELATED VARIABLES Name Units Description Elevation m The mean height elevation above sea level for each pixel in the EFAS domain. Field capacity mm The amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. Soil depth m Soil depth, positive downward for each of the three soil layers at each grid point. The value is relative from the top of the land surface to the bottom of each layer respectively. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Wilting point mm The minimal amount of water in the soil that the plant requires not to wilt. If the soil water content decreases to this or any lower point a plant wilts and can no longer recover its turgidity when placed in a saturated atmosphere for 12 hours. RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Elevation m The mean height elevation above sea level for each pixel in the EFAS domain. Elevation m The mean height elevation above sea level for each pixel in the EFAS domain. Field capacity mm The amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. Field capacity mm The amount of soil moisture or water content held in the soil after excess water has drained away and the rate of downward movement has decreased. Soil depth m Soil depth, positive downward for each of the three soil layers at each grid point. The value is relative from the top of the land surface to the bottom of each layer respectively. Soil depth m Soil depth, positive downward for each of the three soil layers at each grid point. The value is relative from the top of the land surface to the bottom of each layer respectively. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Upstream area m2 The total upstream area for each river pixel. This is defined as the catchment area for each river segment, meaning the total area that contributes with water to the river at the specific grid point. The upstream area always includes the area of the pixel. Wilting point mm The minimal amount of water in the soil that the plant requires not to wilt. If the soil water content decreases to this or any lower point a plant wilts and can no longer recover its turgidity when placed in a saturated atmosphere for 12 hours. Wilting point mm The minimal amount of water in the soil that the plant requires not to wilt. If the soil water content decreases to this or any lower point a plant wilts and can no longer recover its turgidity when placed in a saturated atmosphere for 12 hours. 687 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/fraction-vegetation-cover-1999-2020-raster-1-km-global-10 http://land.copernicus.eu/global/products/LORUM_IPSUM Fraction of Vegetation Cover 1999-2020 (raster 1 km), global, 10-daily - version 2 FCOVER was defined by CEOS as half the developed area of the convex hull wrapping the green canopy elements per unit horizontal ground. This definition allows accounting for elements which are not flat such as needles or stems. FCOVER is strongly non linearly related to reflectance. Therefore, its estimation from remote sensing observations will be scale dependant over heterogeneous landscapes. When observing a canopy made of different layers of vegetation, it is therefore mandatory to consider all the green layers. This is particularly important for forest canopies where the understory may represent a very significant contribution to the total canopy FCOVER. The derived FCOVER corresponds therefore to the total green FCOVER, including the contribution of the green elements of the understory. The resulting GEOV1 FCOVER products are relatively consistent with the actual FCOVER for low FCOVER values and ?non-forest? surfaces; while for forests, particularly for needle leaf types, significant departures with the true FCOVER are expected. 688 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-total-surface-and-15m-current-copernicus-0 http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MULTIOBS_GLO_PHY_NRT_015_003 Global Total Surface and 15m Current (COPERNICUS-GLOBCURRENT) from Altimetric Geostrophic Current  and Modeled Ekman Current Processing Description: This product is a NRT L4 global total velocity field at 0m and 15m. It consists of the zonal and meridional velocity at a 6h frequency and at 1/4 degree regular grid produced on a daily basis. These total velocity fields are obtained by combining CMEMS NRT satellite Geostrophic Surface Currents and modelled Ekman current at the surface and 15m depth (using ECMWF NRT wind). 6 hourly product, daily and monthly mean are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been consolidated during the Globcurrent project (funded by the ESA User Element Program). DOI (product) :https://doi.org/10.48670/moi-00049 https://doi.org/10.48670/moi-00049 689 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-delayed-mode-situ-observations-surface http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_GLO_PHY_UV_DISCRETE_MY_013_044 Global Ocean-Delayed Mode in-situ Observations of surface (drifters and HFR) and sub-surface (vessel-mounted ADCPs) water velocity Short description: Global Ocean - This delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for ocean surface currents and current vertical profiles. It concerns three delayed time datasets dedicated to near-surface currents measurements coming from two platforms (Lagrangian surface drifters and High Frequency radars) and velocity profiles within the water column coming from the Acoustic Doppler Current Profiler (ADCP, vessel mounted only) platform DOI (product) :https://doi.org/10.17882/86236 https://doi.org/10.17882/86236 690 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-mean-dynamic-topography http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_MED_PHY_MDT_L4_STATIC_008_066 MEDITERRANEAN SEA MEAN DYNAMIC TOPOGRAPHY Short description: The Mean Dynamic Topography MDT-CMEMS_2020_MED is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Mediterranean Sea. This is consistent with the reference time period also used in the SSALTO DUACS products DOI (product) :https://doi.org/10.48670/moi-00151 https://doi.org/10.48670/moi-00151 691 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/european-ground-motion-service-gnss-model-2015-2020 https://land.copernicus.eu/user-corner/technical-library/egms-algorithm-theoretical-basis-document European Ground Motion Service: GNSS model 2015-2020 (vector), Europe, 2-yearly, Jul. 2022 The European Ground Motion Service (EGMS) is a component of the Copernicus Land Monitoring Service. EGMS provides consistent, regular, standardised, harmonised and reliable information regarding natural and anthropogenic ground motion phenomena over the Copernicus Participating States and across national borders, with millimetre accuracy. This set of metadata describes the global navigation satellite system (GNSS) model used to calibrate the EGMS Calibrated product (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/db…). This layer is produced based on GNSS data from various sources, with the EUREF Densification network as the main entry point. After filtering and quality control, a total of 3770 stations are used to generate the GNSS model which contains average velocities in east, north and up directions displayed on a 50-km grid. The grid dimension is determined by the average distance between well-maintained GNSS stations over continental Europe. The GNSS model is distributed to users in a single comma-separated values file. Each cell of the model is associated to a value of vertical and horizontal velocity. The product covers the Copernicus Participating States (except for DROMs) and United Kingdom. https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/db… 692 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/viewer-satellite-methane https://cds.climate.copernicus.eu/cdsapp#!/dataset/viewer-satellite-methane viewer-satellite-methane Viewer application for dataset User-selectable parameters - year: 2003 to 2019 - month: 01 to 12 User-selectable parameters More details about the products are given in the Documentation section. MAIN VARIABLES Name Units Description Column-average dry-air mole fraction of atmospheric methane (XCH4) ppb Average molar mixing ratio (or mole fraction in micro mole methane (CH4) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CH4 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCH4 indicates that the reported quantity is a "mole fraction". Mid-tropospheric columns of atmospheric methane (CH4) ppb Average CH4 mixing ratio of the mid-troposphere. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Column-average dry-air mole fraction of atmospheric methane (XCH4) ppb Average molar mixing ratio (or mole fraction in micro mole methane (CH4) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CH4 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCH4 indicates that the reported quantity is a "mole fraction". Column-average dry-air mole fraction of atmospheric methane (XCH4) ppb Average molar mixing ratio (or mole fraction in micro mole methane (CH4) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CH4 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCH4 indicates that the reported quantity is a "mole fraction". Average molar mixing ratio (or mole fraction in micro mole methane (CH4) per mole dry air) of the total atmospheric column (excluding water vapour molecules) from Earth surface to the top of the atmosphere. The number of CH4 molecules in the column per surface area divided by the number of dry air molecules above the same surface area (note that the size of the surface area cancels in the ratio. Note that the "X" in XCH4 indicates that the reported quantity is a "mole fraction". Mid-tropospheric columns of atmospheric methane (CH4) ppb Average CH4 mixing ratio of the mid-troposphere. Mid-tropospheric columns of atmospheric methane (CH4) ppb Average CH4 mixing ratio of the mid-troposphere. RELATED VARIABLES The optimal estimation inversion algorithms used to compute the column average CH4 are based on a number of atmospheric variables like pressure, temperature, water vapour, scattering by aerosols and clouds, spectral albedo, including initial a-priori values and averaging kernels as well as estimates of uncertainty on the values of CH4. Depending on the sensor and on the algorithm, a number of these variables are also included in the files along the main variable CH4. RELATED VARIABLES RELATED VARIABLES The optimal estimation inversion algorithms used to compute the column average CH4 are based on a number of atmospheric variables like pressure, temperature, water vapour, scattering by aerosols and clouds, spectral albedo, including initial a-priori values and averaging kernels as well as estimates of uncertainty on the values of CH4. Depending on the sensor and on the algorithm, a number of these variables are also included in the files along the main variable CH4. The optimal estimation inversion algorithms used to compute the column average CH4 are based on a number of atmospheric variables like pressure, temperature, water vapour, scattering by aerosols and clouds, spectral albedo, including initial a-priori values and averaging kernels as well as estimates of uncertainty on the values of CH4. Depending on the sensor and on the algorithm, a number of these variables are also included in the files along the main variable CH4. 693 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/top-canopy-reflectances-1999-2018-raster-1-km-global-10 https://land.copernicus.eu/global/products/toc-r Top of Canopy Reflectances 1999-2018 (raster 1 km), global, 10-daily - version 1 The TOC Spectral reflectance refers to the portion of the incident energy reflected by the surface in a given spectral band and without atmospheric interferences. Because of the natural anisotropy of the land surface, reflectance depends on the illumination and viewing angular conditions. Therefore, to compare and use jointly successive observations, it is necessary to normalize the measurements into a same angular configuration. The resulting value is the normalized TOC reflectance. 694 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-mean-dynamic-topography http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_BLK_PHY_MDT_L4_STATIC_008_067 BLACK SEA MEAN DYNAMIC TOPOGRAPHY Short description: The Mean Dynamic Topography MDT-CMEMS_2020_BLK is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Black Sea. This is consistent with the reference time period also used in the SSALTO DUACS products DOI (product) :https://doi.org/10.48670/moi-00138 https://doi.org/10.48670/moi-00138 695 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/sis-soil-erosion https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-soil-erosion sis-soil-erosion This dataset provides rainfall erosivity (R factor), associated indicators, and the potential for soil loss induced by water erosion for Italy. The dataset is derived from integrating rainfall data included in Climate Data Store with non-climate data to assess soil susceptibility to water erosion according to Revised Universal Soil Loss Equation (RUSLE) approach. The gridded dataset can support the decision-making process of many stakeholders for strategical planning purposes across different sectors addressed by the Copernicus Climate Change Service. The dataset provides: Key information on water erosion dynamics in terms of R factor and potential soil loss for the historical period. The assessment of R factor uses as input gridded observations (E-OBS) and reanalysis data (ERA5, ERA5-Land) for precipitation included in Climate Data Store. The soil loss assessment is obtained by further operating the R factor derived from ERA5-Land with non-climate gridded data representing soil susceptibility to water erosion in accordance to the RUSLE formulation. These are provided at ≈500m horizontal resolution in this dataset. Key information on water erosion dynamics in terms of R factor and potential soil loss for future periods. The assessment is again based on the RUSLE approach and uses as input precipitation data included in the EURO-CORDEX ensemble climate projections (0.11°) under several Representative Concentration Pathways (RCPs). In this case, bias-corrected monthly precipitation data are used and the R factor is provided both at native horizontal resolution and regridded to ≈500m to be operated with other RUSLE factors to derive potential soil loss for the future. Additional R factor proxy variables, based on daily precipitation data, are provided at native horizontal resolution and as anomalies with respect to the historical period, with no bias-correction. Key information on water erosion dynamics in terms of R factor and potential soil loss for the historical period. The assessment of R factor uses as input gridded observations (E-OBS) and reanalysis data (ERA5, ERA5-Land) for precipitation included in Climate Data Store. The soil loss assessment is obtained by further operating the R factor derived from ERA5-Land with non-climate gridded data representing soil susceptibility to water erosion in accordance to the RUSLE formulation. These are provided at ≈500m horizontal resolution in this dataset. Key information on water erosion dynamics in terms of R factor and potential soil loss for the historical period. The assessment of R factor uses as input gridded observations (E-OBS) and reanalysis data (ERA5, ERA5-Land) for precipitation included in Climate Data Store. The soil loss assessment is obtained by further operating the R factor derived from ERA5-Land with non-climate gridded data representing soil susceptibility to water erosion in accordance to the RUSLE formulation. These are provided at ≈500m horizontal resolution in this dataset. Key information on water erosion dynamics in terms of R factor and potential soil loss for future periods. The assessment is again based on the RUSLE approach and uses as input precipitation data included in the EURO-CORDEX ensemble climate projections (0.11°) under several Representative Concentration Pathways (RCPs). In this case, bias-corrected monthly precipitation data are used and the R factor is provided both at native horizontal resolution and regridded to ≈500m to be operated with other RUSLE factors to derive potential soil loss for the future. Additional R factor proxy variables, based on daily precipitation data, are provided at native horizontal resolution and as anomalies with respect to the historical period, with no bias-correction. Key information on water erosion dynamics in terms of R factor and potential soil loss for future periods. The assessment is again based on the RUSLE approach and uses as input precipitation data included in the EURO-CORDEX ensemble climate projections (0.11°) under several Representative Concentration Pathways (RCPs). In this case, bias-corrected monthly precipitation data are used and the R factor is provided both at native horizontal resolution and regridded to ≈500m to be operated with other RUSLE factors to derive potential soil loss for the future. Additional R factor proxy variables, based on daily precipitation data, are provided at native horizontal resolution and as anomalies with respect to the historical period, with no bias-correction. This dataset was produced on behalf of the Copernicus Climate Change Service. DATA DESCRIPTION Data type Gridded Horizontal coverage Italy Horizontal resolution High resolution data: remapped datasets are available for R factor and soil loss at 500 m x 500 m resolution for the ERA5-Land and EURO-CORDEX output. Native resolution data: the R factor and precipitation variables are provided at the native-resolution for ERA5, ERA5-Land, E-OBS and EURO-CORDEX output. Vertical coverage Surface Vertical resolution Single level Temporal coverage 1981-2080 (2011-2020 excluded) Temporal resolution Historical period: single value representing the 30-year period from 1981 to 2010 for R factor and soil loss. This is also the reference period for the future climate projections. Future periods: single value representing the 30-year periods 2021-2050 and 2051-2080 for R factor and soil loss as absolute values. Other indicators are presented as anomalies with respect to the reference period 1981-2010. File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.4 and greater (based on input data), Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Update frequency No updates expected DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Italy Horizontal coverage Italy Horizontal resolution High resolution data: remapped datasets are available for R factor and soil loss at 500 m x 500 m resolution for the ERA5-Land and EURO-CORDEX output. Native resolution data: the R factor and precipitation variables are provided at the native-resolution for ERA5, ERA5-Land, E-OBS and EURO-CORDEX output. Horizontal resolution High resolution data: remapped datasets are available for R factor and soil loss at 500 m x 500 m resolution for the ERA5-Land and EURO-CORDEX output. Native resolution data: the R factor and precipitation variables are provided at the native-resolution for ERA5, ERA5-Land, E-OBS and EURO-CORDEX output. High resolution data: remapped datasets are available for R factor and soil loss at 500 m x 500 m resolution for the ERA5-Land and EURO-CORDEX output. Native resolution data: the R factor and precipitation variables are provided at the native-resolution for ERA5, ERA5-Land, E-OBS and EURO-CORDEX output. Vertical coverage Surface Vertical coverage Surface Vertical resolution Single level Vertical resolution Single level Temporal coverage 1981-2080 (2011-2020 excluded) Temporal coverage 1981-2080 (2011-2020 excluded) Temporal resolution Historical period: single value representing the 30-year period from 1981 to 2010 for R factor and soil loss. This is also the reference period for the future climate projections. Future periods: single value representing the 30-year periods 2021-2050 and 2051-2080 for R factor and soil loss as absolute values. Other indicators are presented as anomalies with respect to the reference period 1981-2010. Temporal resolution Historical period: single value representing the 30-year period from 1981 to 2010 for R factor and soil loss. This is also the reference period for the future climate projections. Future periods: single value representing the 30-year periods 2021-2050 and 2051-2080 for R factor and soil loss as absolute values. Other indicators are presented as anomalies with respect to the reference period 1981-2010. Historical period: single value representing the 30-year period from 1981 to 2010 for R factor and soil loss. This is also the reference period for the future climate projections. Future periods: single value representing the 30-year periods 2021-2050 and 2051-2080 for R factor and soil loss as absolute values. Other indicators are presented as anomalies with respect to the reference period 1981-2010. File format NetCDF-4 File format NetCDF-4 Conventions Climate and Forecast (CF) Metadata Convention v1.4 and greater (based on input data), Attribute Convention for Dataset Discovery (ACDD) v1.3 Conventions Climate and Forecast (CF) Metadata Convention v1.4 and greater (based on input data), Attribute Convention for Dataset Discovery (ACDD) v1.3 Versions 1.0 Versions 1.0 Update frequency No updates expected Update frequency No updates expected MAIN VARIABLES Name Units Description Maximum 1-day precipitation kg m-2 The maximum value of daily precipitation amount per annum averaged over the 30-year periods provided in the dataset. Maximum 5-day precipitation kg m-2 The maximum value of five-day cumulative precipitation per annum computed by moving sum average and further averaged over the 30-year periods provided in the dataset. Events between two years are associated to that in which more days occur. Number of days with LWE greater than 1 mm Count Number of days with at least 1 mm of liquid water equivalent (LWE) per annum averaged over the 30-year periods provided in the dataset. Number of days with LWE greater than 20 mm Count Number of days with at least 20 mm of liquid water equivalent (LWE) per annum averaged over the 30-year periods provided in the dataset. R factor MJ mm ha-1 h-1 yr-1 This is the rainfall erosivity. It is the kinetic energy associated with rainfall-related mechanisms (raindrop impact or rate of associated runoff) used to quantify the effect of rainfall on sheet and rill erosion. It is computed according to six empirical models consolidated in the literature (cited in the documentation) using input data from time series of cumulated rainfall at monthly and/or annual time steps depending on each specific model. The values from the six models are then averaged to derive a single R factor value. The R factor is used in calculating the Revised Universal Soil Loss Equation (RUSLE). Soil loss t ha-1 yr-1 Potential soil loss by rainfall-related rill and inter-rill erosion, obtained using the Revised Universal Soil Loss Equation (RUSLE) approach. Spell length of days with LWE greater than 1 mm Count The maximum number of consecutive days with at least 1 mm of liquid water equivalent (LWE) per annum averaged over the 30-year periods provided in the dataset. Total precipitation kg m-2 Total precipitation amount per annum averaged over the 30-year periods provided in the dataset. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Maximum 1-day precipitation kg m-2 The maximum value of daily precipitation amount per annum averaged over the 30-year periods provided in the dataset. Maximum 1-day precipitation kg m-2 The maximum value of daily precipitation amount per annum averaged over the 30-year periods provided in the dataset. Maximum 5-day precipitation kg m-2 The maximum value of five-day cumulative precipitation per annum computed by moving sum average and further averaged over the 30-year periods provided in the dataset. Events between two years are associated to that in which more days occur. Maximum 5-day precipitation kg m-2 The maximum value of five-day cumulative precipitation per annum computed by moving sum average and further averaged over the 30-year periods provided in the dataset. Events between two years are associated to that in which more days occur. Number of days with LWE greater than 1 mm Count Number of days with at least 1 mm of liquid water equivalent (LWE) per annum averaged over the 30-year periods provided in the dataset. Number of days with LWE greater than 1 mm Count Number of days with at least 1 mm of liquid water equivalent (LWE) per annum averaged over the 30-year periods provided in the dataset. Number of days with LWE greater than 20 mm Count Number of days with at least 20 mm of liquid water equivalent (LWE) per annum averaged over the 30-year periods provided in the dataset. Number of days with LWE greater than 20 mm Count Number of days with at least 20 mm of liquid water equivalent (LWE) per annum averaged over the 30-year periods provided in the dataset. R factor MJ mm ha-1 h-1 yr-1 This is the rainfall erosivity. It is the kinetic energy associated with rainfall-related mechanisms (raindrop impact or rate of associated runoff) used to quantify the effect of rainfall on sheet and rill erosion. It is computed according to six empirical models consolidated in the literature (cited in the documentation) using input data from time series of cumulated rainfall at monthly and/or annual time steps depending on each specific model. The values from the six models are then averaged to derive a single R factor value. The R factor is used in calculating the Revised Universal Soil Loss Equation (RUSLE). R factor MJ mm ha-1 h-1 yr-1 This is the rainfall erosivity. It is the kinetic energy associated with rainfall-related mechanisms (raindrop impact or rate of associated runoff) used to quantify the effect of rainfall on sheet and rill erosion. It is computed according to six empirical models consolidated in the literature (cited in the documentation) using input data from time series of cumulated rainfall at monthly and/or annual time steps depending on each specific model. The values from the six models are then averaged to derive a single R factor value. The R factor is used in calculating the Revised Universal Soil Loss Equation (RUSLE). Soil loss t ha-1 yr-1 Potential soil loss by rainfall-related rill and inter-rill erosion, obtained using the Revised Universal Soil Loss Equation (RUSLE) approach. Soil loss t ha-1 yr-1 Potential soil loss by rainfall-related rill and inter-rill erosion, obtained using the Revised Universal Soil Loss Equation (RUSLE) approach. Spell length of days with LWE greater than 1 mm Count The maximum number of consecutive days with at least 1 mm of liquid water equivalent (LWE) per annum averaged over the 30-year periods provided in the dataset. Spell length of days with LWE greater than 1 mm Count The maximum number of consecutive days with at least 1 mm of liquid water equivalent (LWE) per annum averaged over the 30-year periods provided in the dataset. Total precipitation kg m-2 Total precipitation amount per annum averaged over the 30-year periods provided in the dataset. Total precipitation kg m-2 Total precipitation amount per annum averaged over the 30-year periods provided in the dataset. 696 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-atmospheric-composition-forecasts https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-atmospheric-composition-forecasts cams-global-atmospheric-composition-forecasts CAMS produces global forecasts for atmospheric composition twice a day. The forecasts consist of more than 50 chemical species (e.g. ozone, nitrogen dioxide, carbon monoxide) and seven different types of aerosol (desert dust, sea salt, organic matter, black carbon, sulphate, nitrate and ammonium aerosol). In addition, several meteorological variables are available as well. The initial conditions of each forecast are obtained by combining a previous forecast with current satellite observations through a process called data assimilation. This best estimate of the state of the atmosphere at the initial forecast time step, called the analysis, provides a globally complete and consistent dataset allowing for estimates at locations where observation data coverage is low or for atmospheric pollutants for which no direct observations are available. The forecast itself uses a model of the atmosphere based on the laws of physics and chemistry to determine the evolution of the concentrations of all species over time for the next five days. Apart from the required initial state, it also uses inventory-based or observation-based emission estimates as a boundary condition at the surface. The CAMS global forecasting system is upgraded about once a year resulting in technical and scientific changes. The horizontal or vertical resolution can change, new species can be added, and more generally the accuracy of the forecasts can be improved. Details of these system changes can be found in the documentation. Users looking for a more consistent long-term data set should consider using the CAMS Global Reanalysis instead, which is available through the ADS and spans the period from 2003 onwards. Finally, because some meteorological fields in the forecast do not fall within the general CAMS data licence, they are only available with a delay of 5 days. More details about the products are given in the Documentation section. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 0.4°x0.4° Vertical coverage Surface, total column, model levels and pressure levels. Vertical resolution 60 model levels before July 7 2019 00UTC, then 137 model levels. Pressure levels: 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa Temporal coverage 2015 to present Temporal resolution 1-hourly (single-level), 3-hourly (multi-level) File format GRIB (optional conversion to netCDF) Versions Only one version, but with occasional model upgrades Update frequency New 00UTC and 12UTC forecasts added each day. Model upgrades made approximately once a year DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 0.4°x0.4° Horizontal resolution 0.4°x0.4° Vertical coverage Surface, total column, model levels and pressure levels. Vertical coverage Surface, total column, model levels and pressure levels. Vertical resolution 60 model levels before July 7 2019 00UTC, then 137 model levels. Pressure levels: 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa Vertical resolution 60 model levels before July 7 2019 00UTC, then 137 model levels. Pressure levels: 1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1 hPa Temporal coverage 2015 to present Temporal coverage 2015 to present Temporal resolution 1-hourly (single-level), 3-hourly (multi-level) Temporal resolution 1-hourly (single-level), 3-hourly (multi-level) File format GRIB (optional conversion to netCDF) File format GRIB (optional conversion to netCDF) Versions Only one version, but with occasional model upgrades Versions Only one version, but with occasional model upgrades Update frequency New 00UTC and 12UTC forecasts added each day. Model upgrades made approximately once a year Update frequency New 00UTC and 12UTC forecasts added each day. Model upgrades made approximately once a year MAIN VARIABLES Name Units 10m u-component of wind m s-1 10m v-component of wind m s-1 10m wind gust in the last 3 hours m s-1 2m dewpoint temperature K 2m temperature K Acetone kg kg-1 Acetone product kg kg-1 Acetonitrile kg kg-1 Aerosol extinction coefficient at 1064 nm m-1 Aerosol extinction coefficient at 355 nm m-1 Aerosol extinction coefficient at 532 nm m-1 Aldehydes kg kg-1 Amine kg kg-1 Ammonia kg kg-1 Ammonium kg kg-1 Ammonium aerosol mass mixing ratio kg kg-1 Ammonium aerosol optical depth at 550 nm dimensionless Anthropogenic secondary organic aerosol mass mixing ratio kg kg-1 Asymmetric chlorine dioxide radical kg kg-1 Asymmetry factor at 1020 nm dimensionless Asymmetry factor at 1064 nm dimensionless Asymmetry factor at 1240 nm dimensionless Asymmetry factor at 1640 nm dimensionless Asymmetry factor at 2130 nm dimensionless Asymmetry factor at 340 nm dimensionless Asymmetry factor at 355 nm dimensionless Asymmetry factor at 380 nm dimensionless Asymmetry factor at 400 nm dimensionless Asymmetry factor at 440 nm dimensionless Asymmetry factor at 469 nm dimensionless Asymmetry factor at 500 nm dimensionless Asymmetry factor at 532 nm dimensionless Asymmetry factor at 550 nm dimensionless Asymmetry factor at 645 nm dimensionless Asymmetry factor at 670 nm dimensionless Asymmetry factor at 800 nm dimensionless Asymmetry factor at 858 nm dimensionless Asymmetry factor at 865 nm dimensionless Attenuated backscatter due to aerosol at 1064 nm (from ground) m-1 sr-1 Attenuated backscatter due to aerosol at 1064 nm (from top of atmosphere) m-1 sr-1 Attenuated backscatter due to aerosol at 355 nm (from ground) m-1 sr-1 Attenuated backscatter due to aerosol at 355 nm (from top of atmosphere) m-1 sr-1 Attenuated backscatter due to aerosol at 532 nm (from ground) m-1 sr-1 Attenuated backscatter due to aerosol at 532 nm (from top of atmosphere) m-1 sr-1 Biogenic secondary organic aerosol mass mixing ratio kg kg-1 Black carbon aerosol optical depth at 550 nm dimensionless Boundary layer height m Bromine kg kg-1 Bromine atom kg kg-1 Bromine monochloride kg kg-1 Bromine monoxide kg kg-1 Bromine nitrate kg kg-1 Bromochlorodifluoromethane kg kg-1 Carbon monoxide kg kg-1 Chlorine kg kg-1 Chlorine atom kg kg-1 Chlorine dioxide kg kg-1 Chlorine monoxide kg kg-1 Chlorine nitrate kg kg-1 Chlorodifluoromethane kg kg-1 Chloropentafluoroethane kg kg-1 Clear sky surface photosynthetically active radiation J m-2 Clear-sky direct solar radiation at surface J m-2 Cloud base height m Convective available potential energy J kg-1 Convective inhibition J kg-1 Convective precipitation m Dibromomethane kg kg-1 Dichlorine dioxide kg kg-1 Dichlorodifluoromethane kg kg-1 Dichlorotetrafluoroethane kg kg-1 Dimethyl sulfide kg kg-1 Dinitrogen pentoxide kg kg-1 Direct solar radiation J m-2 Downward UV radiation at the surface J m-2 Dry deposition of ammonium aerosol kg m-2 s-1 Dry deposition of coarse-mode nitrate aerosol kg m-2 s-1 Dry deposition of dust aerosol (0.03 - 0.55 µm) kg m-2 s-1 Dry deposition of dust aerosol (0.55 - 9 µm) kg m-2 s-1 Dry deposition of dust aerosol (9 - 20 µm) kg m-2 s-1 Dry deposition of fine-mode nitrate aerosol kg m-2 s-1 Dry deposition of hydrophilic black carbon aerosol kg m-2 s-1 Dry deposition of hydrophilic organic matter aerosol kg m-2 s-1 Dry deposition of hydrophobic black carbon aerosol kg m-2 s-1 Dry deposition of hydrophobic organic matter aerosol kg m-2 s-1 Dry deposition of sea salt aerosol (0.03 - 0.5 µm) kg m-2 s-1 Dry deposition of sea salt aerosol (0.5 - 5 µm) kg m-2 s-1 Dry deposition of sea salt aerosol (5 - 20 µm) kg m-2 s-1 Dry deposition of sulphate aerosol kg m-2 s-1 Dust aerosol (0.03 - 0.55 µm) mixing ratio kg kg-1 Dust aerosol (0.03 - 0.55 µm) optical depth at 550 nm dimensionless Dust aerosol (0.55 - 0.9 µm) mixing ratio kg kg-1 Dust aerosol (0.55 - 9 µm) optical depth at 550 nm dimensionless Dust aerosol (0.9 - 20 µm) mixing ratio kg kg-1 Dust aerosol (9 - 20 µm) optical depth at 550 nm dimensionless Dust aerosol optical depth at 550 nm dimensionless Ethane kg kg-1 Ethanol kg kg-1 Ethene kg kg-1 Evaporation m of water equivalent Forecast albedo (0 - 1) Formaldehyde kg kg-1 Formic acid kg kg-1 Fraction of cloud cover (0 - 1) Friction velocity m s-1 Geopotential m2 s-2 Glyoxal kg kg-1 Height of convective cloud top m High cloud cover (0 - 1) Hydrogen bromide kg kg-1 Hydrogen chloride kg kg-1 Hydrogen cyanide kg kg-1 Hydrogen fluoride kg kg-1 Hydrogen peroxide kg kg-1 Hydroperoxy radical kg kg-1 Hydrophilic black carbon aerosol mixing ratio kg kg-1 Hydrophilic black carbon aerosol optical depth at 550 nm dimensionless Hydrophilic organic matter aerosol mixing ratio kg kg-1 Hydrophilic organic matter aerosol optical depth at 550 nm dimensionless Hydrophobic black carbon aerosol mixing ratio kg kg-1 Hydrophobic black carbon aerosol optical depth at 550 nm dimensionless Hydrophobic organic matter aerosol mixing ratio kg kg-1 Hydrophobic organic matter aerosol optical depth at 550 nm dimensionless Hydroxyl radical kg kg-1 Hypobromous acid kg kg-1 Hypochlorous acid kg kg-1 Isoprene kg kg-1 Lake cover (0 - 1) Land-sea mask (0 - 1) Large-scale precipitation m Lead kg kg-1 Leaf area index, high vegetation m2 m-2 Leaf area index, low vegetation m2 m-2 Logarithm of surface pressure ~ Low cloud cover (0 - 1) Mean sea level pressure Pa Medium cloud cover (0 - 1) Methacrolein MVK kg kg-1 Methacrylic acid kg kg-1 Methane kg kg-1 Methane sulfonic acid kg kg-1 Methanol kg kg-1 Methyl bromide kg kg-1 Methyl chloride kg kg-1 Methyl chloroform kg kg-1 Methyl glyoxal kg kg-1 Methyl peroxide kg kg-1 Methylperoxy radical kg kg-1 Nitrate kg kg-1 Nitrate aerosol optical depth at 550 nm dimensionless Nitrate coarse mode aerosol mass mixing ratio kg kg-1 Nitrate coarse-mode aerosol optical depth at 550 nm dimensionless Nitrate fine mode aerosol mass mixing ratio kg kg-1 Nitrate fine-mode aerosol optical depth at 550 nm dimensionless Nitrate radical kg kg-1 Nitric acid kg kg-1 Nitrogen dioxide kg kg-1 Nitrogen monoxide kg kg-1 Nitryl chloride kg kg-1 Olefins kg kg-1 Organic ethers kg kg-1 Organic matter aerosol optical depth at 550 nm dimensionless Organic nitrates kg kg-1 Ozone kg kg-1 Paraffins kg kg-1 Particulate matter d < 1 µm (PM1) kg m-3 Particulate matter d < 10 µm (PM10) kg m-3 Particulate matter d < 2.5 µm (PM2.5) kg m-3 Pernitric acid kg kg-1 Peroxides kg kg-1 Peroxy acetyl radical kg kg-1 Peroxyacetyl nitrate kg kg-1 Photosynthetically active radiation at the surface J m-2 Potential evaporation m Potential vorticity K m2 kg-1 s-1 Precipitation type dimensionless Propane kg kg-1 Propene kg kg-1 Radon kg kg-1 Relative humidity % Sea salt aerosol (0.03 - 0.5 µm) mixing ratio kg kg-1 Sea salt aerosol (0.03 - 0.5 µm) optical depth at 550 nm dimensionless Sea salt aerosol (0.5 - 5 µm) mixing ratio kg kg-1 Sea salt aerosol (0.5 - 5 µm) optical depth at 550 nm dimensionless Sea salt aerosol (5 - 20 µm) mixing ratio kg kg-1 Sea salt aerosol (5 - 20 µm) optical depth at 550 nm dimensionless Sea salt aerosol optical depth at 550 nm dimensionless Sea surface temperature K Sea-ice cover (0 - 1) Secondary organic aerosol optical depth at 550 nm dimensionless Sedimentation of ammonium aerosol kg m-2 s-1 Sedimentation of coarse-mode nitrate aerosol kg m-2 s-1 Sedimentation of dust aerosol (0.03 - 0.55 µm) kg m-2 s-1 Sedimentation of dust aerosol (0.55 - 9 µm) kg m-2 s-1 Sedimentation of dust aerosol (9 - 20 µm) kg m-2 s-1 Sedimentation of fine-mode nitrate aerosol kg m-2 s-1 Sedimentation of hydrophilic black carbon aerosol kg m-2 s-1 Sedimentation of hydrophilic organic matter aerosol kg m-2 s-1 Sedimentation of hydrophobic black carbon aerosol kg m-2 s-1 Sedimentation of hydrophobic organic matter aerosol kg m-2 s-1 Sedimentation of sea salt aerosol (0.03 - 0.5 µm) kg m-2 s-1 Sedimentation of sea salt aerosol (0.5 - 5 µm) kg m-2 s-1 Sedimentation of sea salt aerosol (5 - 20 µm) kg m-2 s-1 Sedimentation of sulphate aerosol kg m-2 s-1 Single scattering albedo at 1020 nm (0 - 1) Single scattering albedo at 1064 nm (0 - 1) Single scattering albedo at 1240 nm (0 - 1) Single scattering albedo at 1640 nm (0 - 1) Single scattering albedo at 2130 nm (0 - 1) Single scattering albedo at 340 nm (0 - 1) Single scattering albedo at 355 nm (0 - 1) Single scattering albedo at 380 nm (0 - 1) Single scattering albedo at 400 nm (0 - 1) Single scattering albedo at 440 nm (0 - 1) Single scattering albedo at 469 nm (0 - 1) Single scattering albedo at 500 nm (0 - 1) Single scattering albedo at 532 nm (0 - 1) Single scattering albedo at 550 nm (0 - 1) Single scattering albedo at 645 nm (0 - 1) Single scattering albedo at 670 nm (0 - 1) Single scattering albedo at 800 nm (0 - 1) Single scattering albedo at 858 nm (0 - 1) Single scattering albedo at 865 nm (0 - 1) Skin reservoir content m of water equivalent Skin temperature K Snow albedo (0 - 1) Snow depth m of water equivalent Source/gain of ammonium aerosol kg m-2 s-1 Source/gain of coarse-mode nitrate aerosol kg m-2 s-1 Source/gain of dust aerosol (0.03 - 0.55 µm) kg m-2 s-1 Source/gain of dust aerosol (0.55 - 9 µm) kg m-2 s-1 Source/gain of dust aerosol (9 - 20 µm) kg m-2 s-1 Source/gain of fine-mode nitrate aerosol kg m-2 s-1 Source/gain of hydrophilic black carbon aerosol kg m-2 s-1 Source/gain of hydrophilic organic matter aerosol kg m-2 s-1 Source/gain of hydrophobic black carbon aerosol kg m-2 s-1 Source/gain of hydrophobic organic matter aerosol kg m-2 s-1 Source/gain of sea salt aerosol (0.03 - 0.5 µm) kg m-2 s-1 Source/gain of sea salt aerosol (0.5 - 5 µm) kg m-2 s-1 Source/gain of sea salt aerosol (5 - 20 µm) kg m-2 s-1 Source/gain of sulphate aerosol kg m-2 s-1 Specific cloud ice water content kg kg-1 Specific cloud liquid water content kg kg-1 Specific humidity kg kg-1 Specific rain water content kg kg-1 Specific snow water content kg kg-1 Stratospheric ozone tracer kg kg-1 Sulphate aerosol mixing ratio kg kg-1 Sulphate aerosol optical depth at 550 nm dimensionless Sulphur dioxide kg kg-1 Sunshine duration s Surface Geopotential m2 s-2 Surface latent heat flux J m-2 Surface net solar radiation J m-2 Surface net solar radiation, clear sky J m-2 Surface net thermal radiation J m-2 Surface net thermal radiation, clear sky J m-2 Surface pressure Pa Surface sensible heat flux J m-2 Surface solar radiation downward, clear sky J m-2 Surface solar radiation downwards J m-2 Surface thermal radiation downward, clear sky J m-2 Surface thermal radiation downwards J m-2 TOA incident solar radiation J m-2 Temperature K Terpenes kg kg-1 Tetrachloromethane kg kg-1 Time-integrated dry deposition mass flux of ammonia kg m**-2 Time-integrated dry deposition mass flux of dinitrogen pentoxide kg m**-2 Time-integrated dry deposition mass flux of nitric acid kg m**-2 Time-integrated dry deposition mass flux of nitrogen dioxide kg m**-2 Time-integrated dry deposition mass flux of nitrogen monoxide kg m**-2 Time-integrated dry deposition mass flux of organic nitrates kg m**-2 Time-integrated dry deposition mass flux of ozone kg m**-2 Time-integrated dry deposition mass flux of peroxyacetyl nitrate kg m**-2 Time-integrated dry deposition mass flux of sulphur dioxide kg m**-2 Time-integrated wet deposition mass flux of ammonia kg m**-2 Time-integrated wet deposition mass flux of dinitrogen pentoxide kg m**-2 Time-integrated wet deposition mass flux of nitric acid kg m**-2 Time-integrated wet deposition mass flux of nitrogen dioxide kg m**-2 Time-integrated wet deposition mass flux of nitrogen monoxide kg m**-2 Time-integrated wet deposition mass flux of organic nitrates kg m**-2 Time-integrated wet deposition mass flux of peroxyacetyl nitrate kg m**-2 Time-integrated wet deposition mass flux of sulphur dioxide kg m**-2 Top net solar radiation J m-2 Top net solar radiation, clear sky J m-2 Top net thermal radiation J m-2 Top net thermal radiation, clear sky J m-2 Total absorption aerosol optical depth at 1020 nm dimensionless Total absorption aerosol optical depth at 1064 nm dimensionless Total absorption aerosol optical depth at 1240 nm dimensionless Total absorption aerosol optical depth at 1640 nm dimensionless Total absorption aerosol optical depth at 2130 nm dimensionless Total absorption aerosol optical depth at 340 nm dimensionless Total absorption aerosol optical depth at 355 nm dimensionless Total absorption aerosol optical depth at 380 nm dimensionless Total absorption aerosol optical depth at 400 nm dimensionless Total absorption aerosol optical depth at 440 nm dimensionless Total absorption aerosol optical depth at 469 nm dimensionless Total absorption aerosol optical depth at 500 nm dimensionless Total absorption aerosol optical depth at 532 nm dimensionless Total absorption aerosol optical depth at 550 nm dimensionless Total absorption aerosol optical depth at 645 nm dimensionless Total absorption aerosol optical depth at 670 nm dimensionless Total absorption aerosol optical depth at 800 nm dimensionless Total absorption aerosol optical depth at 858 nm dimensionless Total absorption aerosol optical depth at 865 nm dimensionless Total aerosol optical depth at 1020 nm dimensionless Total aerosol optical depth at 1064 nm dimensionless Total aerosol optical depth at 1240 nm dimensionless Total aerosol optical depth at 1640 nm dimensionless Total aerosol optical depth at 2130 nm dimensionless Total aerosol optical depth at 340 nm dimensionless Total aerosol optical depth at 355 nm dimensionless Total aerosol optical depth at 380 nm dimensionless Total aerosol optical depth at 400 nm dimensionless Total aerosol optical depth at 440 nm dimensionless Total aerosol optical depth at 469 nm dimensionless Total aerosol optical depth at 500 nm dimensionless Total aerosol optical depth at 532 nm dimensionless Total aerosol optical depth at 550 nm dimensionless Total aerosol optical depth at 645 nm dimensionless Total aerosol optical depth at 670 nm dimensionless Total aerosol optical depth at 800 nm dimensionless Total aerosol optical depth at 858 nm dimensionless Total aerosol optical depth at 865 nm dimensionless Total cloud cover (0 - 1) Total column HYPROPO2 kg m-2 Total column IC3H7O2 kg m-2 Total column NO to NO2 operator kg m-2 Total column NO to alkyl nitrate operator kg m-2 Total column acetone kg m-2 Total column acetone product kg m-2 Total column acetonitrile kg m-2 Total column aldehydes kg m-2 Total column amine kg m-2 Total column ammonia kg m-2 Total column ammonium kg m-2 Total column asymmetric chlorine dioxide radical kg m-2 Total column bromine kg m-2 Total column bromine atom kg m-2 Total column bromine monochloride kg m-2 Total column bromine monoxide kg m-2 Total column bromine nitrate kg m-2 Total column bromochlorodifluoromethane kg m-2 Total column carbon monoxide kg m-2 Total column chlorine kg m-2 Total column chlorine atom kg m-2 Total column chlorine dioxide kg m-2 Total column chlorine monoxide kg m-2 Total column chlorine nitrate kg m-2 Total column chlorodifluoromethane kg m-2 Total column chloropentafluoroethane kg m-2 Total column cloud ice water kg m-2 Total column cloud liquid water kg m-2 Total column dibromomethane kg m-2 Total column dichlorine dioxide kg m-2 Total column dichlorodifluoromethane kg m-2 Total column dichlorotetrafluoroethane kg m-2 Total column dimethyl sulfide kg m-2 Total column dinitrogen pentoxide kg m-2 Total column ethane kg m-2 Total column ethanol kg m-2 Total column ethene kg m-2 Total column formaldehyde kg m-2 Total column formic acid kg m-2 Total column glyoxal kg m-2 Total column hydrogen bromide kg m-2 Total column hydrogen chloride kg m-2 Total column hydrogen cyanide kg m-2 Total column hydrogen fluoride kg m-2 Total column hydrogen peroxide kg m-2 Total column hydroperoxy radical kg m-2 Total column hydroxyl radical kg m-2 Total column hypobromous acid kg m-2 Total column hypochlorous acid kg m-2 Total column isoprene kg m-2 Total column lead kg m-2 Total column methacrolein MVK kg m-2 Total column methacrylic acid kg m-2 Total column methane kg m-2 Total column methane sulfonic acid kg m-2 Total column methanol kg m-2 Total column methyl bromide kg m-2 Total column methyl chloride kg m-2 Total column methyl chloroform kg m-2 Total column methyl glyoxal kg m-2 Total column methyl peroxide kg m-2 Total column methylperoxy radical kg m-2 Total column nitrate kg m-2 Total column nitrate radical kg m-2 Total column nitric acid kg m-2 Total column nitrogen dioxide kg m-2 Total column nitrogen monoxide kg m-2 Total column nitrogen oxides transp kg m-2 Total column nitryl chloride kg m-2 Total column olefins kg m-2 Total column organic ethers kg m-2 Total column organic nitrates kg m-2 Total column ozone kg m-2 Total column paraffins kg m-2 Total column pernitric acid kg m-2 Total column peroxides kg m-2 Total column peroxy acetyl radical kg m-2 Total column peroxyacetyl nitrate kg m-2 Total column polar stratospheric cloud kg m-2 Total column propane kg m-2 Total column propene kg m-2 Total column radon kg m-2 Total column rain water kg m-2 Total column snow water kg m-2 Total column stratospheric ozone kg m-2 Total column sulphur dioxide kg m-2 Total column supercooled liquid water kg m-2 Total column terpenes kg m-2 Total column tetrachloromethane kg m-2 Total column tribromomethane kg m-2 Total column trichlorofluoromethane kg m-2 Total column trichlorotrifluoroethane kg m-2 Total column trifluorobromomethane kg m-2 Total column water kg m-2 Total column water vapour kg m-2 Total column water vapour (chemistry) kg m-2 Total fine mode (r < 0.5 µm) aerosol optical depth at 1020 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 1064 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 1240 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 1640 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 2130 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 340 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 355 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 380 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 400 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 440 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 469 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 500 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 532 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 550 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 645 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 670 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 800 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 858 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 865 nm dimensionless Total precipitation m Total sky direct solar radiation at surface J m-2 Tribromomethane kg kg-1 Trichlorofluoromethane kg kg-1 Trichlorotrifluoroethane kg kg-1 Trifluorobromomethane kg kg-1 U-component of wind m s-1 UV biologically effective dose W m-2 UV biologically effective dose, clear sky W m-2 V-component of wind m s-1 Vertical velocity Pa s-1 Vertically integrated mass of ammonium aerosol kg m-2 Vertically integrated mass of coarse-mode nitrate aerosol kg m-2 Vertically integrated mass of dust aerosol (0.03 - 0.55 µm) kg m-2 Vertically integrated mass of dust aerosol (0.55 - 9 µm) kg m-2 Vertically integrated mass of dust aerosol (9 - 20 µm) kg m-2 Vertically integrated mass of fine-mode nitrate aerosol kg m-2 Vertically integrated mass of hydrophilic black carbon aerosol kg m-2 Vertically integrated mass of hydrophilic organic matter aerosol kg m-2 Vertically integrated mass of hydrophobic black carbon aerosol kg m-2 Vertically integrated mass of hydrophobic organic matter aerosol kg m-2 Vertically integrated mass of sea salt aerosol (0.03 - 0.5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (0.5 - 5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (5 - 20 µm) kg m-2 Vertically integrated mass of sulphate aerosol kg m-2 Vertically integrated moisture divergence kg m-2 Visibility km Water vapour (chemistry) kg kg-1 Wet deposition of ammonium aerosol by convective precipitation kg m-2 s-1 Wet deposition of ammonium aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of coarse-mode nitrate aerosol by convective precipitation kg m-2 s-1 Wet deposition of coarse-mode nitrate aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of dust aerosol (0.03 - 0.55 µm) by convective precipitation kg m-2 s-1 Wet deposition of dust aerosol (0.03 - 0.55 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of dust aerosol (0.55 - 9 µm) by convective precipitation kg m-2 s-1 Wet deposition of dust aerosol (0.55 - 9 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of dust aerosol (9 - 20 µm) by convective precipitation kg m-2 s-1 Wet deposition of dust aerosol (9 - 20 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of fine-mode nitrate aerosol by convective precipitation kg m-2 s-1 Wet deposition of fine-mode nitrate aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of hydrophilic black carbon aerosol by convective precipitation kg m-2 s-1 Wet deposition of hydrophilic black carbon aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of hydrophilic organic matter aerosol by convective precipitation kg m-2 s-1 Wet deposition of hydrophilic organic matter aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of hydrophobic black carbon aerosol by convective precipitation kg m-2 s-1 Wet deposition of hydrophobic black carbon aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of hydrophobic organic matter aerosol by convective precipitation kg m-2 s-1 Wet deposition of hydrophobic organic matter aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (0.03 - 0.5 µm) by convective precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (0.03 - 0.5 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (0.5 - 5 µm) by convective precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (0.5 - 5 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (5 - 20 µm) by convective precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (5 - 20 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of sulphate aerosol by convective precipitation kg m-2 s-1 Wet deposition of sulphate aerosol by large-scale precipitation kg m-2 s-1 MAIN VARIABLES MAIN VARIABLES Name Units Name Units 10m u-component of wind m s-1 10m u-component of wind m s-1 10m v-component of wind m s-1 10m v-component of wind m s-1 10m wind gust in the last 3 hours m s-1 10m wind gust in the last 3 hours m s-1 2m dewpoint temperature K 2m dewpoint temperature K 2m temperature K 2m temperature K Acetone kg kg-1 Acetone kg kg-1 Acetone product kg kg-1 Acetone product kg kg-1 Acetonitrile kg kg-1 Acetonitrile kg kg-1 Aerosol extinction coefficient at 1064 nm m-1 Aerosol extinction coefficient at 1064 nm m-1 Aerosol extinction coefficient at 355 nm m-1 Aerosol extinction coefficient at 355 nm m-1 Aerosol extinction coefficient at 532 nm m-1 Aerosol extinction coefficient at 532 nm m-1 Aldehydes kg kg-1 Aldehydes kg kg-1 Amine kg kg-1 Amine kg kg-1 Ammonia kg kg-1 Ammonia kg kg-1 Ammonium kg kg-1 Ammonium kg kg-1 Ammonium aerosol mass mixing ratio kg kg-1 Ammonium aerosol mass mixing ratio kg kg-1 Ammonium aerosol optical depth at 550 nm dimensionless Ammonium aerosol optical depth at 550 nm dimensionless Anthropogenic secondary organic aerosol mass mixing ratio kg kg-1 Anthropogenic secondary organic aerosol mass mixing ratio kg kg-1 Asymmetric chlorine dioxide radical kg kg-1 Asymmetric chlorine dioxide radical kg kg-1 Asymmetry factor at 1020 nm dimensionless Asymmetry factor at 1020 nm dimensionless Asymmetry factor at 1064 nm dimensionless Asymmetry factor at 1064 nm dimensionless Asymmetry factor at 1240 nm dimensionless Asymmetry factor at 1240 nm dimensionless Asymmetry factor at 1640 nm dimensionless Asymmetry factor at 1640 nm dimensionless Asymmetry factor at 2130 nm dimensionless Asymmetry factor at 2130 nm dimensionless Asymmetry factor at 340 nm dimensionless Asymmetry factor at 340 nm dimensionless Asymmetry factor at 355 nm dimensionless Asymmetry factor at 355 nm dimensionless Asymmetry factor at 380 nm dimensionless Asymmetry factor at 380 nm dimensionless Asymmetry factor at 400 nm dimensionless Asymmetry factor at 400 nm dimensionless Asymmetry factor at 440 nm dimensionless Asymmetry factor at 440 nm dimensionless Asymmetry factor at 469 nm dimensionless Asymmetry factor at 469 nm dimensionless Asymmetry factor at 500 nm dimensionless Asymmetry factor at 500 nm dimensionless Asymmetry factor at 532 nm dimensionless Asymmetry factor at 532 nm dimensionless Asymmetry factor at 550 nm dimensionless Asymmetry factor at 550 nm dimensionless Asymmetry factor at 645 nm dimensionless Asymmetry factor at 645 nm dimensionless Asymmetry factor at 670 nm dimensionless Asymmetry factor at 670 nm dimensionless Asymmetry factor at 800 nm dimensionless Asymmetry factor at 800 nm dimensionless Asymmetry factor at 858 nm dimensionless Asymmetry factor at 858 nm dimensionless Asymmetry factor at 865 nm dimensionless Asymmetry factor at 865 nm dimensionless Attenuated backscatter due to aerosol at 1064 nm (from ground) m-1 sr-1 Attenuated backscatter due to aerosol at 1064 nm (from ground) m-1 sr-1 Attenuated backscatter due to aerosol at 1064 nm (from top of atmosphere) m-1 sr-1 Attenuated backscatter due to aerosol at 1064 nm (from top of atmosphere) m-1 sr-1 Attenuated backscatter due to aerosol at 355 nm (from ground) m-1 sr-1 Attenuated backscatter due to aerosol at 355 nm (from ground) m-1 sr-1 Attenuated backscatter due to aerosol at 355 nm (from top of atmosphere) m-1 sr-1 Attenuated backscatter due to aerosol at 355 nm (from top of atmosphere) m-1 sr-1 Attenuated backscatter due to aerosol at 532 nm (from ground) m-1 sr-1 Attenuated backscatter due to aerosol at 532 nm (from ground) m-1 sr-1 Attenuated backscatter due to aerosol at 532 nm (from top of atmosphere) m-1 sr-1 Attenuated backscatter due to aerosol at 532 nm (from top of atmosphere) m-1 sr-1 Biogenic secondary organic aerosol mass mixing ratio kg kg-1 Biogenic secondary organic aerosol mass mixing ratio kg kg-1 Black carbon aerosol optical depth at 550 nm dimensionless Black carbon aerosol optical depth at 550 nm dimensionless Boundary layer height m Boundary layer height m Bromine kg kg-1 Bromine kg kg-1 Bromine atom kg kg-1 Bromine atom kg kg-1 Bromine monochloride kg kg-1 Bromine monochloride kg kg-1 Bromine monoxide kg kg-1 Bromine monoxide kg kg-1 Bromine nitrate kg kg-1 Bromine nitrate kg kg-1 Bromochlorodifluoromethane kg kg-1 Bromochlorodifluoromethane kg kg-1 Carbon monoxide kg kg-1 Carbon monoxide kg kg-1 Chlorine kg kg-1 Chlorine kg kg-1 Chlorine atom kg kg-1 Chlorine atom kg kg-1 Chlorine dioxide kg kg-1 Chlorine dioxide kg kg-1 Chlorine monoxide kg kg-1 Chlorine monoxide kg kg-1 Chlorine nitrate kg kg-1 Chlorine nitrate kg kg-1 Chlorodifluoromethane kg kg-1 Chlorodifluoromethane kg kg-1 Chloropentafluoroethane kg kg-1 Chloropentafluoroethane kg kg-1 Clear sky surface photosynthetically active radiation J m-2 Clear sky surface photosynthetically active radiation J m-2 Clear-sky direct solar radiation at surface J m-2 Clear-sky direct solar radiation at surface J m-2 Cloud base height m Cloud base height m Convective available potential energy J kg-1 Convective available potential energy J kg-1 Convective inhibition J kg-1 Convective inhibition J kg-1 Convective precipitation m Convective precipitation m Dibromomethane kg kg-1 Dibromomethane kg kg-1 Dichlorine dioxide kg kg-1 Dichlorine dioxide kg kg-1 Dichlorodifluoromethane kg kg-1 Dichlorodifluoromethane kg kg-1 Dichlorotetrafluoroethane kg kg-1 Dichlorotetrafluoroethane kg kg-1 Dimethyl sulfide kg kg-1 Dimethyl sulfide kg kg-1 Dinitrogen pentoxide kg kg-1 Dinitrogen pentoxide kg kg-1 Direct solar radiation J m-2 Direct solar radiation J m-2 Downward UV radiation at the surface J m-2 Downward UV radiation at the surface J m-2 Dry deposition of ammonium aerosol kg m-2 s-1 Dry deposition of ammonium aerosol kg m-2 s-1 Dry deposition of coarse-mode nitrate aerosol kg m-2 s-1 Dry deposition of coarse-mode nitrate aerosol kg m-2 s-1 Dry deposition of dust aerosol (0.03 - 0.55 µm) kg m-2 s-1 Dry deposition of dust aerosol (0.03 - 0.55 µm) kg m-2 s-1 Dry deposition of dust aerosol (0.55 - 9 µm) kg m-2 s-1 Dry deposition of dust aerosol (0.55 - 9 µm) kg m-2 s-1 Dry deposition of dust aerosol (9 - 20 µm) kg m-2 s-1 Dry deposition of dust aerosol (9 - 20 µm) kg m-2 s-1 Dry deposition of fine-mode nitrate aerosol kg m-2 s-1 Dry deposition of fine-mode nitrate aerosol kg m-2 s-1 Dry deposition of hydrophilic black carbon aerosol kg m-2 s-1 Dry deposition of hydrophilic black carbon aerosol kg m-2 s-1 Dry deposition of hydrophilic organic matter aerosol kg m-2 s-1 Dry deposition of hydrophilic organic matter aerosol kg m-2 s-1 Dry deposition of hydrophobic black carbon aerosol kg m-2 s-1 Dry deposition of hydrophobic black carbon aerosol kg m-2 s-1 Dry deposition of hydrophobic organic matter aerosol kg m-2 s-1 Dry deposition of hydrophobic organic matter aerosol kg m-2 s-1 Dry deposition of sea salt aerosol (0.03 - 0.5 µm) kg m-2 s-1 Dry deposition of sea salt aerosol (0.03 - 0.5 µm) kg m-2 s-1 Dry deposition of sea salt aerosol (0.5 - 5 µm) kg m-2 s-1 Dry deposition of sea salt aerosol (0.5 - 5 µm) kg m-2 s-1 Dry deposition of sea salt aerosol (5 - 20 µm) kg m-2 s-1 Dry deposition of sea salt aerosol (5 - 20 µm) kg m-2 s-1 Dry deposition of sulphate aerosol kg m-2 s-1 Dry deposition of sulphate aerosol kg m-2 s-1 Dust aerosol (0.03 - 0.55 µm) mixing ratio kg kg-1 Dust aerosol (0.03 - 0.55 µm) mixing ratio kg kg-1 Dust aerosol (0.03 - 0.55 µm) optical depth at 550 nm dimensionless Dust aerosol (0.03 - 0.55 µm) optical depth at 550 nm dimensionless Dust aerosol (0.55 - 0.9 µm) mixing ratio kg kg-1 Dust aerosol (0.55 - 0.9 µm) mixing ratio kg kg-1 Dust aerosol (0.55 - 9 µm) optical depth at 550 nm dimensionless Dust aerosol (0.55 - 9 µm) optical depth at 550 nm dimensionless Dust aerosol (0.9 - 20 µm) mixing ratio kg kg-1 Dust aerosol (0.9 - 20 µm) mixing ratio kg kg-1 Dust aerosol (9 - 20 µm) optical depth at 550 nm dimensionless Dust aerosol (9 - 20 µm) optical depth at 550 nm dimensionless Dust aerosol optical depth at 550 nm dimensionless Dust aerosol optical depth at 550 nm dimensionless Ethane kg kg-1 Ethane kg kg-1 Ethanol kg kg-1 Ethanol kg kg-1 Ethene kg kg-1 Ethene kg kg-1 Evaporation m of water equivalent Evaporation m of water equivalent Forecast albedo (0 - 1) Forecast albedo (0 - 1) Formaldehyde kg kg-1 Formaldehyde kg kg-1 Formic acid kg kg-1 Formic acid kg kg-1 Fraction of cloud cover (0 - 1) Fraction of cloud cover (0 - 1) Friction velocity m s-1 Friction velocity m s-1 Geopotential m2 s-2 Geopotential m2 s-2 Glyoxal kg kg-1 Glyoxal kg kg-1 Height of convective cloud top m Height of convective cloud top m High cloud cover (0 - 1) High cloud cover (0 - 1) Hydrogen bromide kg kg-1 Hydrogen bromide kg kg-1 Hydrogen chloride kg kg-1 Hydrogen chloride kg kg-1 Hydrogen cyanide kg kg-1 Hydrogen cyanide kg kg-1 Hydrogen fluoride kg kg-1 Hydrogen fluoride kg kg-1 Hydrogen peroxide kg kg-1 Hydrogen peroxide kg kg-1 Hydroperoxy radical kg kg-1 Hydroperoxy radical kg kg-1 Hydrophilic black carbon aerosol mixing ratio kg kg-1 Hydrophilic black carbon aerosol mixing ratio kg kg-1 Hydrophilic black carbon aerosol optical depth at 550 nm dimensionless Hydrophilic black carbon aerosol optical depth at 550 nm dimensionless Hydrophilic organic matter aerosol mixing ratio kg kg-1 Hydrophilic organic matter aerosol mixing ratio kg kg-1 Hydrophilic organic matter aerosol optical depth at 550 nm dimensionless Hydrophilic organic matter aerosol optical depth at 550 nm dimensionless Hydrophobic black carbon aerosol mixing ratio kg kg-1 Hydrophobic black carbon aerosol mixing ratio kg kg-1 Hydrophobic black carbon aerosol optical depth at 550 nm dimensionless Hydrophobic black carbon aerosol optical depth at 550 nm dimensionless Hydrophobic organic matter aerosol mixing ratio kg kg-1 Hydrophobic organic matter aerosol mixing ratio kg kg-1 Hydrophobic organic matter aerosol optical depth at 550 nm dimensionless Hydrophobic organic matter aerosol optical depth at 550 nm dimensionless Hydroxyl radical kg kg-1 Hydroxyl radical kg kg-1 Hypobromous acid kg kg-1 Hypobromous acid kg kg-1 Hypochlorous acid kg kg-1 Hypochlorous acid kg kg-1 Isoprene kg kg-1 Isoprene kg kg-1 Lake cover (0 - 1) Lake cover (0 - 1) Land-sea mask (0 - 1) Land-sea mask (0 - 1) Large-scale precipitation m Large-scale precipitation m Lead kg kg-1 Lead kg kg-1 Leaf area index, high vegetation m2 m-2 Leaf area index, high vegetation m2 m-2 Leaf area index, low vegetation m2 m-2 Leaf area index, low vegetation m2 m-2 Logarithm of surface pressure ~ Logarithm of surface pressure ~ Low cloud cover (0 - 1) Low cloud cover (0 - 1) Mean sea level pressure Pa Mean sea level pressure Pa Medium cloud cover (0 - 1) Medium cloud cover (0 - 1) Methacrolein MVK kg kg-1 Methacrolein MVK kg kg-1 Methacrylic acid kg kg-1 Methacrylic acid kg kg-1 Methane kg kg-1 Methane kg kg-1 Methane sulfonic acid kg kg-1 Methane sulfonic acid kg kg-1 Methanol kg kg-1 Methanol kg kg-1 Methyl bromide kg kg-1 Methyl bromide kg kg-1 Methyl chloride kg kg-1 Methyl chloride kg kg-1 Methyl chloroform kg kg-1 Methyl chloroform kg kg-1 Methyl glyoxal kg kg-1 Methyl glyoxal kg kg-1 Methyl peroxide kg kg-1 Methyl peroxide kg kg-1 Methylperoxy radical kg kg-1 Methylperoxy radical kg kg-1 Nitrate kg kg-1 Nitrate kg kg-1 Nitrate aerosol optical depth at 550 nm dimensionless Nitrate aerosol optical depth at 550 nm dimensionless Nitrate coarse mode aerosol mass mixing ratio kg kg-1 Nitrate coarse mode aerosol mass mixing ratio kg kg-1 Nitrate coarse-mode aerosol optical depth at 550 nm dimensionless Nitrate coarse-mode aerosol optical depth at 550 nm dimensionless Nitrate fine mode aerosol mass mixing ratio kg kg-1 Nitrate fine mode aerosol mass mixing ratio kg kg-1 Nitrate fine-mode aerosol optical depth at 550 nm dimensionless Nitrate fine-mode aerosol optical depth at 550 nm dimensionless Nitrate radical kg kg-1 Nitrate radical kg kg-1 Nitric acid kg kg-1 Nitric acid kg kg-1 Nitrogen dioxide kg kg-1 Nitrogen dioxide kg kg-1 Nitrogen monoxide kg kg-1 Nitrogen monoxide kg kg-1 Nitryl chloride kg kg-1 Nitryl chloride kg kg-1 Olefins kg kg-1 Olefins kg kg-1 Organic ethers kg kg-1 Organic ethers kg kg-1 Organic matter aerosol optical depth at 550 nm dimensionless Organic matter aerosol optical depth at 550 nm dimensionless Organic nitrates kg kg-1 Organic nitrates kg kg-1 Ozone kg kg-1 Ozone kg kg-1 Paraffins kg kg-1 Paraffins kg kg-1 Particulate matter d < 1 µm (PM1) kg m-3 Particulate matter d < 1 µm (PM1) kg m-3 Particulate matter d < 10 µm (PM10) kg m-3 Particulate matter d < 10 µm (PM10) kg m-3 Particulate matter d < 2.5 µm (PM2.5) kg m-3 Particulate matter d < 2.5 µm (PM2.5) kg m-3 Pernitric acid kg kg-1 Pernitric acid kg kg-1 Peroxides kg kg-1 Peroxides kg kg-1 Peroxy acetyl radical kg kg-1 Peroxy acetyl radical kg kg-1 Peroxyacetyl nitrate kg kg-1 Peroxyacetyl nitrate kg kg-1 Photosynthetically active radiation at the surface J m-2 Photosynthetically active radiation at the surface J m-2 Potential evaporation m Potential evaporation m Potential vorticity K m2 kg-1 s-1 Potential vorticity K m2 kg-1 s-1 Precipitation type dimensionless Precipitation type dimensionless Propane kg kg-1 Propane kg kg-1 Propene kg kg-1 Propene kg kg-1 Radon kg kg-1 Radon kg kg-1 Relative humidity % Relative humidity % Sea salt aerosol (0.03 - 0.5 µm) mixing ratio kg kg-1 Sea salt aerosol (0.03 - 0.5 µm) mixing ratio kg kg-1 Sea salt aerosol (0.03 - 0.5 µm) optical depth at 550 nm dimensionless Sea salt aerosol (0.03 - 0.5 µm) optical depth at 550 nm dimensionless Sea salt aerosol (0.5 - 5 µm) mixing ratio kg kg-1 Sea salt aerosol (0.5 - 5 µm) mixing ratio kg kg-1 Sea salt aerosol (0.5 - 5 µm) optical depth at 550 nm dimensionless Sea salt aerosol (0.5 - 5 µm) optical depth at 550 nm dimensionless Sea salt aerosol (5 - 20 µm) mixing ratio kg kg-1 Sea salt aerosol (5 - 20 µm) mixing ratio kg kg-1 Sea salt aerosol (5 - 20 µm) optical depth at 550 nm dimensionless Sea salt aerosol (5 - 20 µm) optical depth at 550 nm dimensionless Sea salt aerosol optical depth at 550 nm dimensionless Sea salt aerosol optical depth at 550 nm dimensionless Sea surface temperature K Sea surface temperature K Sea-ice cover (0 - 1) Sea-ice cover (0 - 1) Secondary organic aerosol optical depth at 550 nm dimensionless Secondary organic aerosol optical depth at 550 nm dimensionless Sedimentation of ammonium aerosol kg m-2 s-1 Sedimentation of ammonium aerosol kg m-2 s-1 Sedimentation of coarse-mode nitrate aerosol kg m-2 s-1 Sedimentation of coarse-mode nitrate aerosol kg m-2 s-1 Sedimentation of dust aerosol (0.03 - 0.55 µm) kg m-2 s-1 Sedimentation of dust aerosol (0.03 - 0.55 µm) kg m-2 s-1 Sedimentation of dust aerosol (0.55 - 9 µm) kg m-2 s-1 Sedimentation of dust aerosol (0.55 - 9 µm) kg m-2 s-1 Sedimentation of dust aerosol (9 - 20 µm) kg m-2 s-1 Sedimentation of dust aerosol (9 - 20 µm) kg m-2 s-1 Sedimentation of fine-mode nitrate aerosol kg m-2 s-1 Sedimentation of fine-mode nitrate aerosol kg m-2 s-1 Sedimentation of hydrophilic black carbon aerosol kg m-2 s-1 Sedimentation of hydrophilic black carbon aerosol kg m-2 s-1 Sedimentation of hydrophilic organic matter aerosol kg m-2 s-1 Sedimentation of hydrophilic organic matter aerosol kg m-2 s-1 Sedimentation of hydrophobic black carbon aerosol kg m-2 s-1 Sedimentation of hydrophobic black carbon aerosol kg m-2 s-1 Sedimentation of hydrophobic organic matter aerosol kg m-2 s-1 Sedimentation of hydrophobic organic matter aerosol kg m-2 s-1 Sedimentation of sea salt aerosol (0.03 - 0.5 µm) kg m-2 s-1 Sedimentation of sea salt aerosol (0.03 - 0.5 µm) kg m-2 s-1 Sedimentation of sea salt aerosol (0.5 - 5 µm) kg m-2 s-1 Sedimentation of sea salt aerosol (0.5 - 5 µm) kg m-2 s-1 Sedimentation of sea salt aerosol (5 - 20 µm) kg m-2 s-1 Sedimentation of sea salt aerosol (5 - 20 µm) kg m-2 s-1 Sedimentation of sulphate aerosol kg m-2 s-1 Sedimentation of sulphate aerosol kg m-2 s-1 Single scattering albedo at 1020 nm (0 - 1) Single scattering albedo at 1020 nm (0 - 1) Single scattering albedo at 1064 nm (0 - 1) Single scattering albedo at 1064 nm (0 - 1) Single scattering albedo at 1240 nm (0 - 1) Single scattering albedo at 1240 nm (0 - 1) Single scattering albedo at 1640 nm (0 - 1) Single scattering albedo at 1640 nm (0 - 1) Single scattering albedo at 2130 nm (0 - 1) Single scattering albedo at 2130 nm (0 - 1) Single scattering albedo at 340 nm (0 - 1) Single scattering albedo at 340 nm (0 - 1) Single scattering albedo at 355 nm (0 - 1) Single scattering albedo at 355 nm (0 - 1) Single scattering albedo at 380 nm (0 - 1) Single scattering albedo at 380 nm (0 - 1) Single scattering albedo at 400 nm (0 - 1) Single scattering albedo at 400 nm (0 - 1) Single scattering albedo at 440 nm (0 - 1) Single scattering albedo at 440 nm (0 - 1) Single scattering albedo at 469 nm (0 - 1) Single scattering albedo at 469 nm (0 - 1) Single scattering albedo at 500 nm (0 - 1) Single scattering albedo at 500 nm (0 - 1) Single scattering albedo at 532 nm (0 - 1) Single scattering albedo at 532 nm (0 - 1) Single scattering albedo at 550 nm (0 - 1) Single scattering albedo at 550 nm (0 - 1) Single scattering albedo at 645 nm (0 - 1) Single scattering albedo at 645 nm (0 - 1) Single scattering albedo at 670 nm (0 - 1) Single scattering albedo at 670 nm (0 - 1) Single scattering albedo at 800 nm (0 - 1) Single scattering albedo at 800 nm (0 - 1) Single scattering albedo at 858 nm (0 - 1) Single scattering albedo at 858 nm (0 - 1) Single scattering albedo at 865 nm (0 - 1) Single scattering albedo at 865 nm (0 - 1) Skin reservoir content m of water equivalent Skin reservoir content m of water equivalent Skin temperature K Skin temperature K Snow albedo (0 - 1) Snow albedo (0 - 1) Snow depth m of water equivalent Snow depth m of water equivalent Source/gain of ammonium aerosol kg m-2 s-1 Source/gain of ammonium aerosol kg m-2 s-1 Source/gain of coarse-mode nitrate aerosol kg m-2 s-1 Source/gain of coarse-mode nitrate aerosol kg m-2 s-1 Source/gain of dust aerosol (0.03 - 0.55 µm) kg m-2 s-1 Source/gain of dust aerosol (0.03 - 0.55 µm) kg m-2 s-1 Source/gain of dust aerosol (0.55 - 9 µm) kg m-2 s-1 Source/gain of dust aerosol (0.55 - 9 µm) kg m-2 s-1 Source/gain of dust aerosol (9 - 20 µm) kg m-2 s-1 Source/gain of dust aerosol (9 - 20 µm) kg m-2 s-1 Source/gain of fine-mode nitrate aerosol kg m-2 s-1 Source/gain of fine-mode nitrate aerosol kg m-2 s-1 Source/gain of hydrophilic black carbon aerosol kg m-2 s-1 Source/gain of hydrophilic black carbon aerosol kg m-2 s-1 Source/gain of hydrophilic organic matter aerosol kg m-2 s-1 Source/gain of hydrophilic organic matter aerosol kg m-2 s-1 Source/gain of hydrophobic black carbon aerosol kg m-2 s-1 Source/gain of hydrophobic black carbon aerosol kg m-2 s-1 Source/gain of hydrophobic organic matter aerosol kg m-2 s-1 Source/gain of hydrophobic organic matter aerosol kg m-2 s-1 Source/gain of sea salt aerosol (0.03 - 0.5 µm) kg m-2 s-1 Source/gain of sea salt aerosol (0.03 - 0.5 µm) kg m-2 s-1 Source/gain of sea salt aerosol (0.5 - 5 µm) kg m-2 s-1 Source/gain of sea salt aerosol (0.5 - 5 µm) kg m-2 s-1 Source/gain of sea salt aerosol (5 - 20 µm) kg m-2 s-1 Source/gain of sea salt aerosol (5 - 20 µm) kg m-2 s-1 Source/gain of sulphate aerosol kg m-2 s-1 Source/gain of sulphate aerosol kg m-2 s-1 Specific cloud ice water content kg kg-1 Specific cloud ice water content kg kg-1 Specific cloud liquid water content kg kg-1 Specific cloud liquid water content kg kg-1 Specific humidity kg kg-1 Specific humidity kg kg-1 Specific rain water content kg kg-1 Specific rain water content kg kg-1 Specific snow water content kg kg-1 Specific snow water content kg kg-1 Stratospheric ozone tracer kg kg-1 Stratospheric ozone tracer kg kg-1 Sulphate aerosol mixing ratio kg kg-1 Sulphate aerosol mixing ratio kg kg-1 Sulphate aerosol optical depth at 550 nm dimensionless Sulphate aerosol optical depth at 550 nm dimensionless Sulphur dioxide kg kg-1 Sulphur dioxide kg kg-1 Sunshine duration s Sunshine duration s Surface Geopotential m2 s-2 Surface Geopotential m2 s-2 Surface latent heat flux J m-2 Surface latent heat flux J m-2 Surface net solar radiation J m-2 Surface net solar radiation J m-2 Surface net solar radiation, clear sky J m-2 Surface net solar radiation, clear sky J m-2 Surface net thermal radiation J m-2 Surface net thermal radiation J m-2 Surface net thermal radiation, clear sky J m-2 Surface net thermal radiation, clear sky J m-2 Surface pressure Pa Surface pressure Pa Surface sensible heat flux J m-2 Surface sensible heat flux J m-2 Surface solar radiation downward, clear sky J m-2 Surface solar radiation downward, clear sky J m-2 Surface solar radiation downwards J m-2 Surface solar radiation downwards J m-2 Surface thermal radiation downward, clear sky J m-2 Surface thermal radiation downward, clear sky J m-2 Surface thermal radiation downwards J m-2 Surface thermal radiation downwards J m-2 TOA incident solar radiation J m-2 TOA incident solar radiation J m-2 Temperature K Temperature K Terpenes kg kg-1 Terpenes kg kg-1 Tetrachloromethane kg kg-1 Tetrachloromethane kg kg-1 Time-integrated dry deposition mass flux of ammonia kg m**-2 Time-integrated dry deposition mass flux of ammonia kg m**-2 Time-integrated dry deposition mass flux of dinitrogen pentoxide kg m**-2 Time-integrated dry deposition mass flux of dinitrogen pentoxide kg m**-2 Time-integrated dry deposition mass flux of nitric acid kg m**-2 Time-integrated dry deposition mass flux of nitric acid kg m**-2 Time-integrated dry deposition mass flux of nitrogen dioxide kg m**-2 Time-integrated dry deposition mass flux of nitrogen dioxide kg m**-2 Time-integrated dry deposition mass flux of nitrogen monoxide kg m**-2 Time-integrated dry deposition mass flux of nitrogen monoxide kg m**-2 Time-integrated dry deposition mass flux of organic nitrates kg m**-2 Time-integrated dry deposition mass flux of organic nitrates kg m**-2 Time-integrated dry deposition mass flux of ozone kg m**-2 Time-integrated dry deposition mass flux of ozone kg m**-2 Time-integrated dry deposition mass flux of peroxyacetyl nitrate kg m**-2 Time-integrated dry deposition mass flux of peroxyacetyl nitrate kg m**-2 Time-integrated dry deposition mass flux of sulphur dioxide kg m**-2 Time-integrated dry deposition mass flux of sulphur dioxide kg m**-2 Time-integrated wet deposition mass flux of ammonia kg m**-2 Time-integrated wet deposition mass flux of ammonia kg m**-2 Time-integrated wet deposition mass flux of dinitrogen pentoxide kg m**-2 Time-integrated wet deposition mass flux of dinitrogen pentoxide kg m**-2 Time-integrated wet deposition mass flux of nitric acid kg m**-2 Time-integrated wet deposition mass flux of nitric acid kg m**-2 Time-integrated wet deposition mass flux of nitrogen dioxide kg m**-2 Time-integrated wet deposition mass flux of nitrogen dioxide kg m**-2 Time-integrated wet deposition mass flux of nitrogen monoxide kg m**-2 Time-integrated wet deposition mass flux of nitrogen monoxide kg m**-2 Time-integrated wet deposition mass flux of organic nitrates kg m**-2 Time-integrated wet deposition mass flux of organic nitrates kg m**-2 Time-integrated wet deposition mass flux of peroxyacetyl nitrate kg m**-2 Time-integrated wet deposition mass flux of peroxyacetyl nitrate kg m**-2 Time-integrated wet deposition mass flux of sulphur dioxide kg m**-2 Time-integrated wet deposition mass flux of sulphur dioxide kg m**-2 Top net solar radiation J m-2 Top net solar radiation J m-2 Top net solar radiation, clear sky J m-2 Top net solar radiation, clear sky J m-2 Top net thermal radiation J m-2 Top net thermal radiation J m-2 Top net thermal radiation, clear sky J m-2 Top net thermal radiation, clear sky J m-2 Total absorption aerosol optical depth at 1020 nm dimensionless Total absorption aerosol optical depth at 1020 nm dimensionless Total absorption aerosol optical depth at 1064 nm dimensionless Total absorption aerosol optical depth at 1064 nm dimensionless Total absorption aerosol optical depth at 1240 nm dimensionless Total absorption aerosol optical depth at 1240 nm dimensionless Total absorption aerosol optical depth at 1640 nm dimensionless Total absorption aerosol optical depth at 1640 nm dimensionless Total absorption aerosol optical depth at 2130 nm dimensionless Total absorption aerosol optical depth at 2130 nm dimensionless Total absorption aerosol optical depth at 340 nm dimensionless Total absorption aerosol optical depth at 340 nm dimensionless Total absorption aerosol optical depth at 355 nm dimensionless Total absorption aerosol optical depth at 355 nm dimensionless Total absorption aerosol optical depth at 380 nm dimensionless Total absorption aerosol optical depth at 380 nm dimensionless Total absorption aerosol optical depth at 400 nm dimensionless Total absorption aerosol optical depth at 400 nm dimensionless Total absorption aerosol optical depth at 440 nm dimensionless Total absorption aerosol optical depth at 440 nm dimensionless Total absorption aerosol optical depth at 469 nm dimensionless Total absorption aerosol optical depth at 469 nm dimensionless Total absorption aerosol optical depth at 500 nm dimensionless Total absorption aerosol optical depth at 500 nm dimensionless Total absorption aerosol optical depth at 532 nm dimensionless Total absorption aerosol optical depth at 532 nm dimensionless Total absorption aerosol optical depth at 550 nm dimensionless Total absorption aerosol optical depth at 550 nm dimensionless Total absorption aerosol optical depth at 645 nm dimensionless Total absorption aerosol optical depth at 645 nm dimensionless Total absorption aerosol optical depth at 670 nm dimensionless Total absorption aerosol optical depth at 670 nm dimensionless Total absorption aerosol optical depth at 800 nm dimensionless Total absorption aerosol optical depth at 800 nm dimensionless Total absorption aerosol optical depth at 858 nm dimensionless Total absorption aerosol optical depth at 858 nm dimensionless Total absorption aerosol optical depth at 865 nm dimensionless Total absorption aerosol optical depth at 865 nm dimensionless Total aerosol optical depth at 1020 nm dimensionless Total aerosol optical depth at 1020 nm dimensionless Total aerosol optical depth at 1064 nm dimensionless Total aerosol optical depth at 1064 nm dimensionless Total aerosol optical depth at 1240 nm dimensionless Total aerosol optical depth at 1240 nm dimensionless Total aerosol optical depth at 1640 nm dimensionless Total aerosol optical depth at 1640 nm dimensionless Total aerosol optical depth at 2130 nm dimensionless Total aerosol optical depth at 2130 nm dimensionless Total aerosol optical depth at 340 nm dimensionless Total aerosol optical depth at 340 nm dimensionless Total aerosol optical depth at 355 nm dimensionless Total aerosol optical depth at 355 nm dimensionless Total aerosol optical depth at 380 nm dimensionless Total aerosol optical depth at 380 nm dimensionless Total aerosol optical depth at 400 nm dimensionless Total aerosol optical depth at 400 nm dimensionless Total aerosol optical depth at 440 nm dimensionless Total aerosol optical depth at 440 nm dimensionless Total aerosol optical depth at 469 nm dimensionless Total aerosol optical depth at 469 nm dimensionless Total aerosol optical depth at 500 nm dimensionless Total aerosol optical depth at 500 nm dimensionless Total aerosol optical depth at 532 nm dimensionless Total aerosol optical depth at 532 nm dimensionless Total aerosol optical depth at 550 nm dimensionless Total aerosol optical depth at 550 nm dimensionless Total aerosol optical depth at 645 nm dimensionless Total aerosol optical depth at 645 nm dimensionless Total aerosol optical depth at 670 nm dimensionless Total aerosol optical depth at 670 nm dimensionless Total aerosol optical depth at 800 nm dimensionless Total aerosol optical depth at 800 nm dimensionless Total aerosol optical depth at 858 nm dimensionless Total aerosol optical depth at 858 nm dimensionless Total aerosol optical depth at 865 nm dimensionless Total aerosol optical depth at 865 nm dimensionless Total cloud cover (0 - 1) Total cloud cover (0 - 1) Total column HYPROPO2 kg m-2 Total column HYPROPO2 kg m-2 Total column IC3H7O2 kg m-2 Total column IC3H7O2 kg m-2 Total column NO to NO2 operator kg m-2 Total column NO to NO2 operator kg m-2 Total column NO to alkyl nitrate operator kg m-2 Total column NO to alkyl nitrate operator kg m-2 Total column acetone kg m-2 Total column acetone kg m-2 Total column acetone product kg m-2 Total column acetone product kg m-2 Total column acetonitrile kg m-2 Total column acetonitrile kg m-2 Total column aldehydes kg m-2 Total column aldehydes kg m-2 Total column amine kg m-2 Total column amine kg m-2 Total column ammonia kg m-2 Total column ammonia kg m-2 Total column ammonium kg m-2 Total column ammonium kg m-2 Total column asymmetric chlorine dioxide radical kg m-2 Total column asymmetric chlorine dioxide radical kg m-2 Total column bromine kg m-2 Total column bromine kg m-2 Total column bromine atom kg m-2 Total column bromine atom kg m-2 Total column bromine monochloride kg m-2 Total column bromine monochloride kg m-2 Total column bromine monoxide kg m-2 Total column bromine monoxide kg m-2 Total column bromine nitrate kg m-2 Total column bromine nitrate kg m-2 Total column bromochlorodifluoromethane kg m-2 Total column bromochlorodifluoromethane kg m-2 Total column carbon monoxide kg m-2 Total column carbon monoxide kg m-2 Total column chlorine kg m-2 Total column chlorine kg m-2 Total column chlorine atom kg m-2 Total column chlorine atom kg m-2 Total column chlorine dioxide kg m-2 Total column chlorine dioxide kg m-2 Total column chlorine monoxide kg m-2 Total column chlorine monoxide kg m-2 Total column chlorine nitrate kg m-2 Total column chlorine nitrate kg m-2 Total column chlorodifluoromethane kg m-2 Total column chlorodifluoromethane kg m-2 Total column chloropentafluoroethane kg m-2 Total column chloropentafluoroethane kg m-2 Total column cloud ice water kg m-2 Total column cloud ice water kg m-2 Total column cloud liquid water kg m-2 Total column cloud liquid water kg m-2 Total column dibromomethane kg m-2 Total column dibromomethane kg m-2 Total column dichlorine dioxide kg m-2 Total column dichlorine dioxide kg m-2 Total column dichlorodifluoromethane kg m-2 Total column dichlorodifluoromethane kg m-2 Total column dichlorotetrafluoroethane kg m-2 Total column dichlorotetrafluoroethane kg m-2 Total column dimethyl sulfide kg m-2 Total column dimethyl sulfide kg m-2 Total column dinitrogen pentoxide kg m-2 Total column dinitrogen pentoxide kg m-2 Total column ethane kg m-2 Total column ethane kg m-2 Total column ethanol kg m-2 Total column ethanol kg m-2 Total column ethene kg m-2 Total column ethene kg m-2 Total column formaldehyde kg m-2 Total column formaldehyde kg m-2 Total column formic acid kg m-2 Total column formic acid kg m-2 Total column glyoxal kg m-2 Total column glyoxal kg m-2 Total column hydrogen bromide kg m-2 Total column hydrogen bromide kg m-2 Total column hydrogen chloride kg m-2 Total column hydrogen chloride kg m-2 Total column hydrogen cyanide kg m-2 Total column hydrogen cyanide kg m-2 Total column hydrogen fluoride kg m-2 Total column hydrogen fluoride kg m-2 Total column hydrogen peroxide kg m-2 Total column hydrogen peroxide kg m-2 Total column hydroperoxy radical kg m-2 Total column hydroperoxy radical kg m-2 Total column hydroxyl radical kg m-2 Total column hydroxyl radical kg m-2 Total column hypobromous acid kg m-2 Total column hypobromous acid kg m-2 Total column hypochlorous acid kg m-2 Total column hypochlorous acid kg m-2 Total column isoprene kg m-2 Total column isoprene kg m-2 Total column lead kg m-2 Total column lead kg m-2 Total column methacrolein MVK kg m-2 Total column methacrolein MVK kg m-2 Total column methacrylic acid kg m-2 Total column methacrylic acid kg m-2 Total column methane kg m-2 Total column methane kg m-2 Total column methane sulfonic acid kg m-2 Total column methane sulfonic acid kg m-2 Total column methanol kg m-2 Total column methanol kg m-2 Total column methyl bromide kg m-2 Total column methyl bromide kg m-2 Total column methyl chloride kg m-2 Total column methyl chloride kg m-2 Total column methyl chloroform kg m-2 Total column methyl chloroform kg m-2 Total column methyl glyoxal kg m-2 Total column methyl glyoxal kg m-2 Total column methyl peroxide kg m-2 Total column methyl peroxide kg m-2 Total column methylperoxy radical kg m-2 Total column methylperoxy radical kg m-2 Total column nitrate kg m-2 Total column nitrate kg m-2 Total column nitrate radical kg m-2 Total column nitrate radical kg m-2 Total column nitric acid kg m-2 Total column nitric acid kg m-2 Total column nitrogen dioxide kg m-2 Total column nitrogen dioxide kg m-2 Total column nitrogen monoxide kg m-2 Total column nitrogen monoxide kg m-2 Total column nitrogen oxides transp kg m-2 Total column nitrogen oxides transp kg m-2 Total column nitryl chloride kg m-2 Total column nitryl chloride kg m-2 Total column olefins kg m-2 Total column olefins kg m-2 Total column organic ethers kg m-2 Total column organic ethers kg m-2 Total column organic nitrates kg m-2 Total column organic nitrates kg m-2 Total column ozone kg m-2 Total column ozone kg m-2 Total column paraffins kg m-2 Total column paraffins kg m-2 Total column pernitric acid kg m-2 Total column pernitric acid kg m-2 Total column peroxides kg m-2 Total column peroxides kg m-2 Total column peroxy acetyl radical kg m-2 Total column peroxy acetyl radical kg m-2 Total column peroxyacetyl nitrate kg m-2 Total column peroxyacetyl nitrate kg m-2 Total column polar stratospheric cloud kg m-2 Total column polar stratospheric cloud kg m-2 Total column propane kg m-2 Total column propane kg m-2 Total column propene kg m-2 Total column propene kg m-2 Total column radon kg m-2 Total column radon kg m-2 Total column rain water kg m-2 Total column rain water kg m-2 Total column snow water kg m-2 Total column snow water kg m-2 Total column stratospheric ozone kg m-2 Total column stratospheric ozone kg m-2 Total column sulphur dioxide kg m-2 Total column sulphur dioxide kg m-2 Total column supercooled liquid water kg m-2 Total column supercooled liquid water kg m-2 Total column terpenes kg m-2 Total column terpenes kg m-2 Total column tetrachloromethane kg m-2 Total column tetrachloromethane kg m-2 Total column tribromomethane kg m-2 Total column tribromomethane kg m-2 Total column trichlorofluoromethane kg m-2 Total column trichlorofluoromethane kg m-2 Total column trichlorotrifluoroethane kg m-2 Total column trichlorotrifluoroethane kg m-2 Total column trifluorobromomethane kg m-2 Total column trifluorobromomethane kg m-2 Total column water kg m-2 Total column water kg m-2 Total column water vapour kg m-2 Total column water vapour kg m-2 Total column water vapour (chemistry) kg m-2 Total column water vapour (chemistry) kg m-2 Total fine mode (r < 0.5 µm) aerosol optical depth at 1020 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 1020 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 1064 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 1064 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 1240 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 1240 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 1640 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 1640 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 2130 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 2130 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 340 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 340 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 355 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 355 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 380 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 380 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 400 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 400 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 440 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 440 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 469 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 469 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 500 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 500 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 532 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 532 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 550 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 550 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 645 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 645 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 670 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 670 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 800 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 800 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 858 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 858 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 865 nm dimensionless Total fine mode (r < 0.5 µm) aerosol optical depth at 865 nm dimensionless Total precipitation m Total precipitation m Total sky direct solar radiation at surface J m-2 Total sky direct solar radiation at surface J m-2 Tribromomethane kg kg-1 Tribromomethane kg kg-1 Trichlorofluoromethane kg kg-1 Trichlorofluoromethane kg kg-1 Trichlorotrifluoroethane kg kg-1 Trichlorotrifluoroethane kg kg-1 Trifluorobromomethane kg kg-1 Trifluorobromomethane kg kg-1 U-component of wind m s-1 U-component of wind m s-1 UV biologically effective dose W m-2 UV biologically effective dose W m-2 UV biologically effective dose, clear sky W m-2 UV biologically effective dose, clear sky W m-2 V-component of wind m s-1 V-component of wind m s-1 Vertical velocity Pa s-1 Vertical velocity Pa s-1 Vertically integrated mass of ammonium aerosol kg m-2 Vertically integrated mass of ammonium aerosol kg m-2 Vertically integrated mass of coarse-mode nitrate aerosol kg m-2 Vertically integrated mass of coarse-mode nitrate aerosol kg m-2 Vertically integrated mass of dust aerosol (0.03 - 0.55 µm) kg m-2 Vertically integrated mass of dust aerosol (0.03 - 0.55 µm) kg m-2 Vertically integrated mass of dust aerosol (0.55 - 9 µm) kg m-2 Vertically integrated mass of dust aerosol (0.55 - 9 µm) kg m-2 Vertically integrated mass of dust aerosol (9 - 20 µm) kg m-2 Vertically integrated mass of dust aerosol (9 - 20 µm) kg m-2 Vertically integrated mass of fine-mode nitrate aerosol kg m-2 Vertically integrated mass of fine-mode nitrate aerosol kg m-2 Vertically integrated mass of hydrophilic black carbon aerosol kg m-2 Vertically integrated mass of hydrophilic black carbon aerosol kg m-2 Vertically integrated mass of hydrophilic organic matter aerosol kg m-2 Vertically integrated mass of hydrophilic organic matter aerosol kg m-2 Vertically integrated mass of hydrophobic black carbon aerosol kg m-2 Vertically integrated mass of hydrophobic black carbon aerosol kg m-2 Vertically integrated mass of hydrophobic organic matter aerosol kg m-2 Vertically integrated mass of hydrophobic organic matter aerosol kg m-2 Vertically integrated mass of sea salt aerosol (0.03 - 0.5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (0.03 - 0.5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (0.5 - 5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (0.5 - 5 µm) kg m-2 Vertically integrated mass of sea salt aerosol (5 - 20 µm) kg m-2 Vertically integrated mass of sea salt aerosol (5 - 20 µm) kg m-2 Vertically integrated mass of sulphate aerosol kg m-2 Vertically integrated mass of sulphate aerosol kg m-2 Vertically integrated moisture divergence kg m-2 Vertically integrated moisture divergence kg m-2 Visibility km Visibility km Water vapour (chemistry) kg kg-1 Water vapour (chemistry) kg kg-1 Wet deposition of ammonium aerosol by convective precipitation kg m-2 s-1 Wet deposition of ammonium aerosol by convective precipitation kg m-2 s-1 Wet deposition of ammonium aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of ammonium aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of coarse-mode nitrate aerosol by convective precipitation kg m-2 s-1 Wet deposition of coarse-mode nitrate aerosol by convective precipitation kg m-2 s-1 Wet deposition of coarse-mode nitrate aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of coarse-mode nitrate aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of dust aerosol (0.03 - 0.55 µm) by convective precipitation kg m-2 s-1 Wet deposition of dust aerosol (0.03 - 0.55 µm) by convective precipitation kg m-2 s-1 Wet deposition of dust aerosol (0.03 - 0.55 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of dust aerosol (0.03 - 0.55 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of dust aerosol (0.55 - 9 µm) by convective precipitation kg m-2 s-1 Wet deposition of dust aerosol (0.55 - 9 µm) by convective precipitation kg m-2 s-1 Wet deposition of dust aerosol (0.55 - 9 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of dust aerosol (0.55 - 9 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of dust aerosol (9 - 20 µm) by convective precipitation kg m-2 s-1 Wet deposition of dust aerosol (9 - 20 µm) by convective precipitation kg m-2 s-1 Wet deposition of dust aerosol (9 - 20 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of dust aerosol (9 - 20 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of fine-mode nitrate aerosol by convective precipitation kg m-2 s-1 Wet deposition of fine-mode nitrate aerosol by convective precipitation kg m-2 s-1 Wet deposition of fine-mode nitrate aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of fine-mode nitrate aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of hydrophilic black carbon aerosol by convective precipitation kg m-2 s-1 Wet deposition of hydrophilic black carbon aerosol by convective precipitation kg m-2 s-1 Wet deposition of hydrophilic black carbon aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of hydrophilic black carbon aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of hydrophilic organic matter aerosol by convective precipitation kg m-2 s-1 Wet deposition of hydrophilic organic matter aerosol by convective precipitation kg m-2 s-1 Wet deposition of hydrophilic organic matter aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of hydrophilic organic matter aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of hydrophobic black carbon aerosol by convective precipitation kg m-2 s-1 Wet deposition of hydrophobic black carbon aerosol by convective precipitation kg m-2 s-1 Wet deposition of hydrophobic black carbon aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of hydrophobic black carbon aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of hydrophobic organic matter aerosol by convective precipitation kg m-2 s-1 Wet deposition of hydrophobic organic matter aerosol by convective precipitation kg m-2 s-1 Wet deposition of hydrophobic organic matter aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of hydrophobic organic matter aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (0.03 - 0.5 µm) by convective precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (0.03 - 0.5 µm) by convective precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (0.03 - 0.5 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (0.03 - 0.5 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (0.5 - 5 µm) by convective precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (0.5 - 5 µm) by convective precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (0.5 - 5 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (0.5 - 5 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (5 - 20 µm) by convective precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (5 - 20 µm) by convective precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (5 - 20 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of sea salt aerosol (5 - 20 µm) by large-scale precipitation kg m-2 s-1 Wet deposition of sulphate aerosol by convective precipitation kg m-2 s-1 Wet deposition of sulphate aerosol by convective precipitation kg m-2 s-1 Wet deposition of sulphate aerosol by large-scale precipitation kg m-2 s-1 Wet deposition of sulphate aerosol by large-scale precipitation kg m-2 s-1 697 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/european-ground-motion-service-ortho-east-west-component https://egms.land.copernicus.eu/ European Ground Motion Service: Ortho – East-West Component 2015-2021 (vector), Europe, yearly, Feb. 2023 The European Ground Motion Service (EGMS) is a component of the Copernicus Land Monitoring Service. EGMS provides consistent, regular, standardised, harmonised and reliable information regarding natural and anthropogenic ground motion phenomena over the Copernicus Participating States and across national borders, with millimetre accuracy. This set of metadata describes the third product level of EGMS: Ortho. This EGMS Ortho product exploits the information provided by ascending and descending orbits of the Calibrated product (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/be…) to derive two further layers; one of purely vertical displacements, the other of purely east-west displacements (the one described by this metadata). Both layers are resampled to a 100 m grid. The Ortho product eases the interpretation process of non-experts since the viewing geometry has not to be considered anymore. EGMS Ortho is visualised as a vector map of measurement points colour-coded by average velocity (vertical or east-west components) and distributed to users in comma-separated values format. Each point is associated with a time series of displacement, i.e. a plot with values of displacement per acquisition of the satellite. The product covers the Copernicus Participating States and United Kingdom. https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/be… 698 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/insitu-observations-gnss https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-observations-gnss insitu-observations-gnss This dataset provides estimates of water vapour derived from atmospheric delays in Global Navigation Satellite System (GNSS) radio signals. The initial data is collected from two in situ ground-based network of GNSS receivers – the International GNSS Service (IGS) and EUREF Permanent Network (EPN). The IGS collects, archives, and freely distributes GNSS data from a cooperatively operated global network of more than 500 ground-based GNSS stations since 1994. The EPN is a European network of more than 300 continuously operating GNSS reference stations with precisely known coordinates. The fundamental observable of ground-based GNSS is the Zenith Total Delay (ZTD). This observable represents the propagation delay induced by the atmosphere on GNSS signals between the GNSS satellites and a GNSS receiver. Using in-situ measurements of ZTD as well as ancillary meteorological data at the site, the vertically-integrated total amount if water vapour in the air column can be retrieved. This integrated quantity is referred to as Total Column Water Vapour (TCWV), or Total Precipitable Water (TPW), or Integrated Precipitable Water (IPW) or also Integrated Water Vapour (IWV). The TCWV values, with corresponding uncertainties, are based on the daily product of IGS and the 2nd reprocessing campaign of the EPN (EPN-repro2). Both networks provide hourly records of ZTD as part of the products derived from GNSS data processing. The GNSS TCWV derived from these ZTD data is a new product, made available exclusively for the Climate Data Store (CDS). The data user must consider that while the EPN-repro2 consists of tropospheric parameters derived from consistently reprocessed data spanning multiple decades, the IGS daily product is a near real-time product which may contain jumps in the time-series when a new processing model is introduced. Therefore, IGS daily ZTD and TCWV should not be considered as reference products. The method for retrieval of GNSS TCWV and details on the algorithms can be found in the documentation. For comparison, TCWV values from the ECMWF ERA5 reanalysis are also made available for the user. The datasets can be downloaded as comma-separated values (CSV) files organized in two different ways: One row per report: for each report all variables are provided as individual columns in one row (referenced as csv-lev in the variable lists below). One row per observable: (no aggregation) each row provides information about one variable only (referenced as csv-obs in the variable lists below). One row per report: for each report all variables are provided as individual columns in one row (referenced as csv-lev in the variable lists below). One row per observable: (no aggregation) each row provides information about one variable only (referenced as csv-obs in the variable lists below). DATA DESCRIPTION Data type Point data Horizontal coverage Global Temporal coverage From 1996; each station has its own start date Temporal resolution Hourly File format CSV Versions v1 Update frequency Monthly for International GNSS service, none for EUREF permanent network DATA DESCRIPTION DATA DESCRIPTION Data type Point data Data type Point data Horizontal coverage Global Horizontal coverage Global Temporal coverage From 1996; each station has its own start date Temporal coverage From 1996; each station has its own start date Temporal resolution Hourly Temporal resolution Hourly File format CSV File format CSV Versions v1 Versions v1 Update frequency Monthly for International GNSS service, none for EUREF permanent network Update frequency Monthly for International GNSS service, none for EUREF permanent network MAIN VARIABLES Name Units Description Total column water vapour kg m-2 This variable is the total amount of water vapour in a column extending vertically from the GNSS receiver position (near the surface) to the top of the atmosphere. Total column water vapour combined uncertainty kg m-2 This variable is the combined sum of all uncertainties in the total column water vapour derived from zenith total delay and ancillary meteorological data. The uncertainties that are included in the calculation include uncertainties of the observed zenith total delay, uncertainties of the ancillary data, and uncertainties of the coefficients used in the retrieval (csv-lev only). Total column water vapour era5 kg m-2 This variable is the total amount of water vapour in a column extending vertically from the GNSS receiver position (near the surface) to the top of the atmosphere, retrieved from ERA5 at the station coordinates, altitude, date, and time (csv-lev only). Zenith total delay m This variable characterizes the delay of the GNSS signal on the path from a satellite to the receiver due to atmospheric refraction and bending, mapped into the zenith direction. It is expressed as an equivalent distance travelled additionally by the radio waves, due to the atmosphere. The numerical value of zenith total delay correlates with the amount of total column water vapour (i.e., not including effects of liquid water and/or ice) above the GNSS receiver antenna. It is hence used to estimate the total column water vapour. Zenith total delay random uncertainty m This variable is an estimate of the standard uncertainty equivalent to 1-sigma uncertainty of zenith total delay (csv-lev only). MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Total column water vapour kg m-2 This variable is the total amount of water vapour in a column extending vertically from the GNSS receiver position (near the surface) to the top of the atmosphere. Total column water vapour kg m-2 This variable is the total amount of water vapour in a column extending vertically from the GNSS receiver position (near the surface) to the top of the atmosphere. Total column water vapour combined uncertainty kg m-2 This variable is the combined sum of all uncertainties in the total column water vapour derived from zenith total delay and ancillary meteorological data. The uncertainties that are included in the calculation include uncertainties of the observed zenith total delay, uncertainties of the ancillary data, and uncertainties of the coefficients used in the retrieval (csv-lev only). Total column water vapour combined uncertainty kg m-2 This variable is the combined sum of all uncertainties in the total column water vapour derived from zenith total delay and ancillary meteorological data. The uncertainties that are included in the calculation include uncertainties of the observed zenith total delay, uncertainties of the ancillary data, and uncertainties of the coefficients used in the retrieval (csv-lev only). Total column water vapour era5 kg m-2 This variable is the total amount of water vapour in a column extending vertically from the GNSS receiver position (near the surface) to the top of the atmosphere, retrieved from ERA5 at the station coordinates, altitude, date, and time (csv-lev only). Total column water vapour era5 kg m-2 This variable is the total amount of water vapour in a column extending vertically from the GNSS receiver position (near the surface) to the top of the atmosphere, retrieved from ERA5 at the station coordinates, altitude, date, and time (csv-lev only). Zenith total delay m This variable characterizes the delay of the GNSS signal on the path from a satellite to the receiver due to atmospheric refraction and bending, mapped into the zenith direction. It is expressed as an equivalent distance travelled additionally by the radio waves, due to the atmosphere. The numerical value of zenith total delay correlates with the amount of total column water vapour (i.e., not including effects of liquid water and/or ice) above the GNSS receiver antenna. It is hence used to estimate the total column water vapour. Zenith total delay m This variable characterizes the delay of the GNSS signal on the path from a satellite to the receiver due to atmospheric refraction and bending, mapped into the zenith direction. It is expressed as an equivalent distance travelled additionally by the radio waves, due to the atmosphere. The numerical value of zenith total delay correlates with the amount of total column water vapour (i.e., not including effects of liquid water and/or ice) above the GNSS receiver antenna. It is hence used to estimate the total column water vapour. Zenith total delay random uncertainty m This variable is an estimate of the standard uncertainty equivalent to 1-sigma uncertainty of zenith total delay (csv-lev only). Zenith total delay random uncertainty m This variable is an estimate of the standard uncertainty equivalent to 1-sigma uncertainty of zenith total delay (csv-lev only). RELATED VARIABLES Name Units Description City None This variable is the name of the location of the GNSS receiver. This name is provided by the SEMISYS database (see Citation). Combined uncertainty kg m-2 This variable is the combined sum of all uncertainties for the measurand. It is only available for the total column water vapour derived from observed zenith total delay and ancillary meteorological data (csv-obs only). Era5 kg m-2 A value 'era5' for this variable indicates that the measurand is retrieved from ERA5 at the station coordinates, altitude, date, and time. It is only available for total column water vapour (csv-obs only). Height of station above sea level m This variable is the altitude of the GNSS receiving station above the mean sea-level. Latitude degree_north This variable is the latitude of the GNSS receiving station. Longitude degree_east This variable is the longitude of the GNSS receiving station. Observation value According to the unit as specified in the main variables table Measurement value of the variable in question (csv-obs only). Observed variable None Specification of the measurand (csv-obs only). Organisation name None This variable indicates the agency responsible for the station. Random uncertainty m This variable is an estimate of the standard uncertainty equivalent to 1-sigma uncertainty of the measurand. It is only available for zenith total delay (csv-obs only). Report id None This variable starts from 1 for the first data report provided in the data file, and is incremented for each new report. Report timestamp None This variable is the date and time (UTC) associated with the observation. Sensor altitude m This variable is the difference between the GNSS antenna height and the World Geodetic System (WGS)-84 ellipsoid. The WGS-84 is a static reference, maintained by the United States National Geospatial-Intelligence Agency. It is also the reference coordinate system used by the GPS. Start date None This variable is the first date and time of data available at the GNSS station. Station name None This variable indicates the name of the GNSS receiving station. RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description City None This variable is the name of the location of the GNSS receiver. This name is provided by the SEMISYS database (see Citation). City None This variable is the name of the location of the GNSS receiver. This name is provided by the SEMISYS database (see Citation). Combined uncertainty kg m-2 This variable is the combined sum of all uncertainties for the measurand. It is only available for the total column water vapour derived from observed zenith total delay and ancillary meteorological data (csv-obs only). Combined uncertainty kg m-2 This variable is the combined sum of all uncertainties for the measurand. It is only available for the total column water vapour derived from observed zenith total delay and ancillary meteorological data (csv-obs only). Era5 kg m-2 A value 'era5' for this variable indicates that the measurand is retrieved from ERA5 at the station coordinates, altitude, date, and time. It is only available for total column water vapour (csv-obs only). Era5 kg m-2 A value 'era5' for this variable indicates that the measurand is retrieved from ERA5 at the station coordinates, altitude, date, and time. It is only available for total column water vapour (csv-obs only). Height of station above sea level m This variable is the altitude of the GNSS receiving station above the mean sea-level. Height of station above sea level m This variable is the altitude of the GNSS receiving station above the mean sea-level. Latitude degree_north This variable is the latitude of the GNSS receiving station. Latitude degree_north This variable is the latitude of the GNSS receiving station. Longitude degree_east This variable is the longitude of the GNSS receiving station. Longitude degree_east This variable is the longitude of the GNSS receiving station. Observation value According to the unit as specified in the main variables table Measurement value of the variable in question (csv-obs only). Observation value According to the unit as specified in the main variables table Measurement value of the variable in question (csv-obs only). Observed variable None Specification of the measurand (csv-obs only). Observed variable None Specification of the measurand (csv-obs only). Organisation name None This variable indicates the agency responsible for the station. Organisation name None This variable indicates the agency responsible for the station. Random uncertainty m This variable is an estimate of the standard uncertainty equivalent to 1-sigma uncertainty of the measurand. It is only available for zenith total delay (csv-obs only). Random uncertainty m This variable is an estimate of the standard uncertainty equivalent to 1-sigma uncertainty of the measurand. It is only available for zenith total delay (csv-obs only). Report id None This variable starts from 1 for the first data report provided in the data file, and is incremented for each new report. Report id None This variable starts from 1 for the first data report provided in the data file, and is incremented for each new report. Report timestamp None This variable is the date and time (UTC) associated with the observation. Report timestamp None This variable is the date and time (UTC) associated with the observation. Sensor altitude m This variable is the difference between the GNSS antenna height and the World Geodetic System (WGS)-84 ellipsoid. The WGS-84 is a static reference, maintained by the United States National Geospatial-Intelligence Agency. It is also the reference coordinate system used by the GPS. Sensor altitude m This variable is the difference between the GNSS antenna height and the World Geodetic System (WGS)-84 ellipsoid. The WGS-84 is a static reference, maintained by the United States National Geospatial-Intelligence Agency. It is also the reference coordinate system used by the GPS. Start date None This variable is the first date and time of data available at the GNSS station. Start date None This variable is the first date and time of data available at the GNSS station. Station name None This variable indicates the name of the GNSS receiving station. Station name None This variable indicates the name of the GNSS receiving station. 699 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-fireweatherindex-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-fireweatherindex-overview-web hidden-app-fireweatherindex-overview-web This application has been published in a hidden state for contractual purposes. It has not gone through the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 700 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-firedangerindex-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-firedangerindex-overview-web hidden-app-firedangerindex-overview-web This application has been published in a hidden state for contractual purposes. It has not gone through the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 701 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-fireweatherindex-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-fireweatherindex-detail-web hidden-app-fireweatherindex-detail-web This application has been published in a hidden state for contractual purposes. It has not gone through the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 702 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-agriculture-precip-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-agriculture-precip-detail-web hidden-app-agriculture-precip-detail-web This application has been published in a hidden state for contractual purposes. It has not gone through the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 703 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-firedangerindex-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-firedangerindex-detail-web hidden-app-firedangerindex-detail-web This application has been published in a hidden state for contractual purposes. It has not gone through the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 704 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-agriculture-precip-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-agriculture-precip-overview-web hidden-app-agriculture-precip-overview-web This application has been published in a hidden state for contractual purposes. It has not gone through the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 705 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/satellite-ozone https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-ozone satellite-ozone This dataset provides estimates of the montly mean values of the ozone concentration, mixing ration and mole content over the globe from a large set of satellite sensors. Most of the ozone data products in this dataset have been developed as part of the ESA Ozone Climate Change Initiative project. They represent the current state-of-the-art in Europe for satellite-based ozone climate data record production, in line with the “Systematic observation requirements for satellite-based products for climate” as defined by GCOS (Global Climate Observing System). concentration mixing ration mole content The dataset is organised around the vertical aggregation of the ozone data in four main products: Ozone total column retrieval from UV-nadir sensors; Ozone total and tropospheric column retrieval from IASI sensors; Ozone profile retrieval from UV-nadir sensors; Ozone profile retrieval from limb and occultation sensors. Ozone total column retrieval from UV-nadir sensors; Ozone total and tropospheric column retrieval from IASI sensors; Ozone profile retrieval from UV-nadir sensors; Ozone profile retrieval from limb and occultation sensors. When dealing with satellite data it is common to encounter references to processing levels which describes the amount of processing applied to the raw data, in this case, Level-3 and Level-4. Level-3 means that data are on a regular latitude/longitude expectedly with gaps in space and time. Level-4 data was futher reprocessed in order to fill any eventual gaps in the dataset. Another common reference is to Climate Data Recors (CDR) and interim-CDR (ICDR). For this dataset, both the ICDR and CDR parts of each product were generated using the same software and algorithms. The CDR is intended to have sufficient length, consistency, and continuity to detect climate variability and change. This is the case for instance with the ozone vertically integrated values computed from the passive remote-sensing UV spectrometry onboard of nadir sensors such as SBUV, TOMS, GOME, SCIAMACHY or OMI. The ICDR provides a short-delay access to current data where consistency with the CDR baseline is expected but was not extensively checked. The user is invited to read the documentation in order to determine for each product which are the time spans of the CDR and ICDR parts. In addition to the vertical aggregation of ozone data, this dataset also provides horizonal aggregations in the form of zonal averages and sensor aggreagations in the from of merged products. For some products, in addition to the main variable, auxiliary variables like air-temperature and air-pressure at different levels are provided as well. DATA DESCRIPTION Data type Gridded Horizontal coverage Global Horizontal resolution 1°x1° (10° for zonal averages) Vertical resolution Profiles and total column data depending on the product Temporal coverage 1995 to present, but shorter for some sensors Temporal resolution Monthly File format NetCDF-4 (respecting GHRSST 2.0 data specifications) Update frequency Mostly annually with four months delay DATA DESCRIPTION DATA DESCRIPTION Data type Gridded Data type Gridded Horizontal coverage Global Horizontal coverage Global Horizontal resolution 1°x1° (10° for zonal averages) Horizontal resolution 1°x1° (10° for zonal averages) Vertical resolution Profiles and total column data depending on the product Vertical resolution Profiles and total column data depending on the product Temporal coverage 1995 to present, but shorter for some sensors Temporal coverage 1995 to present, but shorter for some sensors Temporal resolution Monthly Temporal resolution Monthly File format NetCDF-4 (respecting GHRSST 2.0 data specifications) File format NetCDF-4 (respecting GHRSST 2.0 data specifications) Update frequency Mostly annually with four months delay Update frequency Mostly annually with four months delay MAIN VARIABLES Name Units Description Ozone concentration mol m-3 Number of moles of ozone divided by the total volume of the air-ozone mixture Ozone concentration anomaly % Percent deviation with respect to the reference monthly concentration value. If C(t) is the monthly ozone concentration at month t, and M(t) is the reference ozone concentration for the same month (derived by averaging all measurements for month t), then the percent anomaly is given by 100*(C(t)-M(t))/M(t) Ozone mixing ratio ppmv Fraction of the ozone volume to the total volume of the air-ozone mixture Ozone mole content mol m-2 (Dobson for some products) Vertical average of the ozone amount MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Ozone concentration mol m-3 Number of moles of ozone divided by the total volume of the air-ozone mixture Ozone concentration mol m-3 Number of moles of ozone divided by the total volume of the air-ozone mixture Ozone concentration anomaly % Percent deviation with respect to the reference monthly concentration value. If C(t) is the monthly ozone concentration at month t, and M(t) is the reference ozone concentration for the same month (derived by averaging all measurements for month t), then the percent anomaly is given by 100*(C(t)-M(t))/M(t) Ozone concentration anomaly % Percent deviation with respect to the reference monthly concentration value. If C(t) is the monthly ozone concentration at month t, and M(t) is the reference ozone concentration for the same month (derived by averaging all measurements for month t), then the percent anomaly is given by 100*(C(t)-M(t))/M(t) Ozone mixing ratio ppmv Fraction of the ozone volume to the total volume of the air-ozone mixture Ozone mixing ratio ppmv Fraction of the ozone volume to the total volume of the air-ozone mixture Ozone mole content mol m-2 (Dobson for some products) Vertical average of the ozone amount Ozone mole content mol m-2 (Dobson for some products) Vertical average of the ozone amount 706 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-3d-chlorophyll-concentration-particulate http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MULTIOBS_GLO_BIO_BGC_3D_REP_015_010 Global Ocean 3D Chlorophyll-a concentration, Particulate Backscattering coefficient and Particulate Organic Carbon Short description: This product consists of 3D fields of Particulate Organic Carbon (POC), Particulate Backscattering coefficient (bbp) and Chlorophyll-a concentration (Chla) at depth. The reprocessed product is provided at 0.25°x0.25° horizontal resolution, over 36 levels from the surface to 1000 m depth. A neural network method estimates both the vertical distribution of Chla concentration and of particulate backscattering coefficient (bbp), a bio-optical proxy for POC, from merged surface ocean color satellite measurements with hydrological properties and additional relevant drivers. DOI (product):https://doi.org/10.48670/moi-00046 https://doi.org/10.48670/moi-00046 Product Citation: Please refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag…. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… 707 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/tree-cover-density-2012-raster-100-m-europe-3-yearly-mar https://land.copernicus.eu/pan-european/high-resolution-layers/forests/tree-cover-density/status-maps/2012/view Tree Cover Density 2012 (raster 100 m), Europe, 3-yearly, Mar. 2018 The high resolution forest product consists of three types of (status) products and additional change products. The status products are available for the 2012 and 2015 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes). The production of the high resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 708 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/forest-type-2012-raster-100-m-europe-3-yearly-apr-2018 https://land.copernicus.eu/pan-european/high-resolution-layers/forests/forest-type-1/status-maps/2012/view Forest Type 2012 (raster 100 m), Europe, 3-yearly, Apr. 2018 The high resolution forest product consists of three types of (status) products and additional change products. The status products are available for the 2012 and 2015 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes). The production of the high resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 709 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-tourism-snow-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-tourism-snow-detail-web hidden-app-tourism-snow-detail-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 710 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-agriculture-tmin-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-agriculture-tmin-overview-web hidden-app-agriculture-tmin-overview-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 711 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-agriculture-tropicalnights-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-agriculture-tropicalnights-detail-web hidden-app-agriculture-tropicalnights-detail-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 712 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-agriculture-bedd-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-agriculture-bedd-detail-web hidden-app-agriculture-bedd-detail-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 713 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-marine-sealevel-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-marine-sealevel-overview-web hidden-app-marine-sealevel-overview-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 714 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-meanradianttemperature-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-meanradianttemperature-overview-web hidden-app-meanradianttemperature-overview-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 715 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-marine-sealevel-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-marine-sealevel-detail-web hidden-app-marine-sealevel-detail-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 716 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-agriculture-tropicalnights-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-agriculture-tropicalnights-overview-web hidden-app-agriculture-tropicalnights-overview-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 717 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-agriculture-frostdays-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-agriculture-frostdays-overview-web hidden-app-agriculture-frostdays-overview-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 718 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-tourism-snow-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-tourism-snow-overview-web hidden-app-tourism-snow-overview-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 719 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-meanradianttemperature-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-meanradianttemperature-detail-web hidden-app-meanradianttemperature-detail-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 720 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-agriculture-frostdays-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-agriculture-frostdays-detail-web hidden-app-agriculture-frostdays-detail-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 721 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-health-heatwave-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-health-heatwave-detail-web hidden-app-health-heatwave-detail-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 722 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-agriculture-tmax-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-agriculture-tmax-detail-web hidden-app-agriculture-tmax-detail-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 723 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-health-heatwave-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-health-heatwave-overview-web hidden-app-health-heatwave-overview-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 724 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-agriculture-tmax-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-agriculture-tmax-overview-web hidden-app-agriculture-tmax-overview-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 725 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-universalthermalclimateindex-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-universalthermalclimateindex-overview-web hidden-app-universalthermalclimateindex-overview-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 726 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-universalthermalclimateindex-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-universalthermalclimateindex-detail-web hidden-app-universalthermalclimateindex-detail-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 727 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-agriculture-tmin-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-agriculture-tmin-detail-web hidden-app-agriculture-tmin-detail-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 728 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-agriculture-bedd-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-agriculture-bedd-overview-web hidden-app-agriculture-bedd-overview-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 729 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-agriculture-tmean-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-agriculture-tmean-detail-web hidden-app-agriculture-tmean-detail-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 730 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-health-mosquito-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-health-mosquito-detail-web hidden-app-health-mosquito-detail-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 731 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-agriculture-tmax-detail https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-agriculture-tmax-detail hidden-app-agriculture-tmax-detail This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 732 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-agriculture-tmean-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-agriculture-tmean-overview-web hidden-app-agriculture-tmean-overview-web This application has been published in a hidden state for contractual purposes. It has not gone thorugh the usual editorial publication process hence the results are not quality assured. More details about the products are given in the Documentation section. 733 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/forest-type-2012-raster-20-m-europe-3-yearly-apr-2018 https://land.copernicus.eu/pan-european/high-resolution-layers/forests/forest-type-1/status-maps/2012/view Forest Type 2012 (raster 20 m), Europe, 3-yearly, Apr. 2018 The high resolution forest product consists of three types of (status) products and additional change products. The status products are available for the 2012, 2015 and 2018 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes). The production of the high resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 734 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/dominant-leaf-type-2015-raster-20-m-europe-3-yearly-apr https://land.copernicus.eu/pan-european/high-resolution-layers/forests/dominant-leaf-type/status-maps/2015/view Dominant Leaf Type 2015 (raster 20 m), Europe, 3-yearly, Apr. 2018 The high resolution forest product consists of three types of (status) products and additional change products. The status products are available for the 2012, 2015 and 2018 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes). The production of the high resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 735 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/tree-cover-density-2015-raster-20-m-europe-3-yearly-mar https://land.copernicus.eu/pan-european/high-resolution-layers/forests/tree-cover-density/status-maps/2015/view Tree Cover Density 2015 (raster 20 m), Europe, 3-yearly, Mar. 2018 The high resolution forest product consists of three types of (status) products and additional change products. The status products are available for the 2012, 2015 and 2018 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes). The production of the high resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 736 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/forest-type-2015-raster-20-m-europe-3-yearly-apr-2018 https://land.copernicus.eu/pan-european/high-resolution-layers/forests/forest-type-1/status-maps/2015/view Forest Type 2015 (raster 20 m), Europe, 3-yearly, Apr. 2018 The high resolution forest product consists of three types of (status) products and additional change products. The status products are available for the 2012, 2015 and 2018 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes). The production of the high resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 737 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/forest-type-2015-raster-100-m-europe-3-yearly-apr-2018 https://land.copernicus.eu/pan-european/high-resolution-layers/forests/forest-type-1/status-maps/2015/view Forest Type 2015 (raster 100 m), Europe, 3-yearly, Apr. 2018 The high resolution forest product consists of three types of (status) products and additional change products. The status products are available for the 2012, 2015 and 2018 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes). The production of the high resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 738 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/tree-cover-density-2015-raster-100-m-europe-3-yearly-mar https://land.copernicus.eu/pan-european/high-resolution-layers/forests/tree-cover-density/status-maps/2015/view Tree Cover Density 2015 (raster 100 m), Europe, 3-yearly, Mar. 2018 The high resolution forest product consists of three types of (status) products and additional change products. The status products are available for the 2012, 2015 and 2018 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes). The production of the high resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 739 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/tree-cover-density-2012-raster-20-m-europe-3-yearly-mar https://land.copernicus.eu/pan-european/high-resolution-layers/forests/tree-cover-density/status-maps/2012/view Tree Cover Density 2012 (raster 20 m), Europe, 3-yearly, Mar. 2018 The high resolution forest product consists of three types of (status) products and additional change products. The status products are available for the 2012, 2015 and 2018 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes). The production of the high resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 740 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/dominant-leaf-type-2012-raster-20-m-europe-3-yearly-apr https://land.copernicus.eu/pan-european/high-resolution-layers/forests/dominant-leaf-type/status-maps/2012/view Dominant Leaf Type 2012 (raster 20 m), Europe, 3-yearly, Apr. 2018 The high resolution forest product consists of three types of (status) products and additional change products. The status products are available for the 2012, 2015 and 2018 reference years: 1. Tree cover density providing level of tree cover density in a range from 0-100%; 2. Dominant leaf type providing information on the dominant leaf type: broadleaved or coniferous; 3. A Forest type product. The forest type product allows to get as close as possible to the FAO forest definition. In its original (20m) resolution it consists of two products: 1) a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps, based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and high resolution imperviousness 2009 data). For the final 100m product trees under agricultural use and urban context from the support layer are removed. The high resolution forest change products comprise a simple tree cover density change product for 2012-2015 (% increase or decrease of real tree cover density changes). The production of the high resolution forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme. 741 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/soil-water-index-2007-present-raster-125-km-global-daily http://web.vgt.vito.be/download_g2.php?file=&path=http%3A//geoland2.meteo.pt/g2system/operations/products/SWI/ Soil Water Index 2007-present (raster 12.5 km), global, daily - version 3 The Soil Water index (SWI) product provides global daily information about moisture conditions in different soil layers. SWI daily images are produced from EUMETSAT ASCAT-25km SSM product in orbit format and include a quality flag indicating the availability of SSM measurements for SWI calculations. Soil moisture is a key parameter in numerous environmental studies including hydrology, meteorology and agriculture. In addition to Surface Soil Moisture (SSM), information on the moisture condition within the underlying soil profile is of interest for different applications. Soil moisture in plant root zone can be estimated by an infiltration model using information on surface soil moisture and soil characteristics. 742 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/arctic-ocean-high-resolution-sea-ice-concentration-and http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_ARC_PHY_AUTO_L4_NRT_011_015 Arctic Ocean - High resolution Sea Ice Concentration and Sea Ice Type Short description: For the European Arctic Sea - A sea ice concentration product based on SAR data and microwave radiometer. The algorithm uses SENTINEL-1 SAR EW mode dual-polarized HH/HV data combined with AMSR2 radiometer data. A sea ice type product covering the same area is produced from SENTINEL-1 SAR EW mode dual-polarized HH/HV data. DOI (product) :https://doi.org/10.48670/moi-00122 https://doi.org/10.48670/moi-00122 743 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/viewer-sis-fisheries-abundance https://cds.climate.copernicus.eu/cdsapp#!/dataset/viewer-sis-fisheries-abundance viewer-sis-fisheries-abundance Viewer application for dataset More details about the products are given in the Documentation section. INPUT VARIABLES Name Units Description DUMMY dummy DUMMY INPUT VARIABLES INPUT VARIABLES Name Units Description Name Units Description DUMMY dummy DUMMY DUMMY dummy DUMMY 744 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/viewer-satellite-surface-radiation-budget https://cds.climate.copernicus.eu/cdsapp#!/dataset/viewer-satellite-surface-radiation-budget viewer-satellite-surface-radiation-budget Viewer application for dataset More details about the products are given in the Documentation section. INPUT VARIABLES Name Units Description DUMMY dummy DUMMY INPUT VARIABLES INPUT VARIABLES Name Units Description Name Units Description DUMMY dummy DUMMY DUMMY dummy DUMMY 745 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/viewer-satellite-lake-water-level https://cds.climate.copernicus.eu/cdsapp#!/dataset/viewer-satellite-lake-water-level viewer-satellite-lake-water-level Viewer application for dataset More details about the products are given in the Documentation section. INPUT VARIABLES Name Units Description DUMMY dummy DUMMY INPUT VARIABLES INPUT VARIABLES Name Units Description Name Units Description DUMMY dummy DUMMY DUMMY dummy DUMMY 746 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/viewer-satellite-aerosol-properties https://cds.climate.copernicus.eu/cdsapp#!/dataset/viewer-satellite-aerosol-properties viewer-satellite-aerosol-properties Viewer application for dataset More details about the products are given in the Documentation section. INPUT VARIABLES Name Units Description DUMMY dummy DUMMY INPUT VARIABLES INPUT VARIABLES Name Units Description Name Units Description DUMMY dummy DUMMY DUMMY dummy DUMMY 747 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/gross-dry-matter-productivity-1999-2020-raster-1-km https://land.copernicus.eu/global/products/dmp Gross Dry Matter Productivity 1999-2020 (raster 1 km), global, 10-daily - version 2 Gross dry matter Productivity (GDMP) is an indication of the overall growth rate or dry biomass increase of the vegetation and is directly related to ecosystem Gross Primary Productivity (GPP), that reflects the ecosystem's overall production of organic compounds from atmospheric carbon dioxide, however its units (kilograms of gross dry matter per hectare per day) are customized for agro-statistical purposes. Like the FAPAR products that are used as input for the GDMP estimation, these GDMP products are provided in Near Real Time, with consolidations in the next six periods, or as offline product. 748 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/insitu-observations-woudc-ozone-total-column-and-profiles https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-observations-woudc-ozone-total-column-and-profiles insitu-observations-woudc-ozone-total-column-and-profiles This dataset provides access to two types of ozone observations: total column ozone estimates and vertical profiles of ozone concentration. The total ozone estimates are based on solar UV radiation measurements made by ground-based spectrophotometers (Dobson or Brewer type spectrophotometers). The vertical profiles of ozone concentration are estimated primarily using ozonesonde observations. Data are available for 159 Dobson stations, 109 Brewer stations and 135 ozonesondes stations. The ozone data are included in the quadrennial Scientific Assessments of Ozone Depletion as part of the “Vienna convention for the protection of the ozone layer” and are used extensively for a range of studies and applications. The data provided here are collated by the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) which is part of the Global Atmosphere Watch programme of the World Meteorological Organization. WOUDC was established in 1960 with the objective to collect, quality-control, archive and provide long-term access to high quality observation data and metadata from WMO Global Atmosphere Watch (GAW) network of stations measuring ozone column and ozone vertical profiles. Furthermore, the data record provides sustainable high-quality data under all application areas via coordinated activities between the WOUDC and regional and global calibration and data quality centers. The total column ozone and ozonesonde can be downloaded as comma-separated values (CSV) files organised in two different ways: CSV one row per level: for each observed height all variables are provided as individual columns in one row. CSV one row per observation: (no aggregation) each row provides information about one variable at one level only. All main variable are grouped together in the following columns: one for the observed variable (indicates variable in question), one for observation value. CSV one row per level: for each observed height all variables are provided as individual columns in one row. CSV one row per observation: (no aggregation) each row provides information about one variable at one level only. All main variable are grouped together in the following columns: one for the observed variable (indicates variable in question), one for observation value. This dataset is collated by WOUDC and guided by the WMO Ozone Scientific Advisory Group in Ozone and Solar UV Radiation. More details about the dataset are given in the Documentation section, and general information and background can be obtained from the WOUDC website. WOUDC website DATA DESCRIPTION Data type Point data Horizontal coverage Global (403 stations) Vertical coverage Ozone profiles cover a mandatory range of 1000 to 10 hPa (some profiles may contain additional levels outside of this range). Total column ozone observations represent the entire atmospheric column. Vertical resolution Ozone profiles are provided on 16 mandatory levels (10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000 hPa) plus significant levels which vary per profile. Total column ozone observations are provide as a single level. Temporal coverage Total column ozone observations available since 1924 to present (start date depends on the chosen station) Ozone profiles starting since 1962 to present, start date depends on station (start date depends on the chosen station) Temporal resolution Sub-daily File format CSV Versions Current version - 1 DATA DESCRIPTION DATA DESCRIPTION Data type Point data Data type Point data Horizontal coverage Global (403 stations) Horizontal coverage Global (403 stations) Vertical coverage Ozone profiles cover a mandatory range of 1000 to 10 hPa (some profiles may contain additional levels outside of this range). Total column ozone observations represent the entire atmospheric column. Vertical coverage Ozone profiles cover a mandatory range of 1000 to 10 hPa (some profiles may contain additional levels outside of this range). Total column ozone observations represent the entire atmospheric column. Ozone profiles cover a mandatory range of 1000 to 10 hPa (some profiles may contain additional levels outside of this range). Total column ozone observations represent the entire atmospheric column. Vertical resolution Ozone profiles are provided on 16 mandatory levels (10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000 hPa) plus significant levels which vary per profile. Total column ozone observations are provide as a single level. Vertical resolution Ozone profiles are provided on 16 mandatory levels (10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000 hPa) plus significant levels which vary per profile. Total column ozone observations are provide as a single level. Ozone profiles are provided on 16 mandatory levels (10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000 hPa) plus significant levels which vary per profile. Total column ozone observations are provide as a single level. Temporal coverage Total column ozone observations available since 1924 to present (start date depends on the chosen station) Ozone profiles starting since 1962 to present, start date depends on station (start date depends on the chosen station) Temporal coverage Total column ozone observations available since 1924 to present (start date depends on the chosen station) Ozone profiles starting since 1962 to present, start date depends on station (start date depends on the chosen station) Total column ozone observations available since 1924 to present (start date depends on the chosen station) Ozone profiles starting since 1962 to present, start date depends on station (start date depends on the chosen station) Temporal resolution Sub-daily Temporal resolution Sub-daily File format CSV File format CSV Versions Current version - 1 Versions Current version - 1 MAIN VARIABLES Name Units Description Air temperature K Level air temperature. Column sulphur dioxide Dobson-units The daily total column sulphur dioxide (SO2) amount calculated as the mean of the individual SO2 amounts from the same observation used for the O3 amount. Geopotential height m Geopotential height in meters. Ozone partial pressure Pa Level partial pressure of ozone in Pascals. Relative humidity % Percentage of water vapour relative to the saturation amount. Total ozone column Dobson-units Daily value of total column ozone amount defined as the 'best representative value' in order of Direct Sun (DS), Zenith Cloud (ZS) and Focused Moon (FM). Total ozone column standard deviation Dimensionless Estimated population standard deviation of the total column ozone measurements used for the daily value. Wind from direction ° The direction, relative to the geographic location of the north pole, that the wind is coming from, e.g. 0° means "coming from the north" and 90° means "coming from the east". Wind speed m s-1 Wind speed in meters per second. MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Air temperature K Level air temperature. Air temperature K Level air temperature. Column sulphur dioxide Dobson-units The daily total column sulphur dioxide (SO2) amount calculated as the mean of the individual SO2 amounts from the same observation used for the O3 amount. Column sulphur dioxide Dobson-units The daily total column sulphur dioxide (SO2) amount calculated as the mean of the individual SO2 amounts from the same observation used for the O3 amount. Geopotential height m Geopotential height in meters. Geopotential height m Geopotential height in meters. Ozone partial pressure Pa Level partial pressure of ozone in Pascals. Ozone partial pressure Pa Level partial pressure of ozone in Pascals. Relative humidity % Percentage of water vapour relative to the saturation amount. Relative humidity % Percentage of water vapour relative to the saturation amount. Total ozone column Dobson-units Daily value of total column ozone amount defined as the 'best representative value' in order of Direct Sun (DS), Zenith Cloud (ZS) and Focused Moon (FM). Total ozone column Dobson-units Daily value of total column ozone amount defined as the 'best representative value' in order of Direct Sun (DS), Zenith Cloud (ZS) and Focused Moon (FM). Total ozone column standard deviation Dimensionless Estimated population standard deviation of the total column ozone measurements used for the daily value. Total ozone column standard deviation Dimensionless Estimated population standard deviation of the total column ozone measurements used for the daily value. Wind from direction ° The direction, relative to the geographic location of the north pole, that the wind is coming from, e.g. 0° means "coming from the north" and 90° means "coming from the east". Wind from direction ° The direction, relative to the geographic location of the north pole, that the wind is coming from, e.g. 0° means "coming from the north" and 90° means "coming from the east". Wind speed m s-1 Wind speed in meters per second. Wind speed m s-1 Wind speed in meters per second. RELATED VARIABLES The csv files contain columns for a range of auxiliary variables which describe the context of the observation(s), for example the time and location of the observation and/or station and the type of station and instrumentation used. Here we provide a list, and description, of the columns that may be provided in the downloaded csv files. Name Units Description Air pressure Pa Atmospheric pressure of each level in Pascals. Daily timestamp YYYY-MM-DD hh:mm:ss+00 Date of the observations. Harmonic mean relative slant path Dimensionless The harmonic mean of the relative slant path through the ozone layer at 22Km for each of the observations used to compute the daily value. Height of station above sea level meters above sea level Height is defined as the altitude, elevation, or height of the defined platform + instrument above sea level. Latitude ° North Latitude of the measurement station, this is used when differs from the one of the instrument. Level code Dimensionless Code for the level type. Location latitude ° North Latitude of the instrument. Location longitude ° East Longitude of the instrument. Longitude ° East Longitude of the measurement station, this is used when differs from the one of the instrument. Monthly npts Dimensionless The number of points used to estimate the monthly mean ozone value. Typically this is the number of daily averages. Number of observations Dimensionless Number of observations used to calculate the total column ozone value. Obs code Dimensionless Code to designate the type of total ozone measurement. Observation height above station surface m Geographical height of the osbservation. Observation value According to the unit as specified in the main variables table. Measurement value of the variable in question when the data is provided in the "one row per observation format". Observed variable Dimensionless Specification of the measured variable when the data is provided in the "one row per observation format". Other ids Dimensionless Three-letter GAW ID as issued by GAWSIS, if available. Ozone reference time mean decimal hours, UTC The mean time of observations. Ozone reference total ozone Dobson-units Daily value of total column ozone amount defined as the "best representative value" in the order of Direct Sun (DS), Zenith Cloud (ZS) and Focused Moon (FM). Pump motor current Ampere Electrical current measured through the pump motor. Pump motor voltage Volt Applied voltage measured across the pump motor. Reference model Dimensionless Model ID where applicable. Report timestamp Datetime timestamp datetime first day. Sample temperature K Temperature where sample is measured. Sensor id Dimensionless Model ID where applicable. Sensor model Dimensionless Radiosonde model. Sonde current Ampere Measured ozonesonde cell current with no corrections applied. Standard deviation According to the property type as specified in the main variables table. Standard deviation of the observation value when the data is provided in the "one row per observation format". Station name Dimensionless Unique station or flight ID assigned by the WOUDC to each registered platform. Time begin decimal hours, UTC The starting time of observations. Time end decimal hours, UTC The ending time of observations. Time mean decimal hours, UTC The mean time of observations. Time since launch s Elapsed flight time since released as primary variable. Type Dimensionless Type of observing platform. Wl code Dimensionless Code to designate the wavelength pair(s) used for total ozone measurement. RELATED VARIABLES RELATED VARIABLES The csv files contain columns for a range of auxiliary variables which describe the context of the observation(s), for example the time and location of the observation and/or station and the type of station and instrumentation used. Here we provide a list, and description, of the columns that may be provided in the downloaded csv files. The csv files contain columns for a range of auxiliary variables which describe the context of the observation(s), for example the time and location of the observation and/or station and the type of station and instrumentation used. Here we provide a list, and description, of the columns that may be provided in the downloaded csv files. Name Units Description Name Units Description Air pressure Pa Atmospheric pressure of each level in Pascals. Air pressure Pa Atmospheric pressure of each level in Pascals. Daily timestamp YYYY-MM-DD hh:mm:ss+00 Date of the observations. Daily timestamp YYYY-MM-DD hh:mm:ss+00 Date of the observations. Harmonic mean relative slant path Dimensionless The harmonic mean of the relative slant path through the ozone layer at 22Km for each of the observations used to compute the daily value. Harmonic mean relative slant path Dimensionless The harmonic mean of the relative slant path through the ozone layer at 22Km for each of the observations used to compute the daily value. Height of station above sea level meters above sea level Height is defined as the altitude, elevation, or height of the defined platform + instrument above sea level. Height of station above sea level meters above sea level Height is defined as the altitude, elevation, or height of the defined platform + instrument above sea level. Latitude ° North Latitude of the measurement station, this is used when differs from the one of the instrument. Latitude ° North Latitude of the measurement station, this is used when differs from the one of the instrument. Level code Dimensionless Code for the level type. Level code Dimensionless Code for the level type. Location latitude ° North Latitude of the instrument. Location latitude ° North Latitude of the instrument. Location longitude ° East Longitude of the instrument. Location longitude ° East Longitude of the instrument. Longitude ° East Longitude of the measurement station, this is used when differs from the one of the instrument. Longitude ° East Longitude of the measurement station, this is used when differs from the one of the instrument. Monthly npts Dimensionless The number of points used to estimate the monthly mean ozone value. Typically this is the number of daily averages. Monthly npts Dimensionless The number of points used to estimate the monthly mean ozone value. Typically this is the number of daily averages. Number of observations Dimensionless Number of observations used to calculate the total column ozone value. Number of observations Dimensionless Number of observations used to calculate the total column ozone value. Obs code Dimensionless Code to designate the type of total ozone measurement. Obs code Dimensionless Code to designate the type of total ozone measurement. Observation height above station surface m Geographical height of the osbservation. Observation height above station surface m Geographical height of the osbservation. Observation value According to the unit as specified in the main variables table. Measurement value of the variable in question when the data is provided in the "one row per observation format". Observation value According to the unit as specified in the main variables table. Measurement value of the variable in question when the data is provided in the "one row per observation format". Observed variable Dimensionless Specification of the measured variable when the data is provided in the "one row per observation format". Observed variable Dimensionless Specification of the measured variable when the data is provided in the "one row per observation format". Other ids Dimensionless Three-letter GAW ID as issued by GAWSIS, if available. Other ids Dimensionless Three-letter GAW ID as issued by GAWSIS, if available. Ozone reference time mean decimal hours, UTC The mean time of observations. Ozone reference time mean decimal hours, UTC The mean time of observations. Ozone reference total ozone Dobson-units Daily value of total column ozone amount defined as the "best representative value" in the order of Direct Sun (DS), Zenith Cloud (ZS) and Focused Moon (FM). Ozone reference total ozone Dobson-units Daily value of total column ozone amount defined as the "best representative value" in the order of Direct Sun (DS), Zenith Cloud (ZS) and Focused Moon (FM). Pump motor current Ampere Electrical current measured through the pump motor. Pump motor current Ampere Electrical current measured through the pump motor. Pump motor voltage Volt Applied voltage measured across the pump motor. Pump motor voltage Volt Applied voltage measured across the pump motor. Reference model Dimensionless Model ID where applicable. Reference model Dimensionless Model ID where applicable. Report timestamp Datetime timestamp datetime first day. Report timestamp Datetime timestamp datetime first day. Sample temperature K Temperature where sample is measured. Sample temperature K Temperature where sample is measured. Sensor id Dimensionless Model ID where applicable. Sensor id Dimensionless Model ID where applicable. Sensor model Dimensionless Radiosonde model. Sensor model Dimensionless Radiosonde model. Sonde current Ampere Measured ozonesonde cell current with no corrections applied. Sonde current Ampere Measured ozonesonde cell current with no corrections applied. Standard deviation According to the property type as specified in the main variables table. Standard deviation of the observation value when the data is provided in the "one row per observation format". Standard deviation According to the property type as specified in the main variables table. Standard deviation of the observation value when the data is provided in the "one row per observation format". Station name Dimensionless Unique station or flight ID assigned by the WOUDC to each registered platform. Station name Dimensionless Unique station or flight ID assigned by the WOUDC to each registered platform. Time begin decimal hours, UTC The starting time of observations. Time begin decimal hours, UTC The starting time of observations. Time end decimal hours, UTC The ending time of observations. Time end decimal hours, UTC The ending time of observations. Time mean decimal hours, UTC The mean time of observations. Time mean decimal hours, UTC The mean time of observations. Time since launch s Elapsed flight time since released as primary variable. Time since launch s Elapsed flight time since released as primary variable. Type Dimensionless Type of observing platform. Type Dimensionless Type of observing platform. Wl code Dimensionless Code to designate the wavelength pair(s) used for total ozone measurement. Wl code Dimensionless Code to designate the wavelength pair(s) used for total ozone measurement. 749 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/water-level-rivers-2002-present-vector-global-overpass http://land.copernicus.eu/global/access Water Level Rivers 2002-present (vector), global, per overpass - version 2 The Water Level is defined as the height, in meters above the geoid, of the reflecting surface of continental water bodies. It is observed by space radar altimeters that measure the time it takes for radar pulses to reach the ground targets, directly below the spacecraft (nadir position), and return. Hence, only water bodies located along the satellite's ground tracks can be monitored, with a quality of measurement that not only depends of the size of the water body, but also on the reflecting targets in its surroundings such as topography or vegetation. Water Level is computed as time series over lakes and at the intersections of rivers and the satellite ground tracks. 750 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/water-level-lakes-1992-present-vector-global-overpass http://land.copernicus.eu/global/access Water Level Lakes 1992-present (vector), global, per overpass - version 2 The Water Level is defined as the height, in meters above the geoid, of the reflecting surface of continental water bodies. It is observed by space radar altimeters that measure the time it takes for radar pulses to reach the ground targets, directly below the spacecraft (nadir position), and return. Hence, only water bodies located along the satellite's ground tracks can be monitored, with a quality of measurement that not only depends of the size of the water body, but also on the reflecting targets in its surroundings such as topography or vegetation. Water Level is computed as time series over lakes and at the intersections of rivers and the satellite ground tracks. 751 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-biogeochemistry-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BLKSEA_ANALYSISFORECAST_BGC_007_010 Black Sea Biogeochemistry Analysis and Forecast Short description: BLKSEA_ANALYSISFORECAST_BGC_007_010 is the nominal product of the Black Sea Biogeochemistry NRT system and is generated by the NEMO 4.0-BAMHBI modelling system. Biogeochemical Model for Hypoxic and Benthic Influenced areas (BAMHBI) is an innovative biogeochemical model with a 28-variable pelagic component (including the carbonate system) and a 6-variable benthic component ; it explicitely represents processes in the anoxic layer. The product provides analysis and forecast for 3D concentration of chlorophyll, nutrients (nitrate and phosphate), dissolved oxygen, phytoplankton carbon biomass, net primary production, pH, dissolved inorganic carbon, total alkalinity, and for 2D fields of bottom oxygen concentration (for the North-Western shelf), surface partial pressure of CO2 and surface flux of CO2. These variables are computed on a grid with ~3km x 59-levels resolution, and are provided as daily and monthly means. Product Citation: Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (product) :https://doi.org/10.25423/cmcc/blksea_analysisforecast_bgc_007_010 https://doi.org/10.25423/cmcc/blksea_analysisforecast_bgc_007_010 752 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/atlantic-european-north-west-shelf-ocean-situ-near-real http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=INSITU_NWS_PHYBGCWAV_DISCRETE_MYNRT_013_036 Atlantic- European North West Shelf- Ocean In-Situ Near Real Time observations Short description: NorthWest Shelf area - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average DOI (product) :https://doi.org/10.48670/moi-00045 https://doi.org/10.48670/moi-00045 753 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/gross-dry-matter-productivity-2014-present-raster-300-m https://land.copernicus.eu/global/products/dmp Gross Dry Matter Productivity 2014-present (raster 300 m), global, 10-daily - version 1 Gross dry matter Productivity (GDMP) is an indication of the overall growth rate or dry biomass increase of the vegetation and is directly related to ecosystem Gross Primary Productivity (GPP), that reflects the ecosystem's overall production of organic compounds from atmospheric carbon dioxide, however its units (kilograms of gross dry matter per hectare per day) are customized for agro-statistical purposes. Like the FAPAR products that are used as input for the GDMP estimation, these GDMP products are provided in Near Real Time, with consolidations in the next periods, or as offline product. 754 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-physics-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BLKSEA_MULTIYEAR_PHY_007_004 Black Sea Physics Reanalysis Short description: The BLKSEA_MULTIYEAR_PHY_007_004 product provides monthly and daily ocean fields for the Black Sea basin starting from 01/01/1993. The hydrodynamical core is based on NEMO general circulation ocean model, implemented in the BS domain with horizontal resolution of 1/27° x 1/36° and 31 vertical levels. NEMO is forced by atmospheric surface fluxes computed by bulk formulation using ECMWF ERA5 atmospheric fields at the resolution of 0.25° in space and 1-h in time. The current version has closed boundary at the Bosporus Strait. The model is online coupled to OceanVar assimilation scheme to assimilate sea level anomaly along-track observations from CMEMS and available in situ vertical profiles of temperature and salinity from both SeaDataNet and CMEMS datasets. Product Citation: Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (Product):https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004 https://doi.org/10.25423/CMCC/BLKSEA_MULTIYEAR_PHY_007_004 755 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/insitu-observations-igra-baseline-network https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-observations-igra-baseline-network insitu-observations-igra-baseline-network This catalogue entry provides access to vertical profiles of standard meteorological variables. It includes two datasets. The first is version 2 of the Integrated Global Radiosounding Archive (IGRA) from 1978 which incorporates global radiosounding profiles of temperature, humidity and wind from a large number of data sources, which is 30% larger than the previous version 1. IGRA v2 is the result of quality assurance procedures applied to the radiosoundings data which can be grouped into eight categories: fundamental “sanity” checks, checks on the plausibility and temporal consistency of surface elevation, internal consistency checks, checks for the repetition of values, checks for gross position errors in ship tracks, climatology-based checks, checks on the vertical and temporal consistency of temperature, and data completeness checks. No uncertainty estimation for the IGRA data is available. The second is the Radiosounding HARMonization (RHARM) homogenized dataset which provides adjusted values of temperature, relative humidity and wind, removing systematic effects (such as change in the measurement sensors, solar radiation biases, sonde time-lag, calibration drifts, station relocation etc.) at 700 IGRA radiosounding stations and radiosoundings from ships. RHARM includes twice daily (0000 and 1200 UTC) radiosonde data at the mandatory levels (listed below). At pressure levels lower than 10 hPa, temperature harmonized values are not provided because of the paucity of available observations and the issues affecting the measurements at those levels. Relative humidity adjustments and data provision have been limited to 250 hPa owing to pervasive sensor performance issues at greater altitudes. Moreover, an estimation of the measurement uncertainty is provided for each value of the time series. The radiosounding significance levels are also adjusted per interpolation along with the corresponding uncertainties. The RHARM dataset thus inherits the IGRA quality assurance procedures, and additional quality checks are then applied, performing tests on physical plausibility, accuracy of the bias adjustment, presence of outliers, and coherency check for the adjustments applied at the significant levels. The IGRA dataset is provided by NOAA's National Centers for Environmental Information (NCEI) also through the IGRA data portal: IGRA data portal. IGRA data portal. IGRA data portal The RHARM dataset has been specifically developed for the Copernicus Climate Change Service (C3S). Radiosoundings can be downloaded as comma-separated values (CSV) files organised in two different ways: CSV one row per level: for each observed height all variables are provided as individual columns in one row. Each main variable has its dedicated columns for total uncertainty, where applies (referenced as csv-lev in the variable lists below). CSV one row per observation: (no aggregation) each row provides information about one variable at one level only. All main variable are grouped together in the following columns: one for the observed variable (indicates variable in question), one for observation value and one column for total uncertainty (referenced as csv-obs in the variable lists below). CSV one row per level: for each observed height all variables are provided as individual columns in one row. Each main variable has its dedicated columns for total uncertainty, where applies (referenced as csv-lev in the variable lists below). CSV one row per observation: (no aggregation) each row provides information about one variable at one level only. All main variable are grouped together in the following columns: one for the observed variable (indicates variable in question), one for observation value and one column for total uncertainty (referenced as csv-obs in the variable lists below). DATA DESCRIPTION Data type Point data Horizontal coverage Global (656 stations) Vertical coverage Up to about 40km, varying from sonder launch Vertical resolution Radiosonde mandatory levels (10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000 hPa) plus significant levels which variates from a profile to another Temporal coverage From 1978 to present (start date is depending on the station) Temporal resolution Sub-daily File format CSV Versions Current dataset is version 1 which corresponds to IGRA v2 and RHARM v1 Update frequency Irregular DATA DESCRIPTION DATA DESCRIPTION Data type Point data Data type Point data Horizontal coverage Global (656 stations) Horizontal coverage Global (656 stations) Vertical coverage Up to about 40km, varying from sonder launch Vertical coverage Up to about 40km, varying from sonder launch Vertical resolution Radiosonde mandatory levels (10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000 hPa) plus significant levels which variates from a profile to another Vertical resolution Radiosonde mandatory levels (10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000 hPa) plus significant levels which variates from a profile to another Temporal coverage From 1978 to present (start date is depending on the station) Temporal coverage From 1978 to present (start date is depending on the station) Temporal resolution Sub-daily Temporal resolution Sub-daily File format CSV File format CSV Versions Current dataset is version 1 which corresponds to IGRA v2 and RHARM v1 Versions Current dataset is version 1 which corresponds to IGRA v2 and RHARM v1 Update frequency Irregular Update frequency Irregular MAIN VARIABLES Name Units Description Air dewpoint depression K The difference between air temperature and dew point temperature. The dew point temperature is the temperature to which a given air parcel must be cooled at constant pressure and constant water vapour content in order for saturation to occur (harmonized global radiosonde archive, global radiosonde archive) Air temperature K The harmonized value temperature obtained using RHARM (Radiosounding HARMonization) approach (harmonized global radiosonde archive, global radiosonde archive) Air temperature total uncertainty K Value of the total uncertainty for the harmonized temperature obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Ascent speed m s-1 Ascent speed of the radiosonde calculated from altitude: (maximum height reported - minimum height reported) / (time at maximum height reported - time at minimum height reported) (harmonized global radiosonde archive) Eastward wind component m s-1 The harmonized value eastward wind speed component obtained using RHARM (Radiosounding HARMonization) approach (harmonized global radiosonde archive) Eastward wind component total uncertainty m s-1 Value of the total uncertainty for the eastward wind speed component humidity obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Frost point temperature K Temperature, below 0° C, at which moisture in the air will condense as a layer of frost on any exposed surface. For details on the calculation formula please check the ATBD (harmonized global radiosonde archive) Geopotential height m Height of a standard or significant pressure level in meters (harmonized global radiosonde archive, global radiosonde archive) Northward wind component m s-1 The harmonized value northward wind speed component obtained using RHARM (Radiosounding HARMonization) approach (harmonized global radiosonde archive) Northward wind component total uncertainty m s-1 Value of the total uncertainty for the northward wind speed component humidity obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Relative humidity % The harmonized value relative humidity obtained using RHARM (Radiosounding HARMonization) approach (harmonized global radiosonde archive, global radiosonde archive) Relative humidity total uncertainty % Value of the total uncertainty for the harmonized relative humidity obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Solar zenith angle degrees from zenith The solar zenith angle is the angle between the zenith and the centre of the Sun's disc (harmonized global radiosonde archive) Water vapor volume mixing ratio mol mol-1 Volume water mixing ratio vapor calculated using Hyland, R. W. and A. Wexler. For details on the calculation formula please check the ATBD (harmonized global radiosonde archive) Wind from direction degree from north Wind direction (degrees from north, 90 = east) (harmonized global radiosonde archive, global radiosonde archive) Wind from direction total uncertainty degree from north Value of the total uncertainty for wind direction obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Wind speed m s-1 Horizontal speed of the wind, or movement of air, at the height of the observation (harmonized global radiosonde archive, global radiosonde archive) Wind speed total uncertainty m s-1 Value of the total uncertainty for wind speed obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) MAIN VARIABLES MAIN VARIABLES Name Units Description Name Units Description Air dewpoint depression K The difference between air temperature and dew point temperature. The dew point temperature is the temperature to which a given air parcel must be cooled at constant pressure and constant water vapour content in order for saturation to occur (harmonized global radiosonde archive, global radiosonde archive) Air dewpoint depression K The difference between air temperature and dew point temperature. The dew point temperature is the temperature to which a given air parcel must be cooled at constant pressure and constant water vapour content in order for saturation to occur (harmonized global radiosonde archive, global radiosonde archive) Air temperature K The harmonized value temperature obtained using RHARM (Radiosounding HARMonization) approach (harmonized global radiosonde archive, global radiosonde archive) Air temperature K The harmonized value temperature obtained using RHARM (Radiosounding HARMonization) approach (harmonized global radiosonde archive, global radiosonde archive) Air temperature total uncertainty K Value of the total uncertainty for the harmonized temperature obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Air temperature total uncertainty K Value of the total uncertainty for the harmonized temperature obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Ascent speed m s-1 Ascent speed of the radiosonde calculated from altitude: (maximum height reported - minimum height reported) / (time at maximum height reported - time at minimum height reported) (harmonized global radiosonde archive) Ascent speed m s-1 Ascent speed of the radiosonde calculated from altitude: (maximum height reported - minimum height reported) / (time at maximum height reported - time at minimum height reported) (harmonized global radiosonde archive) Eastward wind component m s-1 The harmonized value eastward wind speed component obtained using RHARM (Radiosounding HARMonization) approach (harmonized global radiosonde archive) Eastward wind component m s-1 The harmonized value eastward wind speed component obtained using RHARM (Radiosounding HARMonization) approach (harmonized global radiosonde archive) Eastward wind component total uncertainty m s-1 Value of the total uncertainty for the eastward wind speed component humidity obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Eastward wind component total uncertainty m s-1 Value of the total uncertainty for the eastward wind speed component humidity obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Frost point temperature K Temperature, below 0° C, at which moisture in the air will condense as a layer of frost on any exposed surface. For details on the calculation formula please check the ATBD (harmonized global radiosonde archive) Frost point temperature K Temperature, below 0° C, at which moisture in the air will condense as a layer of frost on any exposed surface. For details on the calculation formula please check the ATBD (harmonized global radiosonde archive) Geopotential height m Height of a standard or significant pressure level in meters (harmonized global radiosonde archive, global radiosonde archive) Geopotential height m Height of a standard or significant pressure level in meters (harmonized global radiosonde archive, global radiosonde archive) Northward wind component m s-1 The harmonized value northward wind speed component obtained using RHARM (Radiosounding HARMonization) approach (harmonized global radiosonde archive) Northward wind component m s-1 The harmonized value northward wind speed component obtained using RHARM (Radiosounding HARMonization) approach (harmonized global radiosonde archive) Northward wind component total uncertainty m s-1 Value of the total uncertainty for the northward wind speed component humidity obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Northward wind component total uncertainty m s-1 Value of the total uncertainty for the northward wind speed component humidity obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Relative humidity % The harmonized value relative humidity obtained using RHARM (Radiosounding HARMonization) approach (harmonized global radiosonde archive, global radiosonde archive) Relative humidity % The harmonized value relative humidity obtained using RHARM (Radiosounding HARMonization) approach (harmonized global radiosonde archive, global radiosonde archive) Relative humidity total uncertainty % Value of the total uncertainty for the harmonized relative humidity obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Relative humidity total uncertainty % Value of the total uncertainty for the harmonized relative humidity obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Solar zenith angle degrees from zenith The solar zenith angle is the angle between the zenith and the centre of the Sun's disc (harmonized global radiosonde archive) Solar zenith angle degrees from zenith The solar zenith angle is the angle between the zenith and the centre of the Sun's disc (harmonized global radiosonde archive) Water vapor volume mixing ratio mol mol-1 Volume water mixing ratio vapor calculated using Hyland, R. W. and A. Wexler. For details on the calculation formula please check the ATBD (harmonized global radiosonde archive) Water vapor volume mixing ratio mol mol-1 Volume water mixing ratio vapor calculated using Hyland, R. W. and A. Wexler. For details on the calculation formula please check the ATBD (harmonized global radiosonde archive) Wind from direction degree from north Wind direction (degrees from north, 90 = east) (harmonized global radiosonde archive, global radiosonde archive) Wind from direction degree from north Wind direction (degrees from north, 90 = east) (harmonized global radiosonde archive, global radiosonde archive) Wind from direction total uncertainty degree from north Value of the total uncertainty for wind direction obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Wind from direction total uncertainty degree from north Value of the total uncertainty for wind direction obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Wind speed m s-1 Horizontal speed of the wind, or movement of air, at the height of the observation (harmonized global radiosonde archive, global radiosonde archive) Wind speed m s-1 Horizontal speed of the wind, or movement of air, at the height of the observation (harmonized global radiosonde archive, global radiosonde archive) Wind speed total uncertainty m s-1 Value of the total uncertainty for wind speed obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) Wind speed total uncertainty m s-1 Value of the total uncertainty for wind speed obtained using RHARM (Radiosounding HARMonization) approach. See section 2-4 of the Algorithm theoretical basis description of the RHARM dataset (harmonized global radiosonde archive) RELATED VARIABLES Name Units Description Actual time Not applicable Release time of the sounding in date time UTC (harmonized global radiosonde archive) Air pressure Pa Barometric air pressure (harmonized global radiosonde archive, global radiosonde archive) Height of station above sea level m Altitude above means sea level (harmonized global radiosonde archive) Latitude degree_north Latitude of the station (deg. North) (harmonized global radiosonde archive, global radiosonde archive) Longitude degree_east Longitude of the station (deg. East) (harmonized global radiosonde archive, global radiosonde archive) Observation value According to the unit as specified in the main variables table. Measurement value of the variable in question (csv-obs only). Observed variable Not applicable Specification of the measurand (csv-obs only). Radiosonde code Not applicable Common Code table as from WMO definitions (code table 3685) (harmonized global radiosonde archive) Report id Not applicable Identifier in the RHARM meta-database (harmonized global radiosonde archive, global radiosonde archive) Report timestamp Not applicable Observation date time UTC (harmonized global radiosonde archive, global radiosonde archive) Sensor model Not applicable Details on the sensor used (harmonized global radiosonde archive) Station name Not applicable Station identification code (harmonized global radiosonde archive, global radiosonde archive) Total uncertainty According to the property type as specified in the main variables table. For variables when a property/attribute named total uncertainty is present provides the corresponding value (csv-obs only) RELATED VARIABLES RELATED VARIABLES Name Units Description Name Units Description Actual time Not applicable Release time of the sounding in date time UTC (harmonized global radiosonde archive) Actual time Not applicable Release time of the sounding in date time UTC (harmonized global radiosonde archive) Air pressure Pa Barometric air pressure (harmonized global radiosonde archive, global radiosonde archive) Air pressure Pa Barometric air pressure (harmonized global radiosonde archive, global radiosonde archive) Height of station above sea level m Altitude above means sea level (harmonized global radiosonde archive) Height of station above sea level m Altitude above means sea level (harmonized global radiosonde archive) Latitude degree_north Latitude of the station (deg. North) (harmonized global radiosonde archive, global radiosonde archive) Latitude degree_north Latitude of the station (deg. North) (harmonized global radiosonde archive, global radiosonde archive) Longitude degree_east Longitude of the station (deg. East) (harmonized global radiosonde archive, global radiosonde archive) Longitude degree_east Longitude of the station (deg. East) (harmonized global radiosonde archive, global radiosonde archive) Observation value According to the unit as specified in the main variables table. Measurement value of the variable in question (csv-obs only). Observation value According to the unit as specified in the main variables table. Measurement value of the variable in question (csv-obs only). Observed variable Not applicable Specification of the measurand (csv-obs only). Observed variable Not applicable Specification of the measurand (csv-obs only). Radiosonde code Not applicable Common Code table as from WMO definitions (code table 3685) (harmonized global radiosonde archive) Radiosonde code Not applicable Common Code table as from WMO definitions (code table 3685) (harmonized global radiosonde archive) Report id Not applicable Identifier in the RHARM meta-database (harmonized global radiosonde archive, global radiosonde archive) Report id Not applicable Identifier in the RHARM meta-database (harmonized global radiosonde archive, global radiosonde archive) Report timestamp Not applicable Observation date time UTC (harmonized global radiosonde archive, global radiosonde archive) Report timestamp Not applicable Observation date time UTC (harmonized global radiosonde archive, global radiosonde archive) Sensor model Not applicable Details on the sensor used (harmonized global radiosonde archive) Sensor model Not applicable Details on the sensor used (harmonized global radiosonde archive) Station name Not applicable Station identification code (harmonized global radiosonde archive, global radiosonde archive) Station name Not applicable Station identification code (harmonized global radiosonde archive, global radiosonde archive) Total uncertainty According to the property type as specified in the main variables table. For variables when a property/attribute named total uncertainty is present provides the corresponding value (csv-obs only) Total uncertainty According to the property type as specified in the main variables table. For variables when a property/attribute named total uncertainty is present provides the corresponding value (csv-obs only) 756 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/antarctic-ocean-high-resolution-sea-ice-information http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_ANT_PHY_AUTO_L3_NRT_011_012 Antarctic Ocean - High Resolution Sea Ice Information Short description: For the Antarctic Sea - A sea ice concentration product based on satellite SAR imagery and microwave radiometer data: The algorithm uses SENTINEL-1 SAR EW and IW mode dual-polarized HH/HV data combined with AMSR2 radiometer data. DOI (product) :https://doi.org/10.48670/mds-00320 https://doi.org/10.48670/mds-00320 757 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-cod-reproductive-volume-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BALTIC_OMI_HEALTH_codt_volume Baltic Sea Cod Reproductive Volume from Reanalysis DEFINITION The cod reproductive volume has been derived from regional reanalysis modelling results for the Baltic Sea (BALTICSEA_REANALYSIS_PHY_003_011 and BALTICSEA_REANALYSIS_BIO_003_012). The volume has been calculated taking into account the three most important influencing abiotic factors of cod reproductive success: salinity > 11 g/kg, oxygen concentration > 2 ml/l and water temperature over 1.5°C (MacKenzie et al., 1996; Heikinheimo, 2008; Plikshs et al., 2015). The daily volumes are calculated as the volumes of the water with salinity > 11 g/kg, oxygen content > 2 ml/l and water temperature over 1.5°C in the Baltic Sea International Council for the Exploration of the Sea subdivisions of 25-28 (ICES, 2019). CONTEXT Cod (Gadus morhua) is a characteristic fish species in the Baltic Sea with major economic importance. The Baltic cod stocks have gone through a steep decline in the late 1980s (ICES, 2005). Water salinity and oxygen concentration affect cod stock through the survival of eggs (Westin and Nissling, 1991; Wieland et al., 1994). Major Baltic Inflows provide a suitable environment for cod reproduction by bringing saline oxygenated water to the deep basins of the Baltic Sea (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm and BALTIC_OMI_WMHE_mbi_sto2tz_gotland). Increased cod reproductive volume has a positive effect on cod reproduction success, which reflects in an increase of stock size indicator 4–5 years after the Major Baltic Inflow (Raudsepp et al., 2019; BALTIC_OMI_MBI). Eastern Baltic cod reaches maturity around age 2–3, depending on the population density and environmental conditions. Low oxygen and salinity cause stress, which negatively affects cod recruitment, whereas sufficient conditions may bring about male cod maturation even at the age of 1.5 years (Cardinale and Modin, 1999; Karasiova et al., 2008). CMEMS KEY FINDINGS In general, the cod reproductive volume fluctuates between 200 and 400 km3. There are periods, when cod reproductive volume increases to 700-800 km3, like 2003-2004, 2006 - first half of 2007, 2014 - first half of 2015 and 2016-beginning of 2017. Usually, the increase of cod reproductive volume is the result of Major Baltic Inflows. Suitable conditions for cod reproduction persist for a shorter time period than that of the saline water volume (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm and BALTIC_OMI_WMHE_mbi_sto2tz_gotland), indicating that oxygen content declines faster than salinity in the bottom layers of the Baltic Sea (Raudsepp et al. 2018). There was a relatively high cod reproductive volume of 800 km3 in the Baltic Sea in 2019, which decreased down to 200 km3 by the end of 2021. DOI (product):https://doi.org/10.48670/moi-00196 https://doi.org/10.48670/moi-00196 758 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/soil-water-index-2007-present-raster-125-km-global-10 http://web.vgt.vito.be/download_g2.php?file=&path=http%3A//geoland2.meteo.pt/g2system/operations/products/SWI/ Soil Water Index 2007-present (raster 12.5 km), global, 10-daily - version 3 The Soil Water Index 10 day product (SWI10) averages the daily SWI product over 10 days. It is produced on every 10th, 20th and last of each Month. 759 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/fraction-absorbed-photosynthetically-active-radiation-1 http://land.copernicus.eu/global/products/fapar Fraction of Absorbed Photosynthetically Active Radiation 1999-2020 (raster 1 km), global, 10-daily - version 2 FAPAR was defined by CEOS as half the developed area of the convex hull wrapping the green canopy elements per unit horizontal ground. This definition allows accounting for elements which are not flat such as needles or stems. FAPAR is strongly non linearly related to reflectance. Therefore, its estimation from remote sensing observations will be scale dependant over heterogeneous landscapes. When observing a canopy made of different layers of vegetation, it is therefore mandatory to consider all the green layers. This is particularly important for forest canopies where the understory may represent a very significant contribution to the total canopy FAPAR. The derived FAPAR corresponds therefore to the total green FAPAR, including the contribution of the green elements of the understory. The resulting GEOV1 FAPAR products are relatively consistent with the actual FAPAR for low FAPAR values and ?non-forest? surfaces; while for forests, particularly for needle leaf types, significant departures with the true FAPAR are expected. 760 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-apparent-temperature-heatwave-days https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-apparent-temperature-heatwave-days ecde-app-apparent-temperature-heatwave-days This application has been published in a hidden state for contractual purposes. 761 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-maximum-temperature-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-maximum-temperature-v2 ecde-app-maximum-temperature-v2 This application has been published in a hidden state for contractual purposes. 762 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-total-precipitation https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-total-precipitation ecde-app-total-precipitation This application has been published in a hidden state for contractual purposes. 763 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-growing-degree-days-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-growing-degree-days-v1 ecde-app-growing-degree-days-v1 This application has been published in a hidden state for contractual purposes. 764 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-maximum-five-days-precipitation https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-maximum-five-days-precipitation ecde-app-maximum-five-days-precipitation This application has been published in a hidden state for contractual purposes. 765 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-frost-days https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-frost-days ecde-app-frost-days This application has been published in a hidden state for contractual purposes. 766 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-tiger-mosquito-season-length https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-tiger-mosquito-season-length ecde-app-tiger-mosquito-season-length This application has been published in a hidden state for contractual purposes. 767 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-maximum-temperature https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-maximum-temperature ecde-app-maximum-temperature This application has been published in a hidden state for contractual purposes. 768 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-river-discharge https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-river-discharge ecde-app-river-discharge This application has been published in a hidden state for contractual purposes. 769 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-total-precipitation-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-total-precipitation-v1 ecde-app-total-precipitation-v1 This application has been published in a hidden state for contractual purposes. 770 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-snowfall-amount https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-snowfall-amount ecde-app-snowfall-amount This application has been published in a hidden state for contractual purposes. 771 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-maximum-five-days-precipitation-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-maximum-five-days-precipitation-v2 ecde-app-maximum-five-days-precipitation-v2 This application has been published in a hidden state for contractual purposes. 772 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-growing-degree-days https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-growing-degree-days ecde-app-growing-degree-days This application has been published in a hidden state for contractual purposes. 773 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-tropical-nights-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-tropical-nights-v2 ecde-app-tropical-nights-v2 This application has been published in a hidden state for contractual purposes. 774 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-aridity-actual-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-aridity-actual-v1 ecde-app-aridity-actual-v1 This application has been published in a hidden state for contractual purposes. 775 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-high-fire-danger-days-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-high-fire-danger-days-v1 ecde-app-high-fire-danger-days-v1 This application has been published in a hidden state for contractual purposes. 776 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-high-fire-danger-days-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-high-fire-danger-days-v2 ecde-app-high-fire-danger-days-v2 This application has been published in a hidden state for contractual purposes. 777 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-snowfall-amount-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-snowfall-amount-v2 ecde-app-snowfall-amount-v2 This application has been published in a hidden state for contractual purposes. 778 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-frost-days-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-frost-days-v2 ecde-app-frost-days-v2 This application has been published in a hidden state for contractual purposes. 779 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-maximum-five-days-precipitation-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-maximum-five-days-precipitation-v1 ecde-app-maximum-five-days-precipitation-v1 This application has been published in a hidden state for contractual purposes. 780 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-minimum-temperature-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-minimum-temperature-v2 ecde-app-minimum-temperature-v2 This application has been published in a hidden state for contractual purposes. 781 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-high-utci-days https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-high-utci-days ecde-app-high-utci-days This application has been published in a hidden state for contractual purposes. 782 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-tiger-mosquito-season-length-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-tiger-mosquito-season-length-v2 ecde-app-tiger-mosquito-season-length-v2 This application has been published in a hidden state for contractual purposes. 783 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-high-fire-danger-days https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-high-fire-danger-days ecde-app-high-fire-danger-days This application has been published in a hidden state for contractual purposes. 784 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-relative-sea-level-rise https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-relative-sea-level-rise ecde-app-relative-sea-level-rise This application has been published in a hidden state for contractual purposes. 785 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-fire-weather-index https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-fire-weather-index ecde-app-fire-weather-index This application has been published in a hidden state for contractual purposes. 786 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-high-utci-days-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-high-utci-days-v1 ecde-app-high-utci-days-v1 This application has been published in a hidden state for contractual purposes. 787 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-tiger-mosquito-suitability-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-tiger-mosquito-suitability-v2 ecde-app-tiger-mosquito-suitability-v2 This application has been published in a hidden state for contractual purposes. 788 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-extreme-sea-level https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-extreme-sea-level ecde-app-extreme-sea-level This application has been published in a hidden state for contractual purposes. 789 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-total-precipitation-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-total-precipitation-v2 ecde-app-total-precipitation-v2 This application has been published in a hidden state for contractual purposes. 790 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-river-flood https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-river-flood ecde-app-river-flood This application has been published in a hidden state for contractual purposes. 791 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-fire-weather-index-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-fire-weather-index-v1 ecde-app-fire-weather-index-v1 This application has been published in a hidden state for contractual purposes. 792 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-aridity-actual https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-aridity-actual ecde-app-aridity-actual This application has been published in a hidden state for contractual purposes. 793 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-aridity-actual-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-aridity-actual-v2 ecde-app-aridity-actual-v2 This application has been published in a hidden state for contractual purposes. 794 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-minimum-temperature-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-minimum-temperature-v1 ecde-app-minimum-temperature-v1 This application has been published in a hidden state for contractual purposes. 795 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-maximum-temperature-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-maximum-temperature-v1 ecde-app-maximum-temperature-v1 This application has been published in a hidden state for contractual purposes. 796 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-climatological-heatwave-days https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-climatological-heatwave-days ecde-app-climatological-heatwave-days This application has been published in a hidden state for contractual purposes. 797 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-tiger-mosquito-suitability https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-tiger-mosquito-suitability ecde-app-tiger-mosquito-suitability This application has been published in a hidden state for contractual purposes. 798 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-minimum-temperature https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-minimum-temperature ecde-app-minimum-temperature This application has been published in a hidden state for contractual purposes. 799 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-tropical-nights https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-tropical-nights ecde-app-tropical-nights This application has been published in a hidden state for contractual purposes. 800 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-tiger-mosquito-suitability-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-tiger-mosquito-suitability-v1 ecde-app-tiger-mosquito-suitability-v1 This application has been published in a hidden state for contractual purposes. 801 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-frost-days-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-frost-days-v1 ecde-app-frost-days-v1 This application has been published in a hidden state for contractual purposes. 802 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-tiger-mosquito-season-length-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-tiger-mosquito-season-length-v1 ecde-app-tiger-mosquito-season-length-v1 This application has been published in a hidden state for contractual purposes. 803 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-high-utci-days-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-high-utci-days-v2 ecde-app-high-utci-days-v2 This application has been published in a hidden state for contractual purposes. 804 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-snowfall-amount-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-snowfall-amount-v1 ecde-app-snowfall-amount-v1 This application has been published in a hidden state for contractual purposes. 805 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-growing-degree-days-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-growing-degree-days-v2 ecde-app-growing-degree-days-v2 This application has been published in a hidden state for contractual purposes. 806 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-apparent-temperature-heatwave-days-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-apparent-temperature-heatwave-days-v2 ecde-app-apparent-temperature-heatwave-days-v2 This application has been published in a hidden state for contractual purposes. 807 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-apparent-temperature-heatwave-days-v1 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-apparent-temperature-heatwave-days-v1 ecde-app-apparent-temperature-heatwave-days-v1 This application has been published in a hidden state for contractual purposes. 808 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/ecde-app-fire-weather-index-v2 https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecde-app-fire-weather-index-v2 ecde-app-fire-weather-index-v2 This application has been published in a hidden state for contractual purposes. 809 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/european-ground-motion-service-ortho-vertical-component https://egms.land.copernicus.eu/ European Ground Motion Service: Ortho – Vertical Component 2015-2021 (vector), Europe, yearly, Feb. 2023 The European Ground Motion Service (EGMS) is a component of the Copernicus Land Monitoring Service. EGMS provides consistent, regular, standardised, harmonised and reliable information regarding natural and anthropogenic ground motion phenomena over the Copernicus Participating States and across national borders, with millimetre accuracy. This set of metadata describes the third product level of EGMS: Ortho. The EGMS Ortho product exploits the information provided by ascending and descending orbits of the Calibrated product (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/be…) to derive two further layers; one of purely vertical displacements (the one described by this metadata), the other of purely east-west displacements. Both layers are resampled to a 100 m grid. The Ortho product eases the interpretation process of non-experts since the viewing geometry has not to be considered anymore. EGMS Ortho is visualised as a vector map of measurement points colour-coded by average velocity (vertical or east-west components) and distributed to users in comma-separated values format. Each point is associated with a time series of displacement, i.e. a plot with values of displacement per acquisition of the satellite. https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/be… 810 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/baltic-sea-sar-sea-ice-thickness-and-drift-multisensor http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEAICE_BAL_SEAICE_L4_NRT_OBSERVATIONS_011_011 Baltic Sea - SAR Sea Ice Thickness and Drift, Multisensor Sea Ice Concentration Short description: For the Baltic Sea - The operational sea ice service at FMI provides ice parameters over the Baltic Sea. The products are based on SAR images and are produced on pass-by-pass basis during the Baltic Sea ice season, and show the ice thickness and drift in a 500 m and 800m grid, respectively. The Baltic sea ice concentration product is based on data from SAR and microwave radiometer. The algorithm uses SENTINEL-1 SAR EW mode dual-polarized HH/HV data combined with AMSR2 radiometer data. DOI (product) :https://doi.org/10.48670/moi-00133 https://doi.org/10.48670/moi-00133 811 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/european-ground-motion-service-basic-2015-2021-vector https://land.copernicus.eu/user-corner/technical-library/egms-product-user-manual European Ground Motion Service: Basic 2015-2021 (vector), Europe, yearly, Feb. 2023 The European Ground Motion Service (EGMS) is a component of the Copernicus Land Monitoring Service. EGMS provides consistent, regular, standardised, harmonised and reliable information regarding natural and anthropogenic ground motion phenomena over the Copernicus Participating States and across national borders, with millimetre accuracy. This set of metadata describes the first product level of EGMS: Basic. The EGMS Basic - provides InSAR displacement data provided in the satellite Line-of-Sight (LOS), with annotated geo-localisation and quality measures per measurement point. This product is generated from the interferometric analysis of Sentinel-1 radar images at full resolution. It contains line of sight velocity maps in ascending and descending orbits with annotated geolocalisation and quality parameters per measurement point. The Basic product is referred to a local reference point; therefore, ground motion measurements are meaningful only within a small subset of the full product. It is not possible to compare deformation from adjacent areas belonging to different processing units of the same level. EGMS Basic is visualised as a vector map of measurement points colour-coded by average line-of-sight velocity and distributed to users in comma-separated values format. Each point is associated with a time series of displacement, i.e. a plot with values of displacement per acquisition of the satellite. The product is generated for both ascending and descending orbits. The processing of the dataset has taken place in the period from August 2022 to January 2023. 812 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-biogechemistry-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MEDSEA_MULTIYEAR_BGC_006_008 Mediterranean Sea Biogechemistry Reanalysis Short Description The Mediterranean Sea biogeochemical reanalysis at 1/24° of horizontal resolution (ca. 4 km) covers the period from Jan 1999 to 1 month to the present and is produced by means of the MedBFM3 model system. MedBFM3, which is run by OGS (IT), includes the transport model OGSTM v4.0 coupled with the biogeochemical flux model BFM v5 and the variational data assimilation module 3DVAR-BIO v2.1 for surface chlorophyll. MedBFM3 is forced by the physical reanalysis (MEDSEA_MULTIYEAR_PHY_006_004 product run by CMCC) that provides daily forcing fields (i.e., currents, temperature, salinity, diffusivities, wind and solar radiation). The ESA-CCI database of surface chlorophyll concentration (CMEMS-OCTAC REP product) is assimilated with a weekly frequency. Cossarini, G., Feudale, L., Teruzzi, A., Bolzon, G., Coidessa, G., Solidoro C., Amadio, C., Lazzari, P., Brosich, A., Di Biagio, V., and Salon, S., 2021. High-resolution reanalysis of the Mediterranean Sea biogeochemistry (1999-2019). Frontiers in Marine Science. Front. Mar. Sci. 8:741486.doi: 10.3389/fmars.2021.741486 ''Product Citation'': Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… ''DOI (Product)'': https://doi.org/10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3 https://doi.org/10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3 ''DOI (Interim dataset)'':https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3I 813 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-reanalysis-eac4-monthly-0 https://cds.climate.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4-monthly cams-global-reanalysis-eac4-monthly No access to data or documentation. 814 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/normalised-difference-vegetation-index-1999-2020-raster-1 https://land.copernicus.eu/global/access Normalised Difference Vegetation Index 1999-2020 (raster 1 km), global, 10-daily - version 3 The Normalised Difference Vegetation Index (NDVI) is a widely used, dimensionless index that is indicative for vegetation density and is defined as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared bands, and Red to the reflectance in the red bands. 815 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/normalised-difference-vegetation-index-2020-present https://land.copernicus.eu/global/access Normalised Difference Vegetation Index 2020-present (raster 300 m), global, 10-daily - version 2 The Normalised Difference Vegetation Index (NDVI) is a widely used, dimensionless index that is indicative for vegetation density and is defined as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared bands, and Red to the reflectance in the red bands. 816 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/normalised-difference-vegetation-index-1998-2020-raster-1 http://land.copernicus.eu/global/products/NDVI Normalised Difference Vegetation Index 1998-2020 (raster 1 km), global, 10-daily - version 2 The Normalised Difference Vegetation Index (NDVI) is a widely used, dimensionless index that is indicative for vegetation density and is defined as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared bands, and Red to the reflectance in the red band. 817 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/nutrient-and-carbon-profiles-vertical-distribution http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MULTIOBS_GLO_BGC_NUTRIENTS_CARBON_PROFILES_MYNRT_015_009 Nutrient and carbon profiles vertical distribution Short description: This product consists of vertical profiles of the concentration of nutrients (nitrates, phosphates, and silicates) and carbonate system variables (total alkalinity, dissolved inorganic carbon, pH, and partial pressure of carbon dioxide), computed for each Argo float equipped with an oxygen sensor. The method called CANYON (Carbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network) is based on a neural network trained using high-quality nutrient data collected over the last 30 years (GLODAPv2 database, https://www.glodap.info/). The method is applied to each Argo float equipped with an oxygen sensor using as input the properties measured by the float (pressure, temperature, salinity, oxygen), and its date and position. https://www.glodap.info/ Product Citation: Please refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag…. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (product) :https://doi.org/10.48670/moi-00048 https://doi.org/10.48670/moi-00048 818 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/european-ground-motion-service-calibrated-2015-2021 https://egms.land.copernicus.eu/ European Ground Motion Service: Calibrated 2015-2021 (vector), Europe, yearly, Feb. 2023 The European Ground Motion Service (EGMS) is a component of the Copernicus Land Monitoring Service. EGMS provides consistent, regular, standardised, harmonised and reliable information regarding natural and anthropogenic ground motion phenomena over the Copernicus Participating States and across national borders, with millimetre accuracy. This set of metadata describes the second product level of EGMS: Calibrated. This product is considered the main EGMS product as it serves the needs of most users. It contains the same type of information as the Basic product (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/1a…), but the measurement points are referenced to a model derived from global navigation satellite system data. Thus, the measurements are not relative anymore and are considered as absolute. The calibrated product makes it possible to compare ground motion measurements from adjacent areas belonging to different products of the same level. https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/1a… EGMS Calibrated is visualised as a vector map of measurement points, colour-coded by average velocity, and distributed to users in comma-separated values format. Each point is associated with a time series of displacement, i.e. a plot with values of displacement per acquisition of the satellite. The product is generated for both ascending and descending orbits. 819 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/mediterranean-sea-biogeochemistry-analysis-and-forecast http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=MEDSEA_ANALYSISFORECAST_BGC_006_014 Mediterranean Sea Biogeochemistry Analysis and Forecast Short Description The biogeochemical analysis and forecasts for the Mediterranean Sea at 1/24° of horizontal resolution (ca. 4 km) are produced by means of the MedBFM4 model system. MedBFM4, which is run by OGS (IT), consists of the coupling of the multi-stream atmosphere radiative model OASIM, the multi-stream in-water radiative and tracer transport model OGSTM_BIOPTIMOD v4.3, and the biogeochemical flux model BFM v5. Additionally, MedBFM4 features the 3D variational data assimilation scheme 3DVAR-BIO v3.3 with the assimilation of surface chlorophyll (CMEMS-OCTAC NRT product) and of vertical profiles of chlorophyll, nitrate and oxygen (BGC-Argo floats provided by CORIOLIS DAC). The biogeochemical MedBFM system, which is forced by the NEMO-OceanVar model (MEDSEA_ANALYSIS_FORECAST_PHY_006_013 product run by CMCC), produces one day of hindcast and ten days of forecast (every day) and seven days of analysis (weekly on Tuesday). Salon, S., Cossarini, G., Bolzon, G., Feudale, L., Lazzari, P., Teruzzi, A., Solidoro, C., Crise, A., 2019. Marine Ecosystem forecasts: skill performance of the CMEMS Mediterranean Sea model system. Ocean Sci. Discuss. 1–35. https://doi.org/10.5194/os-2018-145 https://doi.org/10.5194/os-2018-145 ''Product Citation'': Please refer to our Technical FAQ for citing products. http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… ''DOI (Product)'': https://doi.org/10.25423/cmcc/medsea_analysisforecast_bgc_006_014_medbf… https://doi.org/10.25423/cmcc/medsea_analysisforecast_bgc_006_014_medbf… 820 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/dry-matter-productivity-1999-2020-raster-1-km-global-10 https://land.copernicus.eu/global/products/dmp Dry Matter Productivity 1999-2020 (raster 1 km), global, 10-daily - version 2 Dry matter Productivity (DMP) is an indication of the overall growth rate or dry biomass increase of the vegetation and is directly related to ecosystem Net Primary Productivity (NPP), however its units (kilograms of gross dry matter per hectare per day) are customized for agro-statistical purposes. Compared to the Gross DMP (GDMP), or its equivalent Gross Primary Productivity, the main difference lies in the inclusion of the autotrophic respiration. Like the FAPAR products that are used as input for the GDMP estimation, these GDMP products are provided in Near Real Time, with consolidations in the next six periods, or as offline product. 821 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/dry-matter-productivity-2014-present-raster-300-m-global https://land.copernicus.eu/global/products/dmp Dry Matter Productivity 2014-present (raster 300 m), global, 10-daily - version 1 Dry matter Productivity (DMP) is an indication of the overall growth rate or dry biomass increase of the vegetation and is directly related to ecosystem Net Primary Productivity (NPP), however its units (kilograms of gross dry matter per hectare per day) are customized for agro-statistical purposes. Compared to the Gross DMP (GDMP), or its equivalent Gross Primary Productivity, the main difference lies in the inclusion of the autotrophic respiration. Like the FAPAR products that are used as input for the GDMP estimation, these GDMP products are provided in Near Real Time, with consolidations in the next periods, or as offline product. 822 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/normalised-difference-vegetation-index-2014-2020-raster http://land.copernicus.eu/global/products/NDVI Normalised Difference Vegetation Index 2014-2020 (raster 300 m), global, 10-daily - version 1 The Normalised Difference Vegetation Index (NDVI) is a proxy to quantify the vegetation amount. It is defined as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared band, and Red to the reflectance in the red band. It is closely related to FAPAR and is little scale dependant. 823 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/black-sea-biogeochemistry-reanalysis http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=BLKSEA_REANALYSIS_BIO_007_005 Black Sea Biogeochemistry Reanalysis Short description: The biogeochemical reanalysis for the Black Sea is produced by the MAST/ULiege Production Unit by means of the BAMHBI biogeochemical model. The workflow runs on the CECI hpc infrastructure (Wallonia, Belgium). Please refer to our Technical FAQ for citing products.http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpag… DOI (product) :https://doi.org/10.25423/CMCC/BLKSEA_REANALYSIS_BIO_007_005_BAMHBI  https://doi.org/10.25423/CMCC/BLKSEA_REANALYSIS_BIO_007_005_BAMHBI 824 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-health-mosquito-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-health-mosquito-overview-web hidden-app-health-mosquito-overview-web Viewer application for dataset More details about the products are given in the Documentation section. 825 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-forestry-fireindex-detail-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-forestry-fireindex-detail-web hidden-app-forestry-fireindex-detail-web Viewer application for dataset More details about the products are given in the Documentation section. 826 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/hidden-app-forestry-fireindex-overview-web https://cds.climate.copernicus.eu/cdsapp#!/dataset/hidden-app-forestry-fireindex-overview-web hidden-app-forestry-fireindex-overview-web Viewer application for dataset More details about the products are given in the Documentation section. 827 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/global-ocean-gridded-normalized-measurement-noise-sea http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033 GLOBAL OCEAN GRIDDED NORMALIZED MEASUREMENT NOISE OF SEA LEVEL ANOMALIES Short description: In wavenumber spectra, the 1hz measurement error is the noise level estimated as the mean value of energy at high wavenumbers (below ~20km in term of wavelength). The 1hz noise level spatial distribution follows the instrumental white-noise linked to the Surface Wave Height but also connections with the backscatter coefficient. The full understanding of this hump of spectral energy (Dibarboure et al., 2013, Investigating short wavelength correlated errors on low-resolution mode altimetry, OSTST 2013 presentation) still remain to be achieved and overcome with new retracking, new editing strategy or new technology. DOI (product) :https://doi.org/10.48670/moi-00144 https://doi.org/10.48670/moi-00144 828 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-era51-complete https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5.1-complete reanalysis-era5.1-complete 829 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-atmospheric-composition-forecasts-test https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-atmospheric-composition-forecasts-test cams-global-atmospheric-composition-forecasts-test 830 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-reanalysis-eac4-external https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4-external cams-global-reanalysis-eac4-external 831 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-atmospheric-composition-forecasts-external https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-atmospheric-composition-forecasts-external cams-global-atmospheric-composition-forecasts-external 832 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-atmospheric-composition-forecasts-internal https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-atmospheric-composition-forecasts-internal cams-global-atmospheric-composition-forecasts-internal 833 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/cams-global-reanalysis-eac4-internal https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4-internal cams-global-reanalysis-eac4-internal 834 https://www.copernicus.eu/en/access-data/copernicus-services-catalogue/reanalysis-era5-complete-preliminary-back-extension https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-complete-preliminary-back-extension reanalysis-era5-complete-preliminary-back-extension