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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing Obs4MIPs and CMIP Data via ESGF\n",
    "\n",
    "This notebook demonstrates how to access **CMIP6** and **Obs4MIPs** data using the [intake-esgf](https://intake-esgf.readthedocs.io/en/latest/) package. \n",
    "\n",
    "To run this notebook, you'll need to install **intake-esgf**. You can install it using the following command:\n",
    "\n",
    "```bash\n",
    "conda install -c conda-forge intake-esgf\n",
    "```\n",
    "\n",
    "Alternatively, you can follow the [official documentation](https://intake-esgf.readthedocs.io/en/latest/) for more installation details and usage instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import intake_esgf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The catalog can be configured by following the instructions outlined in the [intake-esgf configuration documentation](https://intake-esgf.readthedocs.io/en/latest/configure.html). This allows you to set up indices that provide access to specific datasets. \n",
    "\n",
    "For instance, the following example demonstrates how to access the **Lawrence Livermore National Laboratory (LLNL)** Solr index, which supports a wide range of ESGF datasets. By configuring the index in this way, you can easily query and retrieve the data you need from the ESGF network.\n",
    "\n",
    "Refer to the [configuration guide](https://intake-esgf.readthedocs.io/en/latest/configure.html) for more details on setting up additional data sources or modifying the existing configuration to suit your needs.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<contextlib._GeneratorContextManager at 0x75e3bb3add30>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intake_esgf.conf.set(indices={\"esgf-node.llnl.gov\":True})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell shows which projects are supported. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cmip6', 'cmip5', 'cmip3', 'obs4mips', 'drcdp']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intake_esgf.supported_projects()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Access CMIP Data\n",
    "\n",
    "In this section, we show how to access the 'temperature at surface' (tas) dataset from CMIP6 (Coupled Model Intercomparison Project). CMIP6 provides various climate model simulations, including tas data for different time periods and scenarios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = intake_esgf.ESGFCatalog()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can query datasets using the `search` method, as demonstrated in the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   Searching indices:   0%|          |0/3 [       ?index/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Summary information for 1 results:\n",
       "mip_era                [CMIP6]\n",
       "activity_drs            [CMIP]\n",
       "institution_id         [CCCma]\n",
       "source_id            [CanESM5]\n",
       "experiment_id     [historical]\n",
       "member_id           [r1i1p1f1]\n",
       "table_id                [Amon]\n",
       "variable_id              [tas]\n",
       "grid_label                [gn]\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.search(\n",
    "    project=\"CMIP6\",\n",
    "    variable_id=\"tas\",\n",
    "    experiment_id=\"historical\",\n",
    "    source_id=\"CanESM5\",\n",
    "    member_id=\"r1i1p1f1\",\n",
    "    table_id=\"Amon\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `to_dataset_dict` method, we can load the selected data into a dictionary, with each value containing an `xarray.Dataset`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb53f5564373462bb386877d23529eea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Get file information:   0%|          |0/3 [       ?index/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "23516fed55d04f6387c673e60f596744",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Adding cell measures:   0%|          |0/1 [     ?dataset/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsd = cat.to_dataset_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 65MB\n",
       "Dimensions:    (time: 1980, bnds: 2, lat: 64, lon: 128)\n",
       "Coordinates:\n",
       "  * time       (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "  * lat        (lat) float64 512B -87.86 -85.1 -82.31 ... 82.31 85.1 87.86\n",
       "  * lon        (lon) float64 1kB 0.0 2.812 5.625 8.438 ... 351.6 354.4 357.2\n",
       "    height     float64 8B ...\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) object 32kB ...\n",
       "    lat_bnds   (lat, bnds) float64 1kB ...\n",
       "    lon_bnds   (lon, bnds) float64 2kB ...\n",
       "    tas        (time, lat, lon) float32 65MB ...\n",
       "    areacella  (lat, lon) float32 33kB ...\n",
       "Attributes: (12/55)\n",
       "    CCCma_model_hash:            3dedf95315d603326fde4f5340dc0519d80d10c0\n",
       "    CCCma_parent_runid:          rc3-pictrl\n",
       "    CCCma_pycmor_hash:           33c30511acc319a98240633965a04ca99c26427e\n",
       "    CCCma_runid:                 rc3.1-his01\n",
       "    Conventions:                 CF-1.7 CMIP-6.2\n",
       "    YMDH_branch_time_in_child:   1850:01:01:00\n",
       "    ...                          ...\n",
       "    variant_label:               r1i1p1f1\n",
       "    version:                     v20190429\n",
       "    license:                     CMIP6 model data produced by The Government ...\n",
       "    cmor_version:                3.4.0\n",
       "    activity_drs:                CMIP\n",
       "    member_id:                   r1i1p1f1</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-a14a2e03-3531-4996-b67a-e2f8ffc04a41' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a14a2e03-3531-4996-b67a-e2f8ffc04a41' 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'>time</span>: 1980</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 64</li><li><span class='xr-has-index'>lon</span>: 128</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-75f83961-5da5-4bc2-969c-525bede4e184' class='xr-section-summary-in' type='checkbox'  checked><label for='section-75f83961-5da5-4bc2-969c-525bede4e184' class='xr-section-summary' >Coordinates: <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 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-5f2a3837-4542-40a8-986b-d9876683f52d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f2a3837-4542-40a8-986b-d9876683f52d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a661f3d-2eac-4e93-807e-e07aa674cda2' class='xr-var-data-in' type='checkbox'><label for='data-0a661f3d-2eac-4e93-807e-e07aa674cda2' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</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 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'>-87.86 -85.1 -82.31 ... 85.1 87.86</div><input id='attrs-4948c103-9124-4c4b-bc29-6c0353b83dd5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4948c103-9124-4c4b-bc29-6c0353b83dd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19b4e2b9-6059-4096-b3ee-23a50f36a46c' class='xr-var-data-in' type='checkbox'><label for='data-19b4e2b9-6059-4096-b3ee-23a50f36a46c' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-87.863799, -85.096527, -82.312913, -79.525607, -76.7369  , -73.947515,\n",
       "       -71.157752, -68.367756, -65.577607, -62.787352, -59.99702 , -57.206632,\n",
       "       -54.4162  , -51.625734, -48.835241, -46.044727, -43.254195, -40.463648,\n",
       "       -37.67309 , -34.882521, -32.091944, -29.30136 , -26.510769, -23.720174,\n",
       "       -20.929574, -18.138971, -15.348365, -12.557756,  -9.767146,  -6.976534,\n",
       "        -4.185921,  -1.395307,   1.395307,   4.185921,   6.976534,   9.767146,\n",
       "        12.557756,  15.348365,  18.138971,  20.929574,  23.720174,  26.510769,\n",
       "        29.30136 ,  32.091944,  34.882521,  37.67309 ,  40.463648,  43.254195,\n",
       "        46.044727,  48.835241,  51.625734,  54.4162  ,  57.206632,  59.99702 ,\n",
       "        62.787352,  65.577607,  68.367756,  71.157752,  73.947515,  76.7369  ,\n",
       "        79.525607,  82.312913,  85.096527,  87.863799])</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 2.812 5.625 ... 354.4 357.2</div><input id='attrs-eb6c08a3-e3bd-464c-bcc9-8f517bdca25b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb6c08a3-e3bd-464c-bcc9-8f517bdca25b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-669f5f6d-0784-4a5c-984c-31a8a9d78b1c' class='xr-var-data-in' type='checkbox'><label for='data-669f5f6d-0784-4a5c-984c-31a8a9d78b1c' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.    ,   2.8125,   5.625 ,   8.4375,  11.25  ,  14.0625,  16.875 ,\n",
       "        19.6875,  22.5   ,  25.3125,  28.125 ,  30.9375,  33.75  ,  36.5625,\n",
       "        39.375 ,  42.1875,  45.    ,  47.8125,  50.625 ,  53.4375,  56.25  ,\n",
       "        59.0625,  61.875 ,  64.6875,  67.5   ,  70.3125,  73.125 ,  75.9375,\n",
       "        78.75  ,  81.5625,  84.375 ,  87.1875,  90.    ,  92.8125,  95.625 ,\n",
       "        98.4375, 101.25  , 104.0625, 106.875 , 109.6875, 112.5   , 115.3125,\n",
       "       118.125 , 120.9375, 123.75  , 126.5625, 129.375 , 132.1875, 135.    ,\n",
       "       137.8125, 140.625 , 143.4375, 146.25  , 149.0625, 151.875 , 154.6875,\n",
       "       157.5   , 160.3125, 163.125 , 165.9375, 168.75  , 171.5625, 174.375 ,\n",
       "       177.1875, 180.    , 182.8125, 185.625 , 188.4375, 191.25  , 194.0625,\n",
       "       196.875 , 199.6875, 202.5   , 205.3125, 208.125 , 210.9375, 213.75  ,\n",
       "       216.5625, 219.375 , 222.1875, 225.    , 227.8125, 230.625 , 233.4375,\n",
       "       236.25  , 239.0625, 241.875 , 244.6875, 247.5   , 250.3125, 253.125 ,\n",
       "       255.9375, 258.75  , 261.5625, 264.375 , 267.1875, 270.    , 272.8125,\n",
       "       275.625 , 278.4375, 281.25  , 284.0625, 286.875 , 289.6875, 292.5   ,\n",
       "       295.3125, 298.125 , 300.9375, 303.75  , 306.5625, 309.375 , 312.1875,\n",
       "       315.    , 317.8125, 320.625 , 323.4375, 326.25  , 329.0625, 331.875 ,\n",
       "       334.6875, 337.5   , 340.3125, 343.125 , 345.9375, 348.75  , 351.5625,\n",
       "       354.375 , 357.1875])</pre></div></li><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-0706f66b-08cd-4913-88db-c59520c490a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0706f66b-08cd-4913-88db-c59520c490a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd5769cd-46d4-44d4-ada4-d63b68b07f70' class='xr-var-data-in' type='checkbox'><label for='data-cd5769cd-46d4-44d4-ada4-d63b68b07f70' 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>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-17a9978e-0f41-4832-b480-ed7fee4c369f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-17a9978e-0f41-4832-b480-ed7fee4c369f' class='xr-section-summary' >Data variables: <span>(5)</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, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fdb1bc13-29f4-4959-9998-73384a076d8d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdb1bc13-29f4-4959-9998-73384a076d8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dd9562a-7262-4ff6-95e9-0d0a4cc8b469' class='xr-var-data-in' type='checkbox'><label for='data-6dd9562a-7262-4ff6-95e9-0d0a4cc8b469' 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>[3960 values with dtype=object]</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'>...</div><input id='attrs-ae4790f8-e474-4582-8aa6-bad62a8f6808' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae4790f8-e474-4582-8aa6-bad62a8f6808' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da1325e5-4bd9-438a-8cdf-ce0ca8748989' class='xr-var-data-in' type='checkbox'><label for='data-da1325e5-4bd9-438a-8cdf-ce0ca8748989' 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>[128 values with dtype=float64]</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'>...</div><input id='attrs-5fbeae65-493d-40ba-88d5-9b8ad9e62f8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5fbeae65-493d-40ba-88d5-9b8ad9e62f8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57e036dc-7eef-4c4e-aa30-519cbd71b731' class='xr-var-data-in' type='checkbox'><label for='data-57e036dc-7eef-4c4e-aa30-519cbd71b731' 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>[256 values with dtype=float64]</pre></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'>...</div><input id='attrs-f42c42d3-51c9-4e04-a81f-2ee03642ff3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f42c42d3-51c9-4e04-a81f-2ee03642ff3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69fc31f4-9f4e-4f3e-aef8-bc31f916dfdc' class='xr-var-data-in' type='checkbox'><label for='data-69fc31f4-9f4e-4f3e-aef8-bc31f916dfdc' 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>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>ST+273.16, CMIP_table_comment: near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>original_name :</span></dt><dd>ST</dd><dt><span>history :</span></dt><dd>degctok 2019-04-30T17:32:17Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2019-04-30T17:32:17Z altered by CMOR: Reordered dimensions, original order: lat lon time. 2019-04-30T17:32:17Z altered by CMOR: replaced missing value flag (1e+38) with standard missing value (1e+20).</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd></dl></div><div class='xr-var-data'><pre>[16220160 values with dtype=float32]</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-70938793-1fe6-42a0-8efb-486a93191619' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70938793-1fe6-42a0-8efb-486a93191619' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29363dce-4da2-4d40-a364-4476c9467f79' class='xr-var-data-in' type='checkbox'><label for='data-29363dce-4da2-4d40-a364-4476c9467f79' 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>standard_name :</span></dt><dd>cell_area</dd><dt><span>long_name :</span></dt><dd>Grid-Cell Area for Atmospheric Grid Variables</dd><dt><span>comment :</span></dt><dd>Use =mkwght=; afrac*4*Pi*R^2, CMIP_table_comment: For atmospheres with more than 1 mesh (e.g., staggered grids), report areas that apply to surface vertical fluxes of energy.</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>original_name :</span></dt><dd>WGHT</dd><dt><span>history :</span></dt><dd>areacella 2019-05-01T18:36:43Z altered by CMOR: replaced missing value flag (1e+38) with standard missing value (1e+20).</dd><dt><span>cell_methods :</span></dt><dd>area: sum</dd></dl></div><div class='xr-var-data'><pre>[8192 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5fbb6612-32e3-4bcb-bd02-466a52a9f314' class='xr-section-summary-in' type='checkbox'  ><label for='section-5fbb6612-32e3-4bcb-bd02-466a52a9f314' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-88b17bda-93c5-4966-863c-954a8d3cab8e' class='xr-index-data-in' type='checkbox'/><label for='index-88b17bda-93c5-4966-863c-954a8d3cab8e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-16 12:00:00, 1850-02-15 00:00:00, 1850-03-16 12:00:00,\n",
       "             1850-04-16 00:00:00, 1850-05-16 12:00:00, 1850-06-16 00:00:00,\n",
       "             1850-07-16 12:00:00, 1850-08-16 12:00:00, 1850-09-16 00:00:00,\n",
       "             1850-10-16 12:00:00,\n",
       "             ...\n",
       "             2014-03-16 12:00:00, 2014-04-16 00:00:00, 2014-05-16 12:00:00,\n",
       "             2014-06-16 00:00:00, 2014-07-16 12:00:00, 2014-08-16 12:00:00,\n",
       "             2014-09-16 00:00:00, 2014-10-16 12:00:00, 2014-11-16 00:00:00,\n",
       "             2014-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=1980, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-5d4716fc-be74-4d26-9aab-65723739ab29' class='xr-index-data-in' type='checkbox'/><label for='index-5d4716fc-be74-4d26-9aab-65723739ab29' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -87.86379883923273,  -85.09652698831745,  -82.31291294788636,\n",
       "        -79.52560657265951,   -76.7368996803684,  -73.94751515398974,\n",
       "        -71.15775201158739,  -68.36775610831324,  -65.57760701082788,\n",
       "        -62.78735179896313, -59.997020108491355,   -57.2066315276433,\n",
       "        -54.41619952608627, -51.625733674938296,  -48.83524096625064,\n",
       "       -46.044726631101724,  -43.25419466535099,  -40.46364817811508,\n",
       "        -37.67308962904537,   -34.8825209937735, -32.091943881744044,\n",
       "       -29.301359621762764, -26.510769325211022,  -23.72017393353477,\n",
       "        -20.92957425448953, -18.138970990239372, -15.348364759491508,\n",
       "        -12.55775611523069,  -9.767145559195578,  -6.976533553948642,\n",
       "        -4.185920533189158, -1.3953069108194975,  1.3953069108194975,\n",
       "         4.185920533189158,   6.976533553948642,   9.767145559195578,\n",
       "         12.55775611523069,  15.348364759491508,  18.138970990239372,\n",
       "         20.92957425448953,   23.72017393353477,  26.510769325211022,\n",
       "        29.301359621762764,  32.091943881744044,    34.8825209937735,\n",
       "         37.67308962904537,   40.46364817811508,   43.25419466535099,\n",
       "        46.044726631101724,   48.83524096625064,  51.625733674938296,\n",
       "         54.41619952608627,    57.2066315276433,  59.997020108491355,\n",
       "         62.78735179896313,   65.57760701082788,   68.36775610831324,\n",
       "         71.15775201158739,   73.94751515398974,    76.7368996803684,\n",
       "         79.52560657265951,   82.31291294788636,   85.09652698831745,\n",
       "         87.86379883923273],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-41fe79a2-61ae-40f4-aea7-ef99da52a4fa' class='xr-index-data-in' type='checkbox'/><label for='index-41fe79a2-61ae-40f4-aea7-ef99da52a4fa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([     0.0,   2.8125,    5.625,   8.4375,    11.25,  14.0625,   16.875,\n",
       "        19.6875,     22.5,  25.3125,\n",
       "       ...\n",
       "        331.875, 334.6875,    337.5, 340.3125,  343.125, 345.9375,   348.75,\n",
       "       351.5625,  354.375, 357.1875],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=128))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e101bbb0-f12f-414f-871e-81b324117588' class='xr-section-summary-in' type='checkbox'  ><label for='section-e101bbb0-f12f-414f-871e-81b324117588' class='xr-section-summary' >Attributes: <span>(55)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CCCma_model_hash :</span></dt><dd>3dedf95315d603326fde4f5340dc0519d80d10c0</dd><dt><span>CCCma_parent_runid :</span></dt><dd>rc3-pictrl</dd><dt><span>CCCma_pycmor_hash :</span></dt><dd>33c30511acc319a98240633965a04ca99c26427e</dd><dt><span>CCCma_runid :</span></dt><dd>rc3.1-his01</dd><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>YMDH_branch_time_in_child :</span></dt><dd>1850:01:01:00</dd><dt><span>YMDH_branch_time_in_parent :</span></dt><dd>5201:01:01:00</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>Spin-up documentation</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>1223115.0</dd><dt><span>contact :</span></dt><dd>ec.cccma.info-info.ccmac.ec@canada.ca</dd><dt><span>creation_date :</span></dt><dd>2019-04-30T17:32:17Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</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.CCCma.CanESM5.historical.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>T63L49 native atmosphere, T63 Linear Gaussian Grid; 128 x 64 longitude/latitude; 49 levels; top level 1 hPa</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2019-04-30T17:32:17Z ;rewrote data to be consistent with CMIP for variable tas found in table Amon.;\n",
       "Output from $runid</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC V8P 5C2, Canada</dd><dt><span>institution_id :</span></dt><dd>CCCma</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>500 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>CanESM5</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-01-01 0:0:0.0</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>references :</span></dt><dd>Geophysical Model Development Special issue on CanESM5 (https://www.geosci-model-dev.net/special_issues.html)</dd><dt><span>source :</span></dt><dd>CanESM5 (2019): \n",
       "aerosol: interactive\n",
       "atmos: CanAM5 (T63L49 native atmosphere, T63 Linear Gaussian Grid; 128 x 64 longitude/latitude; 49 levels; top level 1 hPa)\n",
       "atmosChem: specified oxidants for aerosols\n",
       "land: CLASS3.6/CTEM1.2\n",
       "landIce: specified ice sheets\n",
       "ocean: NEMO3.4.1 (ORCA1 tripolar grid, 1 deg with refinement to 1/3 deg within 20 degrees of the equator; 361 x 290 longitude/latitude; 45 vertical levels; top grid cell 0-6.19 m)\n",
       "ocnBgchem: Canadian Model of Ocean Carbon (CMOC); NPZD ecosystem with OMIP prescribed carbonate chemistry\n",
       "seaIce: LIM2</dd><dt><span>source_id :</span></dt><dd>CanESM5</dd><dt><span>source_type :</span></dt><dd>AOGCM</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:(20 February 2019) MD5:374fbe5a2bcca535c40f7f23da271e49</dd><dt><span>title :</span></dt><dd>CanESM5 output prepared for CMIP6</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/872062df-acae-499b-aa0f-9eaca7681abc</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>version :</span></dt><dd>v20190429</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by The Government of Canada (Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada) 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>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>activity_drs :</span></dt><dd>CMIP</dd><dt><span>member_id :</span></dt><dd>r1i1p1f1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 65MB\n",
       "Dimensions:    (time: 1980, bnds: 2, lat: 64, lon: 128)\n",
       "Coordinates:\n",
       "  * time       (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "  * lat        (lat) float64 512B -87.86 -85.1 -82.31 ... 82.31 85.1 87.86\n",
       "  * lon        (lon) float64 1kB 0.0 2.812 5.625 8.438 ... 351.6 354.4 357.2\n",
       "    height     float64 8B ...\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) object 32kB ...\n",
       "    lat_bnds   (lat, bnds) float64 1kB ...\n",
       "    lon_bnds   (lon, bnds) float64 2kB ...\n",
       "    tas        (time, lat, lon) float32 65MB ...\n",
       "    areacella  (lat, lon) float32 33kB ...\n",
       "Attributes: (12/55)\n",
       "    CCCma_model_hash:            3dedf95315d603326fde4f5340dc0519d80d10c0\n",
       "    CCCma_parent_runid:          rc3-pictrl\n",
       "    CCCma_pycmor_hash:           33c30511acc319a98240633965a04ca99c26427e\n",
       "    CCCma_runid:                 rc3.1-his01\n",
       "    Conventions:                 CF-1.7 CMIP-6.2\n",
       "    YMDH_branch_time_in_child:   1850:01:01:00\n",
       "    ...                          ...\n",
       "    variant_label:               r1i1p1f1\n",
       "    version:                     v20190429\n",
       "    license:                     CMIP6 model data produced by The Government ...\n",
       "    cmor_version:                3.4.0\n",
       "    activity_drs:                CMIP\n",
       "    member_id:                   r1i1p1f1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsd[\"tas\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following cell, we calculate the mean temperature over time, convert it to degrees Celsius, and plot the global surface temperature variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x75e3a5135940>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_plot = dsd[\"tas\"][\"tas\"].mean(dim=\"time\") - 273.15\n",
    "ds_plot.plot(cmap=\"bwr\", vmin=-40, vmax=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Access obs4MIPS\n",
    "\n",
    "In this section, we show how to access the 'temperature at surface' (tas) dataset from obs4MIPS (Observations for Model Intercomparison Projects). obs4MIPS is a project that provides observational datasets for comparison with climate model outputs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first use the `search` method to get the desired dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8fe94bde2f05426689cde6843a2a6134",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "   Searching indices:   0%|          |0/3 [       ?index/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Summary information for 1 results:\n",
       "activity_id       [obs4MIPs]\n",
       "institution_id       [ECMWF]\n",
       "source_id            [ERA-5]\n",
       "frequency              [mon]\n",
       "variable_id            [tas]\n",
       "grid_label              [gn]\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.search(\n",
    "    project=\"obs4MIPs\",\n",
    "    institution_id=\"ECMWF\",\n",
    "    variable_id=\"tas\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the search results into a dictionary and extract the `xarray.Dataset` containing the data in the subsequent cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "34955f2ef8724c68acdc099d78b74856",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Get file information:   0%|          |0/3 [       ?index/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsd = cat.to_dataset_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "\n",
       ".xr-attrs dt,\n",
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2GB\n",
       "Dimensions:    (time: 528, bnds: 2, lat: 721, lon: 1440)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 4kB 1979-01-16T12:00:00 ... 2022-12-16T1...\n",
       "  * lat        (lat) float64 6kB -90.0 -89.75 -89.5 -89.25 ... 89.5 89.75 90.0\n",
       "  * lon        (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "    height     float64 8B 2.0\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) datetime64[ns] 8kB dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
       "    lat_bnds   (time, lat, bnds) float64 6MB dask.array&lt;chunksize=(12, 721, 2), meta=np.ndarray&gt;\n",
       "    lon_bnds   (time, lon, bnds) float64 12MB dask.array&lt;chunksize=(12, 1440, 2), meta=np.ndarray&gt;\n",
       "    tas        (time, lat, lon) float32 2GB dask.array&lt;chunksize=(1, 721, 1440), meta=np.ndarray&gt;\n",
       "Attributes: (12/41)\n",
       "    CONVERSION_DATE:             Mon Nov 11 07:48:24 MST 2019\n",
       "    CONVERSION_PLATFORM:         Linux casper02 3.10.0-693.21.1.el7.x86_64 #1...\n",
       "    Conventions:                 CF-1.11; ODS-2.5\n",
       "    DATA_SOURCE:                 ECMWF: https://cds.climate.copernicus.eu, Co...\n",
       "    NCO:                         netCDF Operators version 4.7.9 (Homepage = h...\n",
       "    NETCDF_COMPRESSION:          NCO: Precision-preserving compression to net...\n",
       "    ...                          ...\n",
       "    tracking_id:                 hdl:21.14102/003d2533-c769-4a37-b5de-8a0cd03...\n",
       "    variable_id:                 tas\n",
       "    variant_info:                obs4MIPs-compliant product prepared by PCMDI...\n",
       "    variant_label:               PCMDI\n",
       "    license:                     License to use Copernicus products available...\n",
       "    cmor_version:                3.9.0</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-05179b3f-7920-4dbf-b2da-873bc5014642' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-05179b3f-7920-4dbf-b2da-873bc5014642' 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'>time</span>: 528</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-87ddda35-f0cb-4065-9b21-d2a3f254232c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-87ddda35-f0cb-4065-9b21-d2a3f254232c' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-16T12:00:00 ... 2022-12-...</div><input id='attrs-d216a233-93c4-4223-94d9-99f0ba057dac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d216a233-93c4-4223-94d9-99f0ba057dac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-095cf468-1258-4056-81ae-7ad3d9827f79' class='xr-var-data-in' type='checkbox'><label for='data-095cf468-1258-4056-81ae-7ad3d9827f79' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</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([&#x27;1979-01-16T12:00:00.000000000&#x27;, &#x27;1979-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-03-16T12:00:00.000000000&#x27;, ..., &#x27;2022-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2022-11-16T00:00:00.000000000&#x27;, &#x27;2022-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</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 -89.75 -89.5 ... 89.75 90.0</div><input id='attrs-04126372-d24d-4b01-9c36-c95eb11ccae0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04126372-d24d-4b01-9c36-c95eb11ccae0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67250549-534d-43e1-b455-d5ebbe904ce2' class='xr-var-data-in' type='checkbox'><label for='data-67250549-534d-43e1-b455-d5ebbe904ce2' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -89.75, -89.5 , ...,  89.5 ,  89.75,  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 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-3377273a-03c4-4673-ad25-0621aac234a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3377273a-03c4-4673-ad25-0621aac234a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cc2dd7d-975c-4cd2-8724-984b97841af2' class='xr-var-data-in' type='checkbox'><label for='data-8cc2dd7d-975c-4cd2-8724-984b97841af2' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
       "       3.5975e+02])</pre></div></li><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'>2.0</div><input id='attrs-ce7c1f65-6c92-45c8-b048-74fe4f6f5551' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce7c1f65-6c92-45c8-b048-74fe4f6f5551' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8642df62-a562-4f02-ac01-af0af7236c19' class='xr-var-data-in' type='checkbox'><label for='data-8642df62-a562-4f02-ac01-af0af7236c19' 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>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-038804a6-081a-4627-8815-01891595a4df' class='xr-section-summary-in' type='checkbox'  checked><label for='section-038804a6-081a-4627-8815-01891595a4df' 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, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-c0b94544-093d-4f6a-bcaa-879109e1189c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c0b94544-093d-4f6a-bcaa-879109e1189c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9920ac85-9b08-4d6c-b5d1-6dd109582e3c' class='xr-var-data-in' type='checkbox'><label for='data-9920ac85-9b08-4d6c-b5d1-6dd109582e3c' 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 style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 8.25 kiB </td>\n",
       "                        <td> 16 B </td>\n",
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       "                        <td> (528, 2) </td>\n",
       "                        <td> (1, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 528 chunks in 89 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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       "                        <td> 135.19 kiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
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       "                        <td> 270.00 kiB </td>\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=(1, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-f0be5780-64d3-432f-a2a6-d5a01a35e3f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0be5780-64d3-432f-a2a6-d5a01a35e3f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a085165-b9eb-482e-b81f-f8366fad14b5' class='xr-var-data-in' type='checkbox'><label for='data-3a085165-b9eb-482e-b81f-f8366fad14b5' 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>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2025-02-20T19:30:47Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2025-02-20T19:30:47Z altered by CMOR: Inverted axis: lat.</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.04 GiB </td>\n",
       "                        <td> 3.96 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (528, 721, 1440) </td>\n",
       "                        <td> (1, 721, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 528 chunks in 89 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-1db2f67f-3ffc-4095-849c-0dfc1bf4c561' class='xr-section-summary-in' type='checkbox'  ><label for='section-1db2f67f-3ffc-4095-849c-0dfc1bf4c561' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f000a110-9252-4586-9f52-1449daa62939' class='xr-index-data-in' type='checkbox'/><label for='index-f000a110-9252-4586-9f52-1449daa62939' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1979-01-16 12:00:00&#x27;, &#x27;1979-02-15 00:00:00&#x27;,\n",
       "               &#x27;1979-03-16 12:00:00&#x27;, &#x27;1979-04-16 00:00:00&#x27;,\n",
       "               &#x27;1979-05-16 12:00:00&#x27;, &#x27;1979-06-16 00:00:00&#x27;,\n",
       "               &#x27;1979-07-16 12:00:00&#x27;, &#x27;1979-08-16 12:00:00&#x27;,\n",
       "               &#x27;1979-09-16 00:00:00&#x27;, &#x27;1979-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2022-03-16 12:00:00&#x27;, &#x27;2022-04-16 00:00:00&#x27;,\n",
       "               &#x27;2022-05-16 12:00:00&#x27;, &#x27;2022-06-16 00:00:00&#x27;,\n",
       "               &#x27;2022-07-16 12:00:00&#x27;, &#x27;2022-08-16 12:00:00&#x27;,\n",
       "               &#x27;2022-09-16 00:00:00&#x27;, &#x27;2022-10-16 12:00:00&#x27;,\n",
       "               &#x27;2022-11-16 00:00:00&#x27;, &#x27;2022-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=528, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0cdcadf7-13b7-45a5-a7ce-272e1c862fc1' class='xr-index-data-in' type='checkbox'/><label for='index-0cdcadf7-13b7-45a5-a7ce-272e1c862fc1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -90.0, -89.75,  -89.5, -89.25,  -89.0, -88.75,  -88.5, -88.25,  -88.0,\n",
       "       -87.75,\n",
       "       ...\n",
       "        87.75,   88.0,  88.25,   88.5,  88.75,   89.0,  89.25,   89.5,  89.75,\n",
       "         90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=721))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-fe3276f6-0fb6-4f8e-8510-d62cc5f0f380' class='xr-index-data-in' type='checkbox'/><label for='index-fe3276f6-0fb6-4f8e-8510-d62cc5f0f380' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
       "         2.25,\n",
       "       ...\n",
       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
       "       359.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ee62dd2c-9202-48cb-bc33-7f448509ffbc' class='xr-section-summary-in' type='checkbox'  ><label for='section-ee62dd2c-9202-48cb-bc33-7f448509ffbc' class='xr-section-summary' >Attributes: <span>(41)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CONVERSION_DATE :</span></dt><dd>Mon Nov 11 07:48:24 MST 2019</dd><dt><span>CONVERSION_PLATFORM :</span></dt><dd>Linux casper02 3.10.0-693.21.1.el7.x86_64 #1 SMP Wed Mar 7 19:03:37 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux</dd><dt><span>Conventions :</span></dt><dd>CF-1.11; ODS-2.5</dd><dt><span>DATA_SOURCE :</span></dt><dd>ECMWF: https://cds.climate.copernicus.eu, Copernicus Climate Data Store</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.9 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>NETCDF_COMPRESSION :</span></dt><dd>NCO: Precision-preserving compression to netCDF4/HDF5 (see &quot;history&quot; and &quot;NCO&quot; global attributes below for specifics).</dd><dt><span>NETCDF_CONVERSION :</span></dt><dd>CISL RDA: Conversion from ECMWF GRIB 1 data to netCDF4.</dd><dt><span>NETCDF_VERSION :</span></dt><dd>4.6.1</dd><dt><span>activity_id :</span></dt><dd>obs4MIPs</dd><dt><span>contact :</span></dt><dd>submissions-obs4mips@wcrp-cmip.org</dd><dt><span>creation_date :</span></dt><dd>2025-02-20T19:30:47Z</dd><dt><span>data_specs_version :</span></dt><dd>ODS-2.5</dd><dt><span>dataset_contributor :</span></dt><dd>PjG</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>.</dd><dt><span>grid :</span></dt><dd>0.25x 0.25 degree latitude x longitude</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2025-02-20T19:30:47Z; CMOR rewrote data to be consistent with obs4MIPs, and CF-1.11; ODS-2.5 standards;\n",
       "Mon Nov 11 07:48:25 2019: ncks -4 --ppc default=7 e5.moda.an.sfc.128_167_2t.ll025sc.1979010100_1979120100.unc.nc e5.moda.an.sfc.128_167_2t.ll025sc.1979010100_1979120100.nc</dd><dt><span>institution :</span></dt><dd>The European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading RG2 9AX, UK</dd><dt><span>institution_id :</span></dt><dd>ECMWF</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>25 km</dd><dt><span>product :</span></dt><dd>reanalysis</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>references :</span></dt><dd>https://doi.org/10.1002/qj.3803</dd><dt><span>region :</span></dt><dd>global</dd><dt><span>source :</span></dt><dd>ECMWF-ERA-5 1.0 (2019): ECMWF - ERA5 (European ReAnalysis)</dd><dt><span>source_data_retrieval_date :</span></dt><dd>20240404</dd><dt><span>source_id :</span></dt><dd>ERA-5</dd><dt><span>source_type :</span></dt><dd>reanalysis</dd><dt><span>source_version_number :</span></dt><dd>1.0</dd><dt><span>table_id :</span></dt><dd>obs4MIPs_Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(18 November 2020) MD5:e1c3cfad2bd885020628302b713a1747</dd><dt><span>title :</span></dt><dd>ERA5 monthly mean data</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14102/003d2533-c769-4a37-b5de-8a0cd038a01c</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_info :</span></dt><dd>obs4MIPs-compliant product prepared by PCMDI (ODS2.5)</dd><dt><span>variant_label :</span></dt><dd>PCMDI</dd><dt><span>license :</span></dt><dd>License to use Copernicus products available https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-monthly-means?tab=overview</dd><dt><span>cmor_version :</span></dt><dd>3.9.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 2GB\n",
       "Dimensions:    (time: 528, bnds: 2, lat: 721, lon: 1440)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 4kB 1979-01-16T12:00:00 ... 2022-12-16T1...\n",
       "  * lat        (lat) float64 6kB -90.0 -89.75 -89.5 -89.25 ... 89.5 89.75 90.0\n",
       "  * lon        (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "    height     float64 8B 2.0\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) datetime64[ns] 8kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    lat_bnds   (time, lat, bnds) float64 6MB dask.array<chunksize=(12, 721, 2), meta=np.ndarray>\n",
       "    lon_bnds   (time, lon, bnds) float64 12MB dask.array<chunksize=(12, 1440, 2), meta=np.ndarray>\n",
       "    tas        (time, lat, lon) float32 2GB dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>\n",
       "Attributes: (12/41)\n",
       "    CONVERSION_DATE:             Mon Nov 11 07:48:24 MST 2019\n",
       "    CONVERSION_PLATFORM:         Linux casper02 3.10.0-693.21.1.el7.x86_64 #1...\n",
       "    Conventions:                 CF-1.11; ODS-2.5\n",
       "    DATA_SOURCE:                 ECMWF: https://cds.climate.copernicus.eu, Co...\n",
       "    NCO:                         netCDF Operators version 4.7.9 (Homepage = h...\n",
       "    NETCDF_COMPRESSION:          NCO: Precision-preserving compression to net...\n",
       "    ...                          ...\n",
       "    tracking_id:                 hdl:21.14102/003d2533-c769-4a37-b5de-8a0cd03...\n",
       "    variable_id:                 tas\n",
       "    variant_info:                obs4MIPs-compliant product prepared by PCMDI...\n",
       "    variant_label:               PCMDI\n",
       "    license:                     License to use Copernicus products available...\n",
       "    cmor_version:                3.9.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsd[\"tas\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following cell, we calculate the mean temperature over time, convert it to degrees Celsius, and plot the global surface temperature variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x75e3b22eb610>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_plot = dsd[\"tas\"][\"tas\"].mean(dim=\"time\") - 273.15  \n",
    "ds_plot.plot(cmap=\"bwr\", vmin=-40, vmax=40)"
   ]
  }
 ],
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