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   "source": [
    "# Search and Load CMIP6 Data via ESGF / OPeNDAP\n",
    "\n",
    "This notebooks shows how to search and load data via [Earth System Grid Federation](https://esgf.llnl.gov/) infrastructure. This infrastructure works great and is the foundation of the CMIP6 distribution system.\n",
    "\n",
    "The main technologies used here are the [ESGF search API](https://github.com/ESGF/esgf.github.io/wiki/ESGF_Search_REST_API), used to figure out what data we want, and [OPeNDAP](https://www.opendap.org/), a remote data access protocol over HTTP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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    "execution": {
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   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "xr.set_options(display_style='html')\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search using ESGF API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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   "source": [
    "#!/usr/bin/env python\n",
    "from __future__ import print_function\n",
    "import requests\n",
    "import xml.etree.ElementTree as ET\n",
    "import numpy\n",
    "\n",
    "# Author: Unknown\n",
    "# I got the original version from a word document published by ESGF\n",
    "# https://docs.google.com/document/d/1pxz1Kd3JHfFp8vR2JCVBfApbsHmbUQQstifhGNdc6U0/edit?usp=sharing\n",
    "\n",
    "# API AT: https://github.com/ESGF/esgf.github.io/wiki/ESGF_Search_REST_API#results-pagination\n",
    "\n",
    "def esgf_search(server=\"https://esgf-node.llnl.gov/esg-search/search\",\n",
    "                files_type=\"OPENDAP\", local_node=True, project=\"CMIP6\",\n",
    "                verbose=False, format=\"application%2Fsolr%2Bjson\",\n",
    "                use_csrf=False, **search):\n",
    "    client = requests.session()\n",
    "    payload = search\n",
    "    payload[\"project\"] = project\n",
    "    payload[\"type\"]= \"File\"\n",
    "    if local_node:\n",
    "        payload[\"distrib\"] = \"false\"\n",
    "    if use_csrf:\n",
    "        client.get(server)\n",
    "        if 'csrftoken' in client.cookies:\n",
    "            # Django 1.6 and up\n",
    "            csrftoken = client.cookies['csrftoken']\n",
    "        else:\n",
    "            # older versions\n",
    "            csrftoken = client.cookies['csrf']\n",
    "        payload[\"csrfmiddlewaretoken\"] = csrftoken\n",
    "\n",
    "    payload[\"format\"] = format\n",
    "\n",
    "    offset = 0\n",
    "    numFound = 10000\n",
    "    all_files = []\n",
    "    files_type = files_type.upper()\n",
    "    while offset < numFound:\n",
    "        payload[\"offset\"] = offset\n",
    "        url_keys = [] \n",
    "        for k in payload:\n",
    "            url_keys += [\"{}={}\".format(k, payload[k])]\n",
    "\n",
    "        url = \"{}/?{}\".format(server, \"&\".join(url_keys))\n",
    "        print(url)\n",
    "        r = client.get(url)\n",
    "        r.raise_for_status()\n",
    "        resp = r.json()[\"response\"]\n",
    "        numFound = int(resp[\"numFound\"])\n",
    "        resp = resp[\"docs\"]\n",
    "        offset += len(resp)\n",
    "        for d in resp:\n",
    "            if verbose:\n",
    "                for k in d:\n",
    "                    print(\"{}: {}\".format(k,d[k]))\n",
    "            url = d[\"url\"]\n",
    "            for f in d[\"url\"]:\n",
    "                sp = f.split(\"|\")\n",
    "                if sp[-1] == files_type:\n",
    "                    all_files.append(sp[0].split(\".html\")[0])\n",
    "    return sorted(all_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2020-10-08T16:07:13.597262Z"
    }
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&table_id=Amon&variable_id=tas&experiment_id=historical&institution_id=NCAR&source_id=CESM2&member_id=r10i1p1f1&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&table_id=Amon&variable_id=tas&experiment_id=historical&institution_id=NCAR&source_id=CESM2&member_id=r10i1p1f1&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_185001-189912.nc',\n",
       " 'http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_190001-194912.nc',\n",
       " 'http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_195001-199912.nc',\n",
       " 'http://aims3.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_200001-201412.nc',\n",
       " 'http://esgf-data.ucar.edu/thredds/dodsC/esg_dataroot/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_185001-189912.nc',\n",
       " 'http://esgf-data.ucar.edu/thredds/dodsC/esg_dataroot/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_190001-194912.nc',\n",
       " 'http://esgf-data.ucar.edu/thredds/dodsC/esg_dataroot/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_195001-199912.nc',\n",
       " 'http://esgf-data.ucar.edu/thredds/dodsC/esg_dataroot/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_200001-201412.nc',\n",
       " 'http://esgf-data3.diasjp.net/thredds/dodsC/esg_dataroot/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_185001-189912.nc',\n",
       " 'http://esgf-data3.diasjp.net/thredds/dodsC/esg_dataroot/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_190001-194912.nc',\n",
       " 'http://esgf-data3.diasjp.net/thredds/dodsC/esg_dataroot/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_195001-199912.nc',\n",
       " 'http://esgf-data3.diasjp.net/thredds/dodsC/esg_dataroot/CMIP6/CMIP/NCAR/CESM2/historical/r10i1p1f1/Amon/tas/gn/v20190313/tas_Amon_CESM2_historical_r10i1p1f1_gn_200001-201412.nc']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = esgf_search(activity_id='CMIP', table_id='Amon', variable_id='tas', experiment_id='historical',\n",
    "                  institution_id=\"NCAR\", source_id=\"CESM2\", member_id=\"r10i1p1f1\")\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data with Xarray\n",
    "\n",
    "These are OPeNDAP endpoints. Xarray, together with the netCDF4 python library, allow lazy loading."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2020-10-08T16:07:19.468973Z"
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/conventions.py:500: SerializationWarning: variable 'tas' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  decode_timedelta=decode_timedelta,\n"
     ]
    },
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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       ".xr-var-name span,\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "  grid-column: 1;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 192, lon: 288, nbnd: 2, time: 1980)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    time_bnds  (time, nbnd) object dask.array&lt;chunksize=(600, 2), meta=np.ndarray&gt;\n",
       "    lat_bnds   (time, lat, nbnd) float64 dask.array&lt;chunksize=(600, 192, 2), meta=np.ndarray&gt;\n",
       "    lon_bnds   (time, lon, nbnd) float64 dask.array&lt;chunksize=(600, 288, 2), meta=np.ndarray&gt;\n",
       "    tas        (time, lat, lon) float32 dask.array&lt;chunksize=(600, 192, 288), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:                     CF-1.7 CMIP-6.2\n",
       "    activity_id:                     CMIP\n",
       "    branch_method:                   standard\n",
       "    branch_time_in_child:            674885.0\n",
       "    branch_time_in_parent:           306600.0\n",
       "    case_id:                         24\n",
       "    cesm_casename:                   b.e21.BHIST.f09_g17.CMIP6-historical.010\n",
       "    contact:                         cesm_cmip6@ucar.edu\n",
       "    creation_date:                   2019-03-12T06:39:18Z\n",
       "    data_specs_version:              01.00.29\n",
       "    experiment:                      Simulation of recent past (1850 to 2014)...\n",
       "    experiment_id:                   historical\n",
       "    external_variables:              areacella\n",
       "    forcing_index:                   1\n",
       "    frequency:                       mon\n",
       "    further_info_url:                https://furtherinfo.es-doc.org/CMIP6.NCA...\n",
       "    grid:                            native 0.9x1.25 finite volume grid (192x...\n",
       "    grid_label:                      gn\n",
       "    initialization_index:            1\n",
       "    institution:                     National Center for Atmospheric Research...\n",
       "    institution_id:                  NCAR\n",
       "    license:                         CMIP6 model data produced by &lt;The Nation...\n",
       "    mip_era:                         CMIP6\n",
       "    model_doi_url:                   https://doi.org/10.5065/D67H1H0V\n",
       "    nominal_resolution:              100 km\n",
       "    parent_activity_id:              CMIP\n",
       "    parent_experiment_id:            piControl\n",
       "    parent_mip_era:                  CMIP6\n",
       "    parent_source_id:                CESM2\n",
       "    parent_time_units:               days since 0001-01-01 00:00:00\n",
       "    parent_variant_label:            r1i1p1f1\n",
       "    physics_index:                   1\n",
       "    product:                         model-output\n",
       "    realization_index:               10\n",
       "    realm:                           atmos\n",
       "    source:                          CESM2 (2017): atmosphere: CAM6 (0.9x1.25...\n",
       "    source_id:                       CESM2\n",
       "    source_type:                     AOGCM BGC\n",
       "    sub_experiment:                  none\n",
       "    sub_experiment_id:               none\n",
       "    table_id:                        Amon\n",
       "    tracking_id:                     hdl:21.14100/e47b79db-3925-45a7-9c0a-679...\n",
       "    variable_id:                     tas\n",
       "    variant_info:                    CMIP6 20th century experiments (1850-201...\n",
       "    variant_label:                   r10i1p1f1\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d2fd0fd3-e2f9-4c52-b354-39e61e72ffe8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d2fd0fd3-e2f9-4c52-b354-39e61e72ffe8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span>nbnd</span>: 2</li><li><span class='xr-has-index'>time</span>: 1980</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dd89ff90-895c-4900-b969-07d918f505d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dd89ff90-895c-4900-b969-07d918f505d6' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-b166ce71-e320-437f-b46b-c3653296c55e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b166ce71-e320-437f-b46b-c3653296c55e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63f756ca-f70b-420f-9732-405edcdb4d03' class='xr-var-data-in' type='checkbox'><label for='data-63f756ca-f70b-420f-9732-405edcdb4d03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>_ChunkSizes :</span></dt><dd>192</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-b874cee1-346e-46e4-8b9e-9228d80908c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b874cee1-346e-46e4-8b9e-9228d80908c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ef3be34-f8d8-43fd-9f3f-357abe075fae' class='xr-var-data-in' type='checkbox'><label for='data-7ef3be34-f8d8-43fd-9f3f-357abe075fae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>_ChunkSizes :</span></dt><dd>288</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-15 12:00:00 ... 2014-12-...</div><input id='attrs-f318398a-bfe1-4507-ba97-e39c7457b303' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f318398a-bfe1-4507-ba97-e39c7457b303' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00f89bea-103a-4a1b-ac11-5628bdd099d4' class='xr-var-data-in' type='checkbox'><label for='data-00f89bea-103a-4a1b-ac11-5628bdd099d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 15, 12, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4fcd0744-87dd-441a-b6f7-df45d0285c29' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4fcd0744-87dd-441a-b6f7-df45d0285c29' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(600, 2), meta=np.ndarray&gt;</div><input id='attrs-f136a72e-6b40-4a32-b282-5a24836de2c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f136a72e-6b40-4a32-b282-5a24836de2c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa3bac35-cf1f-4a12-8b7e-b452c3d92854' class='xr-var-data-in' type='checkbox'><label for='data-aa3bac35-cf1f-4a12-8b7e-b452c3d92854' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1 2]</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 31.68 kB </td> <td> 9.60 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1980, 2) </td> <td> (600, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 12 Tasks </td><td> 4 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> object </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"36\" x2=\"25\" y2=\"36\" />\n",
       "  <line x1=\"0\" y1=\"72\" x2=\"25\" y2=\"72\" />\n",
       "  <line x1=\"0\" y1=\"109\" x2=\"25\" y2=\"109\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.000000,0.000000 25.412617,0.000000 25.412617,120.000000 0.000000,120.000000\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">1980</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(time, lat, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(600, 192, 2), meta=np.ndarray&gt;</div><input id='attrs-01ef24bb-8919-4d69-98d3-cdd90c15ea58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01ef24bb-8919-4d69-98d3-cdd90c15ea58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20335d41-3113-4f51-8e51-d38eec1e7f86' class='xr-var-data-in' type='checkbox'><label for='data-20335d41-3113-4f51-8e51-d38eec1e7f86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>_ChunkSizes :</span></dt><dd>[192   2]</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 6.08 MB </td> <td> 1.84 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1980, 192, 2) </td> <td> (600, 192, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 16 Tasks </td><td> 4 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(time, lon, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(600, 288, 2), meta=np.ndarray&gt;</div><input id='attrs-d023a98b-3261-40f0-9849-30846196d5c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d023a98b-3261-40f0-9849-30846196d5c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21835ebb-f4da-4852-9e1f-988f90770800' class='xr-var-data-in' type='checkbox'><label for='data-21835ebb-f4da-4852-9e1f-988f90770800' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>_ChunkSizes :</span></dt><dd>[288   2]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(600, 192, 288), meta=np.ndarray&gt;</div><input id='attrs-efc51e13-065f-4d85-85bf-a57694c81059' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efc51e13-065f-4d85-85bf-a57694c81059' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7db47244-33f8-40c5-9d13-a1ef6784d1d9' class='xr-var-data-in' type='checkbox'><label for='data-7db47244-33f8-40c5-9d13-a1ef6784d1d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>description :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>id :</span></dt><dd>tas</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>mipTable :</span></dt><dd>Amon</dd><dt><span>out_name :</span></dt><dd>tas</dd><dt><span>prov :</span></dt><dd>Amon ((isd.003))</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>time :</span></dt><dd>time</dd><dt><span>time_label :</span></dt><dd>time-mean</dd><dt><span>time_title :</span></dt><dd>Temporal mean</dd><dt><span>title :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1 192 288]</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-72b3862f-1b23-4a77-923f-b304bd8a35f1' class='xr-section-summary-in' type='checkbox'  ><label for='section-72b3862f-1b23-4a77-923f-b304bd8a35f1' class='xr-section-summary' >Attributes: <span>(46)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>674885.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>306600.0</dd><dt><span>case_id :</span></dt><dd>24</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BHIST.f09_g17.CMIP6-historical.010</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-03-12T06:39:18Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>experiment :</span></dt><dd>Simulation of recent past (1850 to 2014). Impose changing conditions (consistent with observations). Should be initialised from a point early enough in the pre-industrial control run to ensure that the end of all the perturbed runs branching from the end of this historical run end before the end of the control. Only one ensemble member is requested but modelling groups are strongly encouraged to submit at least three ensemble members of their CMIP historical simulation. </dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native 0.9x1.25 finite volume grid (192x288 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/e47b79db-3925-45a7-9c0a-6799c2f1e8ae</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_info :</span></dt><dd>CMIP6 20th century experiments (1850-2014) with CAM6, interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)\r\n",
       "</dd><dt><span>variant_label :</span></dt><dd>r10i1p1f1</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 192, lon: 288, nbnd: 2, time: 1980)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    time_bnds  (time, nbnd) object dask.array<chunksize=(600, 2), meta=np.ndarray>\n",
       "    lat_bnds   (time, lat, nbnd) float64 dask.array<chunksize=(600, 192, 2), meta=np.ndarray>\n",
       "    lon_bnds   (time, lon, nbnd) float64 dask.array<chunksize=(600, 288, 2), meta=np.ndarray>\n",
       "    tas        (time, lat, lon) float32 dask.array<chunksize=(600, 192, 288), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:                     CF-1.7 CMIP-6.2\n",
       "    activity_id:                     CMIP\n",
       "    branch_method:                   standard\n",
       "    branch_time_in_child:            674885.0\n",
       "    branch_time_in_parent:           306600.0\n",
       "    case_id:                         24\n",
       "    cesm_casename:                   b.e21.BHIST.f09_g17.CMIP6-historical.010\n",
       "    contact:                         cesm_cmip6@ucar.edu\n",
       "    creation_date:                   2019-03-12T06:39:18Z\n",
       "    data_specs_version:              01.00.29\n",
       "    experiment:                      Simulation of recent past (1850 to 2014)...\n",
       "    experiment_id:                   historical\n",
       "    external_variables:              areacella\n",
       "    forcing_index:                   1\n",
       "    frequency:                       mon\n",
       "    further_info_url:                https://furtherinfo.es-doc.org/CMIP6.NCA...\n",
       "    grid:                            native 0.9x1.25 finite volume grid (192x...\n",
       "    grid_label:                      gn\n",
       "    initialization_index:            1\n",
       "    institution:                     National Center for Atmospheric Research...\n",
       "    institution_id:                  NCAR\n",
       "    license:                         CMIP6 model data produced by <The Nation...\n",
       "    mip_era:                         CMIP6\n",
       "    model_doi_url:                   https://doi.org/10.5065/D67H1H0V\n",
       "    nominal_resolution:              100 km\n",
       "    parent_activity_id:              CMIP\n",
       "    parent_experiment_id:            piControl\n",
       "    parent_mip_era:                  CMIP6\n",
       "    parent_source_id:                CESM2\n",
       "    parent_time_units:               days since 0001-01-01 00:00:00\n",
       "    parent_variant_label:            r1i1p1f1\n",
       "    physics_index:                   1\n",
       "    product:                         model-output\n",
       "    realization_index:               10\n",
       "    realm:                           atmos\n",
       "    source:                          CESM2 (2017): atmosphere: CAM6 (0.9x1.25...\n",
       "    source_id:                       CESM2\n",
       "    source_type:                     AOGCM BGC\n",
       "    sub_experiment:                  none\n",
       "    sub_experiment_id:               none\n",
       "    table_id:                        Amon\n",
       "    tracking_id:                     hdl:21.14100/e47b79db-3925-45a7-9c0a-679...\n",
       "    variable_id:                     tas\n",
       "    variant_info:                    CMIP6 20th century experiments (1850-201...\n",
       "    variant_label:                   r10i1p1f1\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are mulitple sources of the same data--need to pick one\n",
    "files_to_open = result[-4:]\n",
    "\n",
    "ds = xr.open_mfdataset(files_to_open, combine='by_coords')\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a map from a specific date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:07:19.481580Z",
     "iopub.status.busy": "2020-10-08T16:07:19.480486Z",
     "iopub.status.idle": "2020-10-08T16:07:20.640389Z",
     "shell.execute_reply": "2020-10-08T16:07:20.641036Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f511a086f10>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 402
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.tas.sel(time='1950-01').squeeze().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a timeseries of global-average surface air temperature. For this we need the area weighting factor for each gridpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:07:20.648843Z",
     "iopub.status.busy": "2020-10-08T16:07:20.648206Z",
     "iopub.status.idle": "2020-10-08T16:07:22.056437Z",
     "shell.execute_reply": "2020-10-08T16:07:22.055781Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://esgf-node.llnl.gov/esg-search/search/?variable_id=areacella&activity_id=CMIP&experiment_id=historical&institution_id=NCAR&source_id=CESM2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://esgf-node.llnl.gov/esg-search/search/?variable_id=areacella&activity_id=CMIP&experiment_id=historical&institution_id=NCAR&source_id=CESM2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=10\n",
      "https://esgf-node.llnl.gov/esg-search/search/?variable_id=areacella&activity_id=CMIP&experiment_id=historical&institution_id=NCAR&source_id=CESM2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=20\n",
      "https://esgf-node.llnl.gov/esg-search/search/?variable_id=areacella&activity_id=CMIP&experiment_id=historical&institution_id=NCAR&source_id=CESM2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=30\n"
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     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/conventions.py:500: SerializationWarning: variable 'areacella' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 192, lon: 288, nbnd: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    lat_bnds   (lat, nbnd) float64 -90.0 -89.53 -89.53 ... 89.53 89.53 90.0\n",
       "    lon_bnds   (lon, nbnd) float64 -0.625 0.625 0.625 ... 358.1 358.1 359.4\n",
       "    areacella  (lat, lon) float32 29948368.0 29948368.0 ... 29948368.0\n",
       "Attributes:\n",
       "    _NCProperties:          version=1|netcdflibversion=4.4.1.1|hdf5libversion...\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          standard\n",
       "    branch_time_in_child:   674885.0\n",
       "    branch_time_in_parent:  306600.0\n",
       "    case_id:                24\n",
       "    cesm_casename:          b.e21.BHIST.f09_g17.CMIP6-historical.010\n",
       "    contact:                cesm_cmip6@ucar.edu\n",
       "    creation_date:          2019-03-12T06:40:59Z\n",
       "    data_specs_version:     01.00.29\n",
       "    experiment:             Simulation of recent past (1850 to 2014). Impose ...\n",
       "    experiment_id:          historical\n",
       "    forcing_index:          1\n",
       "    frequency:              fx\n",
       "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.h...\n",
       "    grid:                   native 0.9x1.25 finite volume grid (192x288 latxlon)\n",
       "    grid_label:             gn\n",
       "    initialization_index:   1\n",
       "    institution:            National Center for Atmospheric Research, Climate...\n",
       "    institution_id:         NCAR\n",
       "    license:                CMIP6 model data produced by &lt;The National Center...\n",
       "    mip_era:                CMIP6\n",
       "    model_doi_url:          https://doi.org/10.5065/D67H1H0V\n",
       "    nominal_resolution:     100 km\n",
       "    parent_activity_id:     CMIP\n",
       "    parent_experiment_id:   piControl\n",
       "    parent_mip_era:         CMIP6\n",
       "    parent_source_id:       CESM2\n",
       "    parent_time_units:      days since 0001-01-01 00:00:00\n",
       "    parent_variant_label:   r1i1p1f1\n",
       "    physics_index:          1\n",
       "    product:                model-output\n",
       "    realization_index:      10\n",
       "    realm:                  atmos land\n",
       "    source:                 CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite v...\n",
       "    source_id:              CESM2\n",
       "    source_type:            AOGCM BGC\n",
       "    sub_experiment:         none\n",
       "    sub_experiment_id:      none\n",
       "    table_id:               fx\n",
       "    tracking_id:            hdl:21.14100/54fe4682-51a1-43ca-a7d5-66b7bca3f8c8\n",
       "    variable_id:            areacella\n",
       "    variant_info:           CMIP6 20th century experiments (1850-2014) with C...\n",
       "    variant_label:          r10i1p1f1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1596f5a4-b719-4e33-9c2f-95c00eb9d53b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1596f5a4-b719-4e33-9c2f-95c00eb9d53b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-23980c85-6de5-415a-afb6-ee00c786c83a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-23980c85-6de5-415a-afb6-ee00c786c83a' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-22ac2242-5a72-4a12-b798-136d25e2f4b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22ac2242-5a72-4a12-b798-136d25e2f4b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d6b2e77-b8ee-4b15-94b3-e71ce1d9fc31' class='xr-var-data-in' type='checkbox'><label for='data-3d6b2e77-b8ee-4b15-94b3-e71ce1d9fc31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>_ChunkSizes :</span></dt><dd>192</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-20cc5e47-d231-4234-b4a4-172c8948e525' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20cc5e47-d231-4234-b4a4-172c8948e525' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-908deae9-7900-49c2-8658-47877b412a25' class='xr-var-data-in' type='checkbox'><label for='data-908deae9-7900-49c2-8658-47877b412a25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>_ChunkSizes :</span></dt><dd>288</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81c6cc24-1705-481c-9c12-8a23901e09cf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-81c6cc24-1705-481c-9c12-8a23901e09cf' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f1a7bfa5-f4af-4876-9345-df60ee4d0420' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1a7bfa5-f4af-4876-9345-df60ee4d0420' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8191a990-64de-4eef-8621-36f36838af0f' class='xr-var-data-in' type='checkbox'><label for='data-8191a990-64de-4eef-8621-36f36838af0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>_ChunkSizes :</span></dt><dd>[192   2]</dd></dl></div><div class='xr-var-data'><pre>array([[-90.      , -89.528796],\n",
       "       [-89.528796, -88.586387],\n",
       "       [-88.586387, -87.643979],\n",
       "       ...,\n",
       "       [ 87.643979,  88.586387],\n",
       "       [ 88.586387,  89.528796],\n",
       "       [ 89.528796,  90.      ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-35bdefa3-51e7-4695-8c83-9d612d3c2daa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35bdefa3-51e7-4695-8c83-9d612d3c2daa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba505525-3fe5-4c33-b8b0-1737dc33666e' class='xr-var-data-in' type='checkbox'><label for='data-ba505525-3fe5-4c33-b8b0-1737dc33666e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>_ChunkSizes :</span></dt><dd>[288   2]</dd></dl></div><div class='xr-var-data'><pre>array([[ -0.625,   0.625],\n",
       "       [  0.625,   1.875],\n",
       "       [  1.875,   3.125],\n",
       "       ...,\n",
       "       [355.625, 356.875],\n",
       "       [356.875, 358.125],\n",
       "       [358.125, 359.375]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>areacella</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-44bdea96-17ba-4b69-9d56-27e441dd1623' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44bdea96-17ba-4b69-9d56-27e441dd1623' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-759f682c-5319-46eb-b06f-489d8b9945ed' class='xr-var-data-in' type='checkbox'><label for='data-759f682c-5319-46eb-b06f-489d8b9945ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: sum</dd><dt><span>comment :</span></dt><dd>Cell areas for any grid used to report atmospheric variables and any other variable using that grid (e.g., soil moisture content). These cell areas should be defined to enable exact calculation of global integrals (e.g., of vertical fluxes of energy at the surface and top of the atmosphere).</dd><dt><span>description :</span></dt><dd>Cell areas for any grid used to report atmospheric variables and any other variable using that grid (e.g., soil moisture content). These cell areas should be defined to enable exact calculation of global integrals (e.g., of vertical fluxes of energy at the surface and top of the atmosphere).</dd><dt><span>frequency :</span></dt><dd>fx</dd><dt><span>id :</span></dt><dd>areacella</dd><dt><span>long_name :</span></dt><dd>Grid-Cell Area for Atmospheric Grid Variables</dd><dt><span>mipTable :</span></dt><dd>fx</dd><dt><span>out_name :</span></dt><dd>areacella</dd><dt><span>prov :</span></dt><dd>fx ((isd.003))</dd><dt><span>realm :</span></dt><dd>atmos land</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>time_label :</span></dt><dd>None</dd><dt><span>time_title :</span></dt><dd>No temporal dimensions ... fixed field</dd><dt><span>title :</span></dt><dd>Grid-Cell Area for Atmospheric Grid Variables</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>variable_id :</span></dt><dd>areacella</dd><dt><span>_ChunkSizes :</span></dt><dd>[192 288]</dd></dl></div><div class='xr-var-data'><pre>array([[2.994837e+07, 2.994837e+07, 2.994837e+07, ..., 2.994837e+07,\n",
       "        2.994837e+07, 2.994837e+07],\n",
       "       [2.395748e+08, 2.395748e+08, 2.395748e+08, ..., 2.395748e+08,\n",
       "        2.395748e+08, 2.395748e+08],\n",
       "       [4.790848e+08, 4.790848e+08, 4.790848e+08, ..., 4.790848e+08,\n",
       "        4.790848e+08, 4.790848e+08],\n",
       "       ...,\n",
       "       [4.790848e+08, 4.790848e+08, 4.790848e+08, ..., 4.790848e+08,\n",
       "        4.790848e+08, 4.790848e+08],\n",
       "       [2.395748e+08, 2.395748e+08, 2.395748e+08, ..., 2.395748e+08,\n",
       "        2.395748e+08, 2.395748e+08],\n",
       "       [2.994837e+07, 2.994837e+07, 2.994837e+07, ..., 2.994837e+07,\n",
       "        2.994837e+07, 2.994837e+07]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7d62fa16-629a-47ac-9fa7-7e0a81d108ac' class='xr-section-summary-in' type='checkbox'  ><label for='section-7d62fa16-629a-47ac-9fa7-7e0a81d108ac' class='xr-section-summary' >Attributes: <span>(45)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>_NCProperties :</span></dt><dd>version=1|netcdflibversion=4.4.1.1|hdf5libversion=1.8.18</dd><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>674885.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>306600.0</dd><dt><span>case_id :</span></dt><dd>24</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BHIST.f09_g17.CMIP6-historical.010</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-03-12T06:40:59Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>experiment :</span></dt><dd>Simulation of recent past (1850 to 2014). Impose changing conditions (consistent with observations). Should be initialised from a point early enough in the pre-industrial control run to ensure that the end of all the perturbed runs branching from the end of this historical run end before the end of the control. Only one ensemble member is requested but modelling groups are strongly encouraged to submit at least three ensemble members of their CMIP historical simulation. </dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>fx</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.historical.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native 0.9x1.25 finite volume grid (192x288 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos land</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>fx</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/54fe4682-51a1-43ca-a7d5-66b7bca3f8c8</dd><dt><span>variable_id :</span></dt><dd>areacella</dd><dt><span>variant_info :</span></dt><dd>CMIP6 20th century experiments (1850-2014) with CAM6, interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)\r\n",
       "</dd><dt><span>variant_label :</span></dt><dd>r10i1p1f1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 192, lon: 288, nbnd: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    lat_bnds   (lat, nbnd) float64 ...\n",
       "    lon_bnds   (lon, nbnd) float64 ...\n",
       "    areacella  (lat, lon) float32 ...\n",
       "Attributes:\n",
       "    _NCProperties:          version=1|netcdflibversion=4.4.1.1|hdf5libversion...\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          standard\n",
       "    branch_time_in_child:   674885.0\n",
       "    branch_time_in_parent:  306600.0\n",
       "    case_id:                24\n",
       "    cesm_casename:          b.e21.BHIST.f09_g17.CMIP6-historical.010\n",
       "    contact:                cesm_cmip6@ucar.edu\n",
       "    creation_date:          2019-03-12T06:40:59Z\n",
       "    data_specs_version:     01.00.29\n",
       "    experiment:             Simulation of recent past (1850 to 2014). Impose ...\n",
       "    experiment_id:          historical\n",
       "    forcing_index:          1\n",
       "    frequency:              fx\n",
       "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.h...\n",
       "    grid:                   native 0.9x1.25 finite volume grid (192x288 latxlon)\n",
       "    grid_label:             gn\n",
       "    initialization_index:   1\n",
       "    institution:            National Center for Atmospheric Research, Climate...\n",
       "    institution_id:         NCAR\n",
       "    license:                CMIP6 model data produced by <The National Center...\n",
       "    mip_era:                CMIP6\n",
       "    model_doi_url:          https://doi.org/10.5065/D67H1H0V\n",
       "    nominal_resolution:     100 km\n",
       "    parent_activity_id:     CMIP\n",
       "    parent_experiment_id:   piControl\n",
       "    parent_mip_era:         CMIP6\n",
       "    parent_source_id:       CESM2\n",
       "    parent_time_units:      days since 0001-01-01 00:00:00\n",
       "    parent_variant_label:   r1i1p1f1\n",
       "    physics_index:          1\n",
       "    product:                model-output\n",
       "    realization_index:      10\n",
       "    realm:                  atmos land\n",
       "    source:                 CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite v...\n",
       "    source_id:              CESM2\n",
       "    source_type:            AOGCM BGC\n",
       "    sub_experiment:         none\n",
       "    sub_experiment_id:      none\n",
       "    table_id:               fx\n",
       "    tracking_id:            hdl:21.14100/54fe4682-51a1-43ca-a7d5-66b7bca3f8c8\n",
       "    variable_id:            areacella\n",
       "    variant_info:           CMIP6 20th century experiments (1850-2014) with C...\n",
       "    variant_label:          r10i1p1f1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files_area = esgf_search(variable_id='areacella', activity_id='CMIP',\n",
    "                         experiment_id='historical', institution_id=\"NCAR\", source_id=\"CESM2\")\n",
    "ds_area = xr.open_dataset(files_area[0])\n",
    "ds_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:07:22.062361Z",
     "iopub.status.busy": "2020-10-08T16:07:22.061288Z",
     "iopub.status.idle": "2020-10-08T16:07:22.086267Z",
     "shell.execute_reply": "2020-10-08T16:07:22.085610Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 1980)&gt;\n",
       "dask.array&lt;truediv, shape=(1980,), dtype=float32, chunksize=(600,), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c0189c73-3feb-42ad-87ea-381bcd839352' class='xr-array-in' type='checkbox' checked><label for='section-c0189c73-3feb-42ad-87ea-381bcd839352' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(600,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 7.92 kB </td> <td> 2.40 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1980,) </td> <td> (600,) </td></tr>\n",
       "    <tr><th> Count </th><td> 38 Tasks </td><td> 4 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1980</text>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-b2660d5a-36b1-4d22-8ca7-3750f2fd276d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b2660d5a-36b1-4d22-8ca7-3750f2fd276d' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-15 12:00:00 ... 2014-12-...</div><input id='attrs-95cf1921-7c11-42b6-8330-85fabaced60f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95cf1921-7c11-42b6-8330-85fabaced60f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d526ab1-e9da-4c6e-a437-530dd6d40cc6' class='xr-var-data-in' type='checkbox'><label for='data-1d526ab1-e9da-4c6e-a437-530dd6d40cc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 15, 12, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1a3ea64a-7543-477d-b0f9-0cd2607507fb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1a3ea64a-7543-477d-b0f9-0cd2607507fb' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (time: 1980)>\n",
       "dask.array<truediv, shape=(1980,), dtype=float32, chunksize=(600,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_area = ds_area.areacella.sum(dim=['lon', 'lat'])\n",
    "ta_timeseries = (ds.tas * ds_area.areacella).sum(dim=['lon', 'lat']) / total_area\n",
    "ta_timeseries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default the data are loaded lazily, as Dask arrays. Here we trigger computation explicitly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:07:22.095559Z",
     "iopub.status.busy": "2020-10-08T16:07:22.094436Z",
     "iopub.status.idle": "2020-10-08T16:08:14.914152Z",
     "shell.execute_reply": "2020-10-08T16:08:14.913454Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.35 s, sys: 1.48 s, total: 4.82 s\n",
      "Wall time: 52.8 s\n"
     ]
    },
    {
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       "\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 1980)&gt;\n",
       "array([284.99948, 285.23215, 285.85364, ..., 288.54376, 287.61884,\n",
       "       287.06284], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6982ab7d-c6e8-4f45-a88d-ee30c9a1400e' class='xr-array-in' type='checkbox' checked><label for='section-6982ab7d-c6e8-4f45-a88d-ee30c9a1400e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>284.99948 285.23215 285.85364 ... 288.54376 287.61884 287.06284</span></div><div class='xr-array-data'><pre>array([284.99948, 285.23215, 285.85364, ..., 288.54376, 287.61884,\n",
       "       287.06284], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-1e81f15a-a71a-4bda-b158-da0ec2a8169c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1e81f15a-a71a-4bda-b158-da0ec2a8169c' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-15 12:00:00 ... 2014-12-...</div><input id='attrs-cdc77dd9-30fd-4481-bcf4-3ca51378e082' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cdc77dd9-30fd-4481-bcf4-3ca51378e082' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f48f537a-0fe8-4271-ba34-bb72207ed999' class='xr-var-data-in' type='checkbox'><label for='data-f48f537a-0fe8-4271-ba34-bb72207ed999' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>_ChunkSizes :</span></dt><dd>512</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 15, 12, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-44012beb-2e88-444a-992a-01e911a76664' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-44012beb-2e88-444a-992a-01e911a76664' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (time: 1980)>\n",
       "array([284.99948, 285.23215, 285.85364, ..., 288.54376, 287.61884,\n",
       "       287.06284], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time ta_timeseries.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:08:14.956856Z",
     "iopub.status.busy": "2020-10-08T16:08:14.937785Z",
     "iopub.status.idle": "2020-10-08T16:08:15.199950Z",
     "shell.execute_reply": "2020-10-08T16:08:15.200448Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Global Mean Surface Air Temperature')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 375
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ta_timeseries.plot(label='monthly')\n",
    "ta_timeseries.rolling(time=12).mean().plot(label='12 month rolling mean')\n",
    "plt.legend()\n",
    "plt.title('Global Mean Surface Air Temperature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:08:15.206413Z",
     "iopub.status.busy": "2020-10-08T16:08:15.205472Z",
     "iopub.status.idle": "2020-10-08T16:08:15.209439Z",
     "shell.execute_reply": "2020-10-08T16:08:15.210116Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.330052"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.nbytes / 1e6 / 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}