{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using masks and computing weighted average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example is based from xarray example http://xarray.pydata.org/en/stable/examples/area_weighted_temperature.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import python packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nc-time-axis in /opt/conda/lib/python3.8/site-packages (1.4.1)\n",
      "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.8/site-packages (from nc-time-axis) (3.5.1)\n",
      "Requirement already satisfied: cftime>=1.5 in /opt/conda/lib/python3.8/site-packages (from nc-time-axis) (1.6.0)\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from nc-time-axis) (1.19.5)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.8/site-packages (from matplotlib->nc-time-axis) (0.10.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib->nc-time-axis) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.8/site-packages (from matplotlib->nc-time-axis) (2.8.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.8/site-packages (from matplotlib->nc-time-axis) (4.30.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.8/site-packages (from matplotlib->nc-time-axis) (8.1.2)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.8/site-packages (from matplotlib->nc-time-axis) (20.9)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib->nc-time-axis) (1.3.1)\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from cycler>=0.10->matplotlib->nc-time-axis) (1.15.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install nc-time-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "xr.set_options(display_style='html')\n",
    "import intake\n",
    "import cftime\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>pangeo-cmip6 catalog with 7686 dataset(s) from 514961 asset(s)</strong>:</p> <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activity_id</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>170</td>\n",
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       "      <td>657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table_id</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_id</th>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_label</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zstore</th>\n",
       "      <td>514961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_url = \"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\"\n",
    "col = intake.open_esm_datastore(cat_url)\n",
    "col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Search data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>table_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>zstore</th>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>NCC</td>\n",
       "      <td>NorESM2-LM</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NCC/NorESM2-LM/historica...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  activity_id institution_id   source_id experiment_id member_id table_id  \\\n",
       "0        CMIP            NCC  NorESM2-LM    historical  r1i1p1f1     Amon   \n",
       "\n",
       "  variable_id grid_label                                             zstore  \\\n",
       "0         tas         gn  gs://cmip6/CMIP6/CMIP/NCC/NorESM2-LM/historica...   \n",
       "\n",
       "   dcpp_init_year   version  \n",
       "0             NaN  20190815  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat = col.search(source_id=['NorESM2-LM'], experiment_id=['historical'], table_id=['Amon'], variable_id=['tas'], member_id=['r1i1p1f1'])\n",
    "cat.df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create dictionary from the list of datasets we found\n",
    "- This step may take several minutes so be patient!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
     ]
    },
    {
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   "source": [
    "dset_dict = cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CMIP.NCC.NorESM2-LM.historical.Amon.gn']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "list(dset_dict.keys())"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 96, bnds: 2, lon: 144, member_id: 1, time: 1980)\n",
       "Coordinates:\n",
       "    height     float64 ...\n",
       "  * lat        (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n",
       "    lat_bnds   (lat, bnds) float64 dask.array&lt;chunksize=(96, 2), meta=np.ndarray&gt;\n",
       "  * lon        (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
       "    lon_bnds   (lon, bnds) float64 dask.array&lt;chunksize=(144, 2), meta=np.ndarray&gt;\n",
       "  * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "    time_bnds  (time, bnds) object dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;\n",
       "  * member_id  (member_id) &lt;U8 &#x27;r1i1p1f1&#x27;\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    tas        (member_id, time, lat, lon) float32 dask.array&lt;chunksize=(1, 990, 96, 144), meta=np.ndarray&gt;\n",
       "Attributes: (12/54)\n",
       "    Conventions:               CF-1.7 CMIP-6.2\n",
       "    activity_id:               CMIP\n",
       "    branch_method:             Hybrid-restart from year 1600-01-01 of piControl\n",
       "    branch_time:               0.0\n",
       "    branch_time_in_child:      0.0\n",
       "    branch_time_in_parent:     430335.0\n",
       "    ...                        ...\n",
       "    variable_id:               tas\n",
       "    variant_label:             r1i1p1f1\n",
       "    netcdf_tracking_ids:       hdl:21.14100/2486cf87-033c-4848-ab3e-e828c3b7c...\n",
       "    version_id:                v20190815\n",
       "    intake_esm_varname:        [&#x27;tas&#x27;]\n",
       "    intake_esm_dataset_key:    CMIP.NCC.NorESM2-LM.historical.Amon.gn</pre><div class='xr-wrap' style='display:none'><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-30d90a86-b511-4f5c-a81b-a276d343bc76' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-30d90a86-b511-4f5c-a81b-a276d343bc76' 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>: 96</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lon</span>: 144</li><li><span class='xr-has-index'>member_id</span>: 1</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-9dd580dc-aa9f-4f1e-8a61-c2ccf310b969' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9dd580dc-aa9f-4f1e-8a61-c2ccf310b969' class='xr-section-summary' >Coordinates: <span>(8)</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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8135e7b0-6295-4bfb-abd6-58f5e7bef979' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8135e7b0-6295-4bfb-abd6-58f5e7bef979' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-964acdb6-a641-4c00-83b8-8cb5d632ac16' class='xr-var-data-in' type='checkbox'><label for='data-964acdb6-a641-4c00-83b8-8cb5d632ac16' 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>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li><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 -88.11 -86.21 ... 88.11 90.0</div><input id='attrs-454fed01-cb28-4b82-b846-e4188fb8654d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-454fed01-cb28-4b82-b846-e4188fb8654d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8abc832-94db-4d67-af93-6330c76fea68' class='xr-var-data-in' type='checkbox'><label for='data-f8abc832-94db-4d67-af93-6330c76fea68' 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>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -88.105263, -86.210526, -84.315789, -82.421053, -80.526316,\n",
       "       -78.631579, -76.736842, -74.842105, -72.947368, -71.052632, -69.157895,\n",
       "       -67.263158, -65.368421, -63.473684, -61.578947, -59.684211, -57.789474,\n",
       "       -55.894737, -54.      , -52.105263, -50.210526, -48.315789, -46.421053,\n",
       "       -44.526316, -42.631579, -40.736842, -38.842105, -36.947368, -35.052632,\n",
       "       -33.157895, -31.263158, -29.368421, -27.473684, -25.578947, -23.684211,\n",
       "       -21.789474, -19.894737, -18.      , -16.105263, -14.210526, -12.315789,\n",
       "       -10.421053,  -8.526316,  -6.631579,  -4.736842,  -2.842105,  -0.947368,\n",
       "         0.947368,   2.842105,   4.736842,   6.631579,   8.526316,  10.421053,\n",
       "        12.315789,  14.210526,  16.105263,  18.      ,  19.894737,  21.789474,\n",
       "        23.684211,  25.578947,  27.473684,  29.368421,  31.263158,  33.157895,\n",
       "        35.052632,  36.947368,  38.842105,  40.736842,  42.631579,  44.526316,\n",
       "        46.421053,  48.315789,  50.210526,  52.105263,  54.      ,  55.894737,\n",
       "        57.789474,  59.684211,  61.578947,  63.473684,  65.368421,  67.263158,\n",
       "        69.157895,  71.052632,  72.947368,  74.842105,  76.736842,  78.631579,\n",
       "        80.526316,  82.421053,  84.315789,  86.210526,  88.105263,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(96, 2), meta=np.ndarray&gt;</div><input id='attrs-09e4ccd3-c0a4-4ce0-9edf-63727f9360eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09e4ccd3-c0a4-4ce0-9edf-63727f9360eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2b63f51-f989-4331-94af-611287fadc91' class='xr-var-data-in' type='checkbox'><label for='data-d2b63f51-f989-4331-94af-611287fadc91' 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'></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> 1.50 kiB </td> <td> 1.50 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (96, 2) </td> <td> (96, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 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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       "\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></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 2.5 5.0 ... 352.5 355.0 357.5</div><input id='attrs-64844de5-fb7f-4697-9b09-5c2c88422736' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64844de5-fb7f-4697-9b09-5c2c88422736' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b0675c5-12bb-43d8-955d-f973b1d361f6' class='xr-var-data-in' type='checkbox'><label for='data-5b0675c5-12bb-43d8-955d-f973b1d361f6' 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>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,\n",
       "        25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,\n",
       "        50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,\n",
       "        75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,\n",
       "       100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,\n",
       "       125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,\n",
       "       150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,\n",
       "       175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,\n",
       "       200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
       "       325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,\n",
       "       350. , 352.5, 355. , 357.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(144, 2), meta=np.ndarray&gt;</div><input id='attrs-a577575d-0832-4c72-b163-c24e013a52d7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a577575d-0832-4c72-b163-c24e013a52d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d3b6d89-c745-48fc-883e-12b81527eb7c' class='xr-var-data-in' type='checkbox'><label for='data-2d3b6d89-c745-48fc-883e-12b81527eb7c' 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'></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> 2.25 kiB </td> <td> 2.25 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (144, 2) </td> <td> (144, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 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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       "\n",
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       "  <text x=\"13.527018\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"47.054036\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,47.054036,60.000000)\">144</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></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'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-ad344a40-1de0-4d46-89cf-324c7c734d27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad344a40-1de0-4d46-89cf-324c7c734d27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0da325b2-b5bb-4c99-96d1-7d65cd25a45f' class='xr-var-data-in' type='checkbox'><label for='data-0da325b2-b5bb-4c99-96d1-7d65cd25a45f' 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>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-05616a8a-375f-4645-ac36-f76072a4910f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05616a8a-375f-4645-ac36-f76072a4910f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae2c13dc-a8c0-497d-97c5-3824edac977e' class='xr-var-data-in' type='checkbox'><label for='data-ae2c13dc-a8c0-497d-97c5-3824edac977e' 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'></dl></div><div class='xr-var-data'><table>\n",
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       "<td>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 30.94 kiB </td> <td> 30.94 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1980, 2) </td> <td> (1980, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-344ea686-aa52-4fc8-adac-6c54a1c219ea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-344ea686-aa52-4fc8-adac-6c54a1c219ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53e470c8-5545-4b12-8e5a-820ab1ae91e2' class='xr-var-data-in' type='checkbox'><label for='data-53e470c8-5545-4b12-8e5a-820ab1ae91e2' 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'></dl></div><div class='xr-var-data'><pre>array([&#x27;r1i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-caa332f6-7dd9-46cf-abff-ba5849396458' class='xr-section-summary-in' type='checkbox'  checked><label for='section-caa332f6-7dd9-46cf-abff-ba5849396458' class='xr-section-summary' >Data variables: <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>tas</span></div><div class='xr-var-dims'>(member_id, time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 990, 96, 144), meta=np.ndarray&gt;</div><input id='attrs-bd511603-d040-4e35-9668-ea51d6744fb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd511603-d040-4e35-9668-ea51d6744fb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2ea1019-340a-4860-b304-c0af60c7f770' class='xr-var-data-in' type='checkbox'><label for='data-a2ea1019-340a-4860-b304-c0af60c7f770' 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>history :</span></dt><dd>2019-08-15T12:42:20Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2019-08-15T12:42:21Z altered by CMOR: Converted type from &#x27;d&#x27; to &#x27;f&#x27;.</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>original_name :</span></dt><dd>TREFHT</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</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> 104.41 MiB </td> <td> 52.21 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1, 1980, 96, 144) </td> <td> (1, 990, 96, 144) </td></tr>\n",
       "    <tr><th> Count </th><td> 5 Tasks </td><td> 2 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0a9d9443-5adc-4972-83af-e29b8da8bf0c' class='xr-section-summary-in' type='checkbox'  ><label for='section-0a9d9443-5adc-4972-83af-e29b8da8bf0c' class='xr-section-summary' >Attributes: <span>(54)</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>Hybrid-restart from year 1600-01-01 of piControl</dd><dt><span>branch_time :</span></dt><dd>0.0</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>430335.0</dd><dt><span>cmor_version :</span></dt><dd>3.5.0</dd><dt><span>contact :</span></dt><dd>Please send any requests or bug reports to noresm-ncc@met.no.</dd><dt><span>creation_date :</span></dt><dd>2019-08-15T12:42:21Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.31</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</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.NCC.NorESM2-LM.historical.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>finite-volume grid with 1.9x2.5 degree lat/lon resolution</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2019-08-15T12:42:21Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway</dd><dt><span>institution_id :</span></dt><dd>NCC</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by NCC 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) and at https:///pcmdi.llnl.gov/. 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_id :</span></dt><dd>NorESM2-LM</dd><dt><span>nominal_resolution :</span></dt><dd>250 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>NorESM2-LM</dd><dt><span>parent_sub_experiment_id :</span></dt><dd>none</dd><dt><span>parent_time_units :</span></dt><dd>days since 0421-01-01</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>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>run_variant :</span></dt><dd>N/A</dd><dt><span>source :</span></dt><dd>NorESM2-LM (2017): \n",
       "aerosol: OsloAero\n",
       "atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb)\n",
       "atmosChem: OsloChemSimp\n",
       "land: CLM\n",
       "landIce: CISM\n",
       "ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data])\n",
       "ocnBgchem: HAMOCC\n",
       "seaIce: CICE</dd><dt><span>source_id :</span></dt><dd>NorESM2-LM</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>status :</span></dt><dd>2020-04-30;created; by gcs.cmip6.ldeo@gmail.com</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>table_info :</span></dt><dd>Creation Date:(24 July 2019) MD5:08e314340b9dd440c36c1b8e484514f4</dd><dt><span>title :</span></dt><dd>NorESM2-LM output prepared for CMIP6</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/2486cf87-033c-4848-ab3e-e828c3b7c759\n",
       "hdl:21.14100/8d2ec216-30d3-4f90-8664-7cf75e29780b\n",
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       "hdl:21.14100/fc7602aa-fd09-49d8-94c6-e619622c211b\n",
       "hdl:21.14100/cd51a9c2-0a94-45b2-8d9b-59bcb3467214\n",
       "hdl:21.14100/9b7ad334-1217-4229-98e8-b5a9287c0e5e\n",
       "hdl:21.14100/043ca527-40c7-451f-990b-ccc94720f612\n",
       "hdl:21.14100/a9613498-ea0d-4be4-97e8-77661872798d\n",
       "hdl:21.14100/52699832-d86c-491b-a0a4-2f27d9ee4d69\n",
       "hdl:21.14100/358c4a18-33b2-4c44-bd30-f6125a4df63c\n",
       "hdl:21.14100/77254eaf-90ca-44e7-9a3c-763a840e188f\n",
       "hdl:21.14100/62aed49c-6ca5-4027-80fe-4ddd99af575a\n",
       "hdl:21.14100/83ffe172-769b-42b1-b979-b92d768f60fd\n",
       "hdl:21.14100/91a8c4e2-d33b-4b87-a978-0247d8112ef3\n",
       "hdl:21.14100/e8fb384f-d5a7-41e1-bf12-b8de3e2ffb80</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/2486cf87-033c-4848-ab3e-e828c3b7c759\n",
       "hdl:21.14100/8d2ec216-30d3-4f90-8664-7cf75e29780b\n",
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       "hdl:21.14100/fc7602aa-fd09-49d8-94c6-e619622c211b\n",
       "hdl:21.14100/cd51a9c2-0a94-45b2-8d9b-59bcb3467214\n",
       "hdl:21.14100/9b7ad334-1217-4229-98e8-b5a9287c0e5e\n",
       "hdl:21.14100/043ca527-40c7-451f-990b-ccc94720f612\n",
       "hdl:21.14100/a9613498-ea0d-4be4-97e8-77661872798d\n",
       "hdl:21.14100/52699832-d86c-491b-a0a4-2f27d9ee4d69\n",
       "hdl:21.14100/358c4a18-33b2-4c44-bd30-f6125a4df63c\n",
       "hdl:21.14100/77254eaf-90ca-44e7-9a3c-763a840e188f\n",
       "hdl:21.14100/62aed49c-6ca5-4027-80fe-4ddd99af575a\n",
       "hdl:21.14100/83ffe172-769b-42b1-b979-b92d768f60fd\n",
       "hdl:21.14100/91a8c4e2-d33b-4b87-a978-0247d8112ef3\n",
       "hdl:21.14100/e8fb384f-d5a7-41e1-bf12-b8de3e2ffb80</dd><dt><span>version_id :</span></dt><dd>v20190815</dd><dt><span>intake_esm_varname :</span></dt><dd>[&#x27;tas&#x27;]</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP.NCC.NorESM2-LM.historical.Amon.gn</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 96, bnds: 2, lon: 144, member_id: 1, time: 1980)\n",
       "Coordinates:\n",
       "    height     float64 ...\n",
       "  * lat        (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n",
       "    lat_bnds   (lat, bnds) float64 dask.array<chunksize=(96, 2), meta=np.ndarray>\n",
       "  * lon        (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5\n",
       "    lon_bnds   (lon, bnds) float64 dask.array<chunksize=(144, 2), meta=np.ndarray>\n",
       "  * time       (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "    time_bnds  (time, bnds) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "  * member_id  (member_id) <U8 'r1i1p1f1'\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    tas        (member_id, time, lat, lon) float32 dask.array<chunksize=(1, 990, 96, 144), meta=np.ndarray>\n",
       "Attributes: (12/54)\n",
       "    Conventions:               CF-1.7 CMIP-6.2\n",
       "    activity_id:               CMIP\n",
       "    branch_method:             Hybrid-restart from year 1600-01-01 of piControl\n",
       "    branch_time:               0.0\n",
       "    branch_time_in_child:      0.0\n",
       "    branch_time_in_parent:     430335.0\n",
       "    ...                        ...\n",
       "    variable_id:               tas\n",
       "    variant_label:             r1i1p1f1\n",
       "    netcdf_tracking_ids:       hdl:21.14100/2486cf87-033c-4848-ab3e-e828c3b7c...\n",
       "    version_id:                v20190815\n",
       "    intake_esm_varname:        ['tas']\n",
       "    intake_esm_dataset_key:    CMIP.NCC.NorESM2-LM.historical.Amon.gn"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset = dset_dict[list(dset_dict.keys())[0]]\n",
    "dset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the first timestep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/cartopy/crs.py:825: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  if len(multi_line_string) > 1:\n",
      "/opt/conda/lib/python3.8/site-packages/cartopy/crs.py:877: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  for line in multi_line_string:\n",
      "/opt/conda/lib/python3.8/site-packages/cartopy/crs.py:944: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  if len(p_mline) > 0:\n",
      "/opt/conda/lib/python3.8/site-packages/cartopy/crs.py:836: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  line_strings = list(multi_line_string)\n",
      "/opt/conda/lib/python3.8/site-packages/cartopy/crs.py:836: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  line_strings = list(multi_line_string)\n",
      "/opt/conda/lib/python3.8/site-packages/cartopy/crs.py:982: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  line_strings.extend(multi_line_string)\n",
      "/opt/conda/lib/python3.8/site-packages/cartopy/crs.py:982: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  line_strings.extend(multi_line_string)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f678bdd0bb0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "projection = ccrs.Mercator(central_longitude=-10)\n",
    "\n",
    "f, ax = plt.subplots(subplot_kw=dict(projection=projection))\n",
    "\n",
    "dset['tas'].isel(time=0).plot(transform=ccrs.PlateCarree(), cbar_kwargs=dict(shrink=0.7), cmap='coolwarm')\n",
    "ax.coastlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute weighted mean\n",
    "\n",
    "1. Creating weights: for a rectangular grid the cosine of the latitude is proportional to the grid cell area.\n",
    "2. Compute weighted mean values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def computeWeightedMean(ds):\n",
    "    # Compute weights based on the xarray you pass\n",
    "    weights = np.cos(np.deg2rad(ds.lat))\n",
    "    weights.name = \"weights\"\n",
    "    # Compute weighted mean\n",
    "    air_weighted = ds.weighted(weights)\n",
    "    weighted_mean = air_weighted.mean((\"lon\", \"lat\"))\n",
    "    return weighted_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute weighted average over the entire globe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "weighted_mean = computeWeightedMean(dset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison with unweighted mean\n",
    "- We select a time range\n",
    "- Note how the weighted mean temperature is higher than the unweighted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f678d3de970>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weighted_mean['tas'].sel(time=slice('2000-01-01', '2010-01-01')).plot(label=\"weighted\")\n",
    "dset['tas'].sel(time=slice('2000-01-01', '2010-01-01')).mean((\"lon\", \"lat\")).plot(label=\"unweighted\")\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Weigted arctic average\n",
    "Let's try to also take only the data above 60$^\\circ$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "weighted_mean = computeWeightedMean(dset.where(dset['lat']>60.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f678c35df70>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weighted_mean['tas'].sel(time=slice('2000-01-01', '2010-01-01')).plot(label=\"weighted\")\n",
    "dset['tas'].where(dset['lat']>60.).sel(time=slice('2000-01-01', '2010-01-01')).mean((\"lon\", \"lat\")).plot(label=\"unweighted\")\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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