datasets/cil-gdpcir/cil-gdpcir-example.ipynb

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    "## Climate Impact Lab Global Downscaled Projections for Climate Impacts Research\n",
    "\n",
    "The Climate Impact Lab Downscaled Projections for Climate Impacts Research (CIL-GDPCR) collections contain bias corrected and downscaled 1/4° CMIP6 projections for temperature and precipitation.\n",
    "\n",
    "See the project homepage for more information: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6).\n",
    "\n",
    "This tutorial covers accessing the STAC API to explore the collections and open a dataset. Additional tutorials are available at [github.com/microsoft/PlanetaryComputerExamples](https://github.com/microsoft/PlanetaryComputerExamples/blob/main/datasets/cil-gdpcir).\n"
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   "source": [
    "import planetary_computer\n",
    "import pystac_client"
   ]
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    "### STAC Metadata\n",
    "\n",
    "The [CIL-GDPR datsets](https://planetarycomputer.microsoft.com/dataset/group/cil-gdpcir) are grouped into several collections, depending on the license the data are provided under.\n",
    "\n",
    "- [CIL-GDPCIR-CC0](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\n",
    "- [CIL-GDPCIR-CC-BY](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\n",
    "- [CIL-GDPCIR-CC-BY-SA](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa)\n",
    "\n",
    "The data assets in this collection are a set of [Zarr](https://zarr.readthedocs.io/) groups which can be opend by tools like [xarray](https://xarray.pydata.org/). Each Zarr group contains a single data variable (either `pr`, `tasmax`, or `tasmin`). The Planetary Computer provides a single STAC item per experiment, and each STAC item has one asset per data variable.\n",
    "\n",
    "To access the data, we'll create a `pystac_client.Client` to access the Planetary Computer data catalog, including `modifier=planetary_computer.sign_inplace` to automatically sign the returned results. See https://planetarycomputer.microsoft.com/docs/quickstarts/reading-stac/ for more."
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       "{'pr': <Asset href=abfs://cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/pr/v1.1.zarr>,\n",
       " 'tasmax': <Asset href=abfs://cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/tasmax/v1.1.zarr>,\n",
       " 'tasmin': <Asset href=abfs://cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/tasmin/v1.1.zarr>}"
      ]
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     "execution_count": 2,
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   "source": [
    "catalog = pystac_client.Client.open(\n",
    "    \"https://planetarycomputer.microsoft.com/api/stac/v1/\",\n",
    "    modifier=planetary_computer.sign_inplace,\n",
    ")\n",
    "item = catalog.get_collection(\"cil-gdpcir-cc-by\").get_item(\n",
    "    \"cil-gdpcir-NUIST-NESM3-ssp585-r1i1p1f1-day\"\n",
    ")\n",
    "item.assets"
   ]
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   "id": "f12407bc-d02f-4906-9158-5f3db6249e62",
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   "source": [
    "The STAC metadata includes all the required fields from the [CMIP 6 controlled vocabularies](https://wcrp-cmip.github.io/CMIP6_CVs/)."
   ]
  },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NESM3'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item.properties[\"cmip6:source_id\"]"
   ]
  },
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   "source": [
    "### Querying the STAC API\n",
    "\n",
    "Use the Planetary Computer STAC API to find the exact data you want. You'll most likely want to query on the controlled vocabularies fields, under the `cmip6:` prefix.. See the collection summary for the set of allowed values for each of those."
   ]
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       "{'cmip6:variable': ['pr', 'tasmax', 'tasmin'],\n",
       " 'cmip6:source_id': ['BCC-CSM2-MR',\n",
       "  'ACCESS-ESM1-5',\n",
       "  'ACCESS-CM2',\n",
       "  'MIROC-ES2L',\n",
       "  'MIROC6',\n",
       "  'NorESM2-LM',\n",
       "  'NorESM2-MM',\n",
       "  'GFDL-CM4',\n",
       "  'GFDL-ESM4',\n",
       "  'NESM3',\n",
       "  'MPI-ESM1-2-HR',\n",
       "  'HadGEM3-GC31-LL',\n",
       "  'UKESM1-0-LL',\n",
       "  'MPI-ESM1-2-LR',\n",
       "  'EC-Earth3',\n",
       "  'EC-Earth3-AerChem',\n",
       "  'EC-Earth3-CC',\n",
       "  'EC-Earth3-Veg',\n",
       "  'EC-Earth3-Veg-LR',\n",
       "  'CMCC-CM2-SR5',\n",
       "  'CMCC-ESM2'],\n",
       " 'cmip6:experiment_id': ['historical', 'ssp126', 'ssp245', 'ssp370', 'ssp585'],\n",
       " 'cmip6:institution_id': ['BCC',\n",
       "  'CAS',\n",
       "  'CCCma',\n",
       "  'CMCC',\n",
       "  'CSIRO',\n",
       "  'CSIRO-ARCCSS',\n",
       "  'DKRZ',\n",
       "  'EC-Earth-Consortium',\n",
       "  'INM',\n",
       "  'MIROC',\n",
       "  'MOHC',\n",
       "  'MPI-M',\n",
       "  'NCC',\n",
       "  'NOAA-GFDL',\n",
       "  'NUIST']}"
      ]
     },
     "execution_count": 4,
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     "output_type": "execute_result"
    }
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   "source": [
    "item.get_collection().summaries.to_dict()"
   ]
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   "execution_count": 5,
   "id": "9f10c8f5-2f29-4cd7-bddf-9facd6515b0e",
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    {
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       "1"
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     "execution_count": 5,
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   "source": [
    "search = catalog.search(\n",
    "    collections=[\"cil-gdpcir-cc-by\"],\n",
    "    query={\"cmip6:source_id\": {\"eq\": \"NESM3\"}, \"cmip6:experiment_id\": {\"eq\": \"ssp585\"}},\n",
    ")\n",
    "items = search.get_all_items()\n",
    "len(items)"
   ]
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    {
     "data": {
      "text/plain": [
       "<Item id=cil-gdpcir-NUIST-NESM3-ssp585-r1i1p1f1-day>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "item = items[0]\n",
    "item"
   ]
  },
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   "id": "66128434-151c-47f9-a5ee-f0ec72f3a2a7",
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   "source": [
    "We can now load the assets with xarray:"
   ]
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       "}\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: 720, lon: 1440, time: 31390)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
       "  * lon      (lon) float64 -179.9 -179.6 -179.4 -179.1 ... 179.4 179.6 179.9\n",
       "  * time     (time) object 2015-01-01 12:00:00 ... 2100-12-31 12:00:00\n",
       "Data variables:\n",
       "    tasmax   (time, lat, lon) float32 dask.array&lt;chunksize=(365, 360, 360), meta=np.ndarray&gt;\n",
       "Attributes: (12/47)\n",
       "    Conventions:                  CF-1.7 CMIP-6.2\n",
       "    activity_id:                  ScenarioMIP\n",
       "    contact:                      climatesci@rhg.com\n",
       "    creation_date:                2019-08-11T09:50:24Z\n",
       "    data_specs_version:           01.00.30\n",
       "    dc6_bias_correction_method:   Quantile Delta Method (QDM)\n",
       "    ...                           ...\n",
       "    sub_experiment_id:            none\n",
       "    table_id:                     day\n",
       "    tracking_id:                  hdl:21.14100/01f58d9c-1317-467e-813d-a2358c...\n",
       "    variable_id:                  tasmax\n",
       "    variant_label:                r1i1p1f1\n",
       "    version_id:                   v20190811</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-19d9ff49-7708-4a33-a246-39b224659480' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-19d9ff49-7708-4a33-a246-39b224659480' 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>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>time</span>: 31390</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e2349289-ffdb-4fa9-976e-b660192dc0fb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e2349289-ffdb-4fa9-976e-b660192dc0fb' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-b014f589-dc0f-4854-8e87-ddd4c4e29c29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b014f589-dc0f-4854-8e87-ddd4c4e29c29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12b9c73e-e0c5-4b47-88fa-66c04004ed7b' class='xr-var-data-in' type='checkbox'><label for='data-12b9c73e-e0c5-4b47-88fa-66c04004ed7b' 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>long_name :</span></dt><dd>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875])</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'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-3ccc6d6e-8126-48f8-99de-5f1768ba2b62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ccc6d6e-8126-48f8-99de-5f1768ba2b62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-599c3717-5b1c-4cd6-9060-87ceb8f5e202' class='xr-var-data-in' type='checkbox'><label for='data-599c3717-5b1c-4cd6-9060-87ceb8f5e202' 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>long_name :</span></dt><dd>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2015-01-01 12:00:00 ... 2100-12-...</div><input id='attrs-d18f1a27-d5c1-4031-869d-f0fda8dc989f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d18f1a27-d5c1-4031-869d-f0fda8dc989f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-786bc810-f510-41b0-be52-3ce5262a22a5' class='xr-var-data-in' type='checkbox'><label for='data-786bc810-f510-41b0-be52-3ce5262a22a5' 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(2015, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e5e80293-4630-4744-b2d0-c9f969124a82' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e5e80293-4630-4744-b2d0-c9f969124a82' 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>tasmax</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=(365, 360, 360), meta=np.ndarray&gt;</div><input id='attrs-db400210-6814-4f82-940d-c2dd4ec2aac4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db400210-6814-4f82-940d-c2dd4ec2aac4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-345c6a80-961d-47ef-b160-ce0f4a28ac6d' class='xr-var-data-in' type='checkbox'><label for='data-345c6a80-961d-47ef-b160-ce0f4a28ac6d' 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: mean time: maximum</dd><dt><span>comment :</span></dt><dd>maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute &#x27;time: max&#x27;)</dd><dt><span>history :</span></dt><dd>2019-08-11T09:50:24Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2019-08-11T09:50:24Z altered by CMOR: Inverted axis: lat.</dd><dt><span>long_name :</span></dt><dd>Daily Maximum Near-Surface Air Temperature</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>\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> 121.24 GiB </td>\n",
       "                        <td> 180.45 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (31390, 720, 1440) </td>\n",
       "                        <td> (365, 360, 360) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 689 Tasks </td>\n",
       "                        <td> 688 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "aerosol: none\n",
       "atmos: ECHAM v6.3 (T63; 192 x 96 longitude/latitude; 47 levels; top level 1 Pa)\n",
       "atmosChem: none\n",
       "land: JSBACH v3.1\n",
       "landIce: none\n",
       "ocean: NEMO v3.4 (NEMO v3.4, tripolar primarily 1deg; 384 x 362 longitude/latitude; 46 levels; top grid cell 0-6 m)\n",
       "ocnBgchem: none\n",
       "seaIce: CICE4.1</dd><dt><span>source_id :</span></dt><dd>NESM3</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>day</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/01f58d9c-1317-467e-813d-a2358cdf7954\n",
       "hdl:21.14100/e027cda7-ac28-4e00-b03f-53ad2e6d30b4\n",
       "hdl:21.14100/3898bc79-5174-4fcc-9637-4573914ad548</dd><dt><span>variable_id :</span></dt><dd>tasmax</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>version_id :</span></dt><dd>v20190811</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 720, lon: 1440, time: 31390)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
       "  * lon      (lon) float64 -179.9 -179.6 -179.4 -179.1 ... 179.4 179.6 179.9\n",
       "  * time     (time) object 2015-01-01 12:00:00 ... 2100-12-31 12:00:00\n",
       "Data variables:\n",
       "    tasmax   (time, lat, lon) float32 dask.array<chunksize=(365, 360, 360), meta=np.ndarray>\n",
       "Attributes: (12/47)\n",
       "    Conventions:                  CF-1.7 CMIP-6.2\n",
       "    activity_id:                  ScenarioMIP\n",
       "    contact:                      climatesci@rhg.com\n",
       "    creation_date:                2019-08-11T09:50:24Z\n",
       "    data_specs_version:           01.00.30\n",
       "    dc6_bias_correction_method:   Quantile Delta Method (QDM)\n",
       "    ...                           ...\n",
       "    sub_experiment_id:            none\n",
       "    table_id:                     day\n",
       "    tracking_id:                  hdl:21.14100/01f58d9c-1317-467e-813d-a2358c...\n",
       "    variable_id:                  tasmax\n",
       "    variant_label:                r1i1p1f1\n",
       "    version_id:                   v20190811"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "asset = item.assets[\"tasmax\"]\n",
    "\n",
    "ds = xr.open_dataset(asset.href, **asset.extra_fields[\"xarray:open_kwargs\"])\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acf2bbfb-e364-4f61-ac17-85fd1ec7fa3b",
   "metadata": {},
   "source": [
    "And form a datacube with [`xarray.combine_by_coords`](https://docs.xarray.dev/en/stable/generated/xarray.combine_by_coords.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c9b4c42c-e016-40e6-95db-4d80d781b2d9",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 720, lon: 1440, time: 31390)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
       "  * lon      (lon) float64 -179.9 -179.6 -179.4 -179.1 ... 179.4 179.6 179.9\n",
       "  * time     (time) object 2015-01-01 12:00:00 ... 2100-12-31 12:00:00\n",
       "Data variables:\n",
       "    pr       (time, lat, lon) float64 dask.array&lt;chunksize=(365, 360, 360), meta=np.ndarray&gt;\n",
       "    tasmax   (time, lat, lon) float32 dask.array&lt;chunksize=(365, 360, 360), meta=np.ndarray&gt;\n",
       "    tasmin   (time, lat, lon) float32 dask.array&lt;chunksize=(365, 360, 360), meta=np.ndarray&gt;\n",
       "Attributes: (12/40)\n",
       "    Conventions:                  CF-1.7 CMIP-6.2\n",
       "    activity_id:                  ScenarioMIP\n",
       "    contact:                      climatesci@rhg.com\n",
       "    data_specs_version:           01.00.30\n",
       "    dc6_bias_correction_method:   Quantile Delta Method (QDM)\n",
       "    dc6_citation:                 Please refer to https://github.com/ClimateI...\n",
       "    ...                           ...\n",
       "    source_type:                  AOGCM\n",
       "    sub_experiment:               none\n",
       "    sub_experiment_id:            none\n",
       "    table_id:                     day\n",
       "    variant_label:                r1i1p1f1\n",
       "    version_id:                   v20190811</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-914825b3-2f4b-4c54-97b0-204aef062ad4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-914825b3-2f4b-4c54-97b0-204aef062ad4' 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>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>time</span>: 31390</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-49f6d576-ed56-4cd5-b7b3-bdfcb478ad23' class='xr-section-summary-in' type='checkbox'  checked><label for='section-49f6d576-ed56-4cd5-b7b3-bdfcb478ad23' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-a9d33006-19bd-4458-bdf2-c22c05bd6549' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9d33006-19bd-4458-bdf2-c22c05bd6549' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-010a59b0-df57-4c7a-bb0d-c16724996c45' class='xr-var-data-in' type='checkbox'><label for='data-010a59b0-df57-4c7a-bb0d-c16724996c45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875])</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'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-0065e612-ef47-4e0b-852a-4496eb7ba6c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0065e612-ef47-4e0b-852a-4496eb7ba6c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40df4b95-4c52-4810-84ec-07cc08b2d6a7' class='xr-var-data-in' type='checkbox'><label for='data-40df4b95-4c52-4810-84ec-07cc08b2d6a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2015-01-01 12:00:00 ... 2100-12-...</div><input id='attrs-bdee7319-8730-41b0-a965-4c04aa97cbc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdee7319-8730-41b0-a965-4c04aa97cbc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a7c3c74-7832-4961-b813-51a9d62279f8' class='xr-var-data-in' type='checkbox'><label for='data-0a7c3c74-7832-4961-b813-51a9d62279f8' 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(2015, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2015, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8ba0b66e-b434-484c-b64c-c4559e224648' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8ba0b66e-b434-484c-b64c-c4559e224648' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(365, 360, 360), meta=np.ndarray&gt;</div><input id='attrs-8e3e811f-71cd-48d7-a19e-c90976a10a35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e3e811f-71cd-48d7-a19e-c90976a10a35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6ac5c66-7885-4931-942b-deea8da7a50e' class='xr-var-data-in' type='checkbox'><label for='data-b6ac5c66-7885-4931-942b-deea8da7a50e' 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>mm day-1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 242.48 GiB </td>\n",
       "                        <td> 360.90 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (31390, 720, 1440) </td>\n",
       "                        <td> (365, 360, 360) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 689 Tasks </td>\n",
       "                        <td> 688 Chunks </td>\n",
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       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</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=(365, 360, 360), meta=np.ndarray&gt;</div><input id='attrs-96c3fcf2-252c-4621-8bdf-270a019db403' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96c3fcf2-252c-4621-8bdf-270a019db403' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42633e53-eef1-45c6-beca-facf0f34e74f' class='xr-var-data-in' type='checkbox'><label for='data-42633e53-eef1-45c6-beca-facf0f34e74f' 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: mean time: maximum</dd><dt><span>comment :</span></dt><dd>maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute &#x27;time: max&#x27;)</dd><dt><span>history :</span></dt><dd>2019-08-11T09:50:24Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2019-08-11T09:50:24Z altered by CMOR: Inverted axis: lat.</dd><dt><span>long_name :</span></dt><dd>Daily Maximum Near-Surface Air Temperature</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",
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       "                        <th> Bytes </th>\n",
       "                        <td> 121.24 GiB </td>\n",
       "                        <td> 180.45 MiB </td>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 689 Tasks </td>\n",
       "                        <td> 688 Chunks </td>\n",
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       "                    <th> Type </th>\n",
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       "aerosol: none\n",
       "atmos: ECHAM v6.3 (T63; 192 x 96 longitude/latitude; 47 levels; top level 1 Pa)\n",
       "atmosChem: none\n",
       "land: JSBACH v3.1\n",
       "landIce: none\n",
       "ocean: NEMO v3.4 (NEMO v3.4, tripolar primarily 1deg; 384 x 362 longitude/latitude; 46 levels; top grid cell 0-6 m)\n",
       "ocnBgchem: none\n",
       "seaIce: CICE4.1</dd><dt><span>source_id :</span></dt><dd>NESM3</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>day</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>version_id :</span></dt><dd>v20190811</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 720, lon: 1440, time: 31390)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
       "  * lon      (lon) float64 -179.9 -179.6 -179.4 -179.1 ... 179.4 179.6 179.9\n",
       "  * time     (time) object 2015-01-01 12:00:00 ... 2100-12-31 12:00:00\n",
       "Data variables:\n",
       "    pr       (time, lat, lon) float64 dask.array<chunksize=(365, 360, 360), meta=np.ndarray>\n",
       "    tasmax   (time, lat, lon) float32 dask.array<chunksize=(365, 360, 360), meta=np.ndarray>\n",
       "    tasmin   (time, lat, lon) float32 dask.array<chunksize=(365, 360, 360), meta=np.ndarray>\n",
       "Attributes: (12/40)\n",
       "    Conventions:                  CF-1.7 CMIP-6.2\n",
       "    activity_id:                  ScenarioMIP\n",
       "    contact:                      climatesci@rhg.com\n",
       "    data_specs_version:           01.00.30\n",
       "    dc6_bias_correction_method:   Quantile Delta Method (QDM)\n",
       "    dc6_citation:                 Please refer to https://github.com/ClimateI...\n",
       "    ...                           ...\n",
       "    source_type:                  AOGCM\n",
       "    sub_experiment:               none\n",
       "    sub_experiment_id:            none\n",
       "    table_id:                     day\n",
       "    variant_label:                r1i1p1f1\n",
       "    version_id:                   v20190811"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "asset = item.assets[\"tasmax\"]\n",
    "ds = xr.combine_by_coords(\n",
    "    [\n",
    "        xr.open_dataset(asset.href, **asset.extra_fields[\"xarray:open_kwargs\"])\n",
    "        for asset in item.assets.values()\n",
    "    ],\n",
    "    combine_attrs=\"drop_conflicts\",\n",
    ")\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef38a5be-3961-4795-8350-b2673206530d",
   "metadata": {},
   "source": [
    "We'll load the data for a specific date and plot the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "537443aa-379f-4405-b12d-036017079ee0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-cil-gdpcir-cil-gdpcir-example.ipynb/9.png\"/>"
      ],
      "text/plain": [
       "<Figure size 960x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmax = ds.tasmax.isel(time=0).load()\n",
    "tmax.plot(size=10);"
   ]
  }
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