datasets/terraclimate/terraclimate-example.ipynb

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    "## Accessing TerraClimate data with the Planetary Computer STAC API\n",
    "\n",
    "[TerraClimate](http://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2019. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. The data cover the period from 1958-2019.\n",
    "\n",
    "This example will show you how temperature has increased over the past 60 years across the globe.\n",
    "\n",
    "### Environment setup"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import pystac_client\n",
    "import planetary_computer\n",
    "import xarray as xr\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", \"invalid value\", RuntimeWarning)"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "### Data access\n",
    "\n",
    "The datasets hosted by the Planetary Computer are available from [Azure Blob Storage](https://docs.microsoft.com/en-us/azure/storage/blobs/). We'll use [pystac-client](https://pystac-client.readthedocs.io/) to search the Planetary Computer's [STAC API](https://planetarycomputer.microsoft.com/api/stac/v1/docs) for the subset of the data that we care about, and then we'll load the data directly from Azure Blob Storage. We'll specify a `modifier` so that we can access the data stored in the Planetary Computer's private Blob Storage Containers. See [Reading from the STAC API](https://planetarycomputer.microsoft.com/docs/quickstarts/reading-stac/) and [Using tokens for data access](https://planetarycomputer.microsoft.com/docs/concepts/sas/) for more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "catalog = pystac_client.Client.open(\n",
    "    \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
    "    modifier=planetary_computer.sign_inplace,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "collection = catalog.get_collection(\"terraclimate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The collection contains assets, which are links to the root of a Zarr store, which can be opened with xarray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Asset href=az://cpdata/raw/terraclimate/4000m/raster.zarr>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asset = collection.assets[\"zarr-abfs\"]\n",
    "asset"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                 (time: 744, lat: 4320, lon: 8640, crs: 1)\n",
       "Coordinates:\n",
       "  * crs                     (crs) int16 3\n",
       "  * lat                     (lat) float64 89.98 89.94 89.9 ... -89.94 -89.98\n",
       "  * lon                     (lon) float64 -180.0 -179.9 -179.9 ... 179.9 180.0\n",
       "  * time                    (time) datetime64[ns] 1958-01-01 ... 2019-12-01\n",
       "Data variables: (12/18)\n",
       "    aet                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    def                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    pdsi                    (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    pet                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    ppt                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    ppt_station_influence   (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    ...                      ...\n",
       "    tmin                    (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    tmin_station_influence  (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    vap                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    vap_station_influence   (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    vpd                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    ws                      (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;</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-c79b256c-afcf-4316-b290-e85638043607' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c79b256c-afcf-4316-b290-e85638043607' 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>: 744</li><li><span class='xr-has-index'>lat</span>: 4320</li><li><span class='xr-has-index'>lon</span>: 8640</li><li><span class='xr-has-index'>crs</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ef76dd3b-1032-472a-8dbe-3bcc61cdc623' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ef76dd3b-1032-472a-8dbe-3bcc61cdc623' 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'>crs</span></div><div class='xr-var-dims'>(crs)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>3</div><input id='attrs-6b6b7a15-84d4-48c8-9fbf-7890762f16b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b6b7a15-84d4-48c8-9fbf-7890762f16b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f37815b6-d171-4708-8224-c4574ef6c88d' class='xr-var-data-in' type='checkbox'><label for='data-f37815b6-d171-4708-8224-c4574ef6c88d' 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>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>inverse_flattening :</span></dt><dd>[298.257223563]</dd><dt><span>long_name :</span></dt><dd>crs</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>[0.0]</dd><dt><span>semi_major_axis :</span></dt><dd>[6378137.0]</dd></dl></div><div class='xr-var-data'><pre>array([3], dtype=int16)</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'>89.98 89.94 89.9 ... -89.94 -89.98</div><input id='attrs-87c20942-a61a-4e55-83ba-08bb44ba7247' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87c20942-a61a-4e55-83ba-08bb44ba7247' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e4bf60d-9712-4eca-8f52-080e56093fd7' class='xr-var-data-in' type='checkbox'><label for='data-0e4bf60d-9712-4eca-8f52-080e56093fd7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>description :</span></dt><dd>Latitude of the center of the grid cell</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 89.979167,  89.9375  ,  89.895833, ..., -89.895833, -89.9375  ,\n",
       "       -89.979167])</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'>-180.0 -179.9 ... 179.9 180.0</div><input id='attrs-632646a8-1f84-4eab-820f-9e2e3631750e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-632646a8-1f84-4eab-820f-9e2e3631750e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4063623d-568a-4d43-ba4e-17e9753fbc73' class='xr-var-data-in' type='checkbox'><label for='data-4063623d-568a-4d43-ba4e-17e9753fbc73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>description :</span></dt><dd>Longitude of the center of the grid cell</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-179.979167, -179.9375  , -179.895833, ...,  179.895833,  179.9375  ,\n",
       "        179.979167])</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1958-01-01 ... 2019-12-01</div><input id='attrs-bcc62be2-1361-418a-8847-5baf449e82aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcc62be2-1361-418a-8847-5baf449e82aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d05919c-bf5d-4d2d-a4f1-cd1994cc4c3d' class='xr-var-data-in' type='checkbox'><label for='data-9d05919c-bf5d-4d2d-a4f1-cd1994cc4c3d' 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>description :</span></dt><dd>days since 1900-01-01</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;1958-01-01T00:00:00.000000000&#x27;, &#x27;1958-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-03-01T00:00:00.000000000&#x27;, ..., &#x27;2019-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T00:00:00.000000000&#x27;, &#x27;2019-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-177c43b0-6c2c-44f7-8f5e-155ab9d02995' class='xr-section-summary-in' type='checkbox'  ><label for='section-177c43b0-6c2c-44f7-8f5e-155ab9d02995' class='xr-section-summary' >Data variables: <span>(18)</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>aet</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=(12, 1440, 1440), meta=np.ndarray&gt;</div><input id='attrs-e53b5cbf-8cb0-4c29-b736-4f8fa31a0869' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e53b5cbf-8cb0-4c29-b736-4f8fa31a0869' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28a9049d-462a-4edc-8a3e-b8df2dc09ac2' class='xr-var-data-in' type='checkbox'><label for='data-28a9049d-462a-4edc-8a3e-b8df2dc09ac2' 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>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Actual Evapotranspiration</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>water_evaporation_amount</dd><dt><span>standard_name :</span></dt><dd>water_evaporation_amount</dd><dt><span>units :</span></dt><dd>mm</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> 103.45 GiB </td>\n",
       "                        <td> 94.92 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (744, 4320, 8640) </td>\n",
       "                        <td> (12, 1440, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 1117 Tasks </td>\n",
       "                        <td> 1116 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "            </table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>def</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=(12, 1440, 1440), meta=np.ndarray&gt;</div><input id='attrs-908d9b77-ea4e-40d7-b59e-73e20544602c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-908d9b77-ea4e-40d7-b59e-73e20544602c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba527622-23c2-4192-8b45-6e6a94f19ba8' class='xr-var-data-in' type='checkbox'><label for='data-ba527622-23c2-4192-8b45-6e6a94f19ba8' 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>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Climatic Water Deficit</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>water_potential_evaporation_amount_minus_water_evaporation_amount</dd><dt><span>standard_name :</span></dt><dd>water_potential_evaporation_amount_minus_water_evaporation_amount</dd><dt><span>units :</span></dt><dd>mm</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> 103.45 GiB </td>\n",
       "                        <td> 94.92 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (744, 4320, 8640) </td>\n",
       "                        <td> (12, 1440, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 1117 Tasks </td>\n",
       "                        <td> 1116 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ppt</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=(12, 1440, 1440), meta=np.ndarray&gt;</div><input id='attrs-3f502888-4d9e-4ab9-b448-0254ec765f3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f502888-4d9e-4ab9-b448-0254ec765f3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af00770f-50b6-474c-b151-0d43e1ce4d18' class='xr-var-data-in' type='checkbox'><label for='data-af00770f-50b6-474c-b151-0d43e1ce4d18' 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>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Accumulated Precipitation</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>precipitation_amount</dd><dt><span>standard_name :</span></dt><dd>precipitation_amount</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ppt_station_influence</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=(12, 1440, 1440), meta=np.ndarray&gt;</div><input id='attrs-765e111a-e884-4449-8336-f0b8aac819dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-765e111a-e884-4449-8336-f0b8aac819dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f003942-8670-4a40-90e0-909198ed4d9a' class='xr-var-data-in' type='checkbox'><label for='data-4f003942-8670-4a40-90e0-909198ed4d9a' 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>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>A number between 0 and 8, indicating the number of stations contributing to each grid value directly from CRU and interpolated to the TerraClimate grid. When this value is greater than 1, CRU data is used for the anomalies in the method. When this value is 0, JRA-55/ERA-20C/ERA-INTERIM is used for anomalies in the method.</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>station_influence</dd><dt><span>standard_name :</span></dt><dd>station_influence</dd><dt><span>units :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (12, 1440, 1440) </td>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vap_station_influence</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=(12, 1440, 1440), meta=np.ndarray&gt;</div><input id='attrs-3457f1b3-95ca-4f32-9543-958cc75764ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3457f1b3-95ca-4f32-9543-958cc75764ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e9c534f-a4fb-491d-b5c7-2e492000316b' class='xr-var-data-in' type='checkbox'><label for='data-0e9c534f-a4fb-491d-b5c7-2e492000316b' 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>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>A number between 0 and 8, indicating the number of stations contributing to each grid value directly from CRU and interpolated to the TerraClimate grid. When this value is greater than 1, CRU data is used for the anomalies in the method. When this value is 0, JRA-55/ERA-20C/ERA-INTERIM is used for anomalies in the method.</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>station_influence</dd><dt><span>standard_name :</span></dt><dd>station_influence</dd><dt><span>units :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vpd</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=(12, 1440, 1440), meta=np.ndarray&gt;</div><input id='attrs-545f541d-6e9b-4138-a399-ba118ca1d7ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-545f541d-6e9b-4138-a399-ba118ca1d7ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc754a65-cf8f-4e69-89f4-13a2e541e519' class='xr-var-data-in' type='checkbox'><label for='data-dc754a65-cf8f-4e69-89f4-13a2e541e519' 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>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Mean Vapor Pressure Deficit</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>vapor_pressure_deficit</dd><dt><span>standard_name :</span></dt><dd>vapor_pressure_deficit</dd><dt><span>units :</span></dt><dd>kPa</dd></dl></div><div class='xr-var-data'><table>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                 (time: 744, lat: 4320, lon: 8640, crs: 1)\n",
       "Coordinates:\n",
       "  * crs                     (crs) int16 3\n",
       "  * lat                     (lat) float64 89.98 89.94 89.9 ... -89.94 -89.98\n",
       "  * lon                     (lon) float64 -180.0 -179.9 -179.9 ... 179.9 180.0\n",
       "  * time                    (time) datetime64[ns] 1958-01-01 ... 2019-12-01\n",
       "Data variables: (12/18)\n",
       "    aet                     (time, lat, lon) float32 dask.array<chunksize=(12, 1440, 1440), meta=np.ndarray>\n",
       "    def                     (time, lat, lon) float32 dask.array<chunksize=(12, 1440, 1440), meta=np.ndarray>\n",
       "    pdsi                    (time, lat, lon) float32 dask.array<chunksize=(12, 1440, 1440), meta=np.ndarray>\n",
       "    pet                     (time, lat, lon) float32 dask.array<chunksize=(12, 1440, 1440), meta=np.ndarray>\n",
       "    ppt                     (time, lat, lon) float32 dask.array<chunksize=(12, 1440, 1440), meta=np.ndarray>\n",
       "    ppt_station_influence   (time, lat, lon) float32 dask.array<chunksize=(12, 1440, 1440), meta=np.ndarray>\n",
       "    ...                      ...\n",
       "    tmin                    (time, lat, lon) float32 dask.array<chunksize=(12, 1440, 1440), meta=np.ndarray>\n",
       "    tmin_station_influence  (time, lat, lon) float32 dask.array<chunksize=(12, 1440, 1440), meta=np.ndarray>\n",
       "    vap                     (time, lat, lon) float32 dask.array<chunksize=(12, 1440, 1440), meta=np.ndarray>\n",
       "    vap_station_influence   (time, lat, lon) float32 dask.array<chunksize=(12, 1440, 1440), meta=np.ndarray>\n",
       "    vpd                     (time, lat, lon) float32 dask.array<chunksize=(12, 1440, 1440), meta=np.ndarray>\n",
       "    ws                      (time, lat, lon) float32 dask.array<chunksize=(12, 1440, 1440), meta=np.ndarray>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_zarr(\n",
    "    asset.href,\n",
    "    storage_options=asset.extra_fields[\"xarray:storage_options\"],\n",
    "    consolidated=True,\n",
    ")\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll process the data in parallel using [Dask](https://dask.org)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://pcc-staging.westeurope.cloudapp.azure.com/compute/services/dask-gateway/clusters/staging.d2b3ad3900e64e66af1e011fc076fd70/status\n"
     ]
    }
   ],
   "source": [
    "from dask_gateway import GatewayCluster\n",
    "\n",
    "cluster = GatewayCluster()\n",
    "cluster.scale(16)\n",
    "client = cluster.get_client()\n",
    "print(cluster.dashboard_link)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The link printed out above can be opened in a new tab or the [Dask labextension](https://github.com/dask/dask-labextension). See [Scale with Dask](https://planetarycomputer.microsoft.com/docs/quickstarts/scale-with-dask/) for more on using Dask, and how to access the Dashboard.\n",
    "\n",
    "### Analyze and plot global temperature\n",
    "\n",
    "We can quickly plot a map of one of the variables. In this case, we are downsampling (coarsening) the dataset for easier plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-terraclimate-terraclimate-example.ipynb/7.png\"/>"
      ],
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "average_max_temp = ds.isel(time=-1)[\"tmax\"].coarsen(lat=8, lon=8).mean().load()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20, 10), subplot_kw=dict(projection=ccrs.Robinson()))\n",
    "\n",
    "average_max_temp.plot(ax=ax, transform=ccrs.PlateCarree())\n",
    "ax.coastlines();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how temperature has changed over the observational record, when averaged across the entire domain. Since we'll do some other calculations below we'll also add `.load()` to execute the command instead of specifying it lazily.  Note that there are some data quality issues before 1965 so we'll start our analysis there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "temperature = (\n",
    "    ds[\"tmax\"].sel(time=slice(\"1965\", None)).mean(dim=[\"lat\", \"lon\"]).persist()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-terraclimate-terraclimate-example.ipynb/9.png\"/>"
      ],
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temperature.plot(figsize=(12, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With all the seasonal fluctuations (from summer and winter) though, it can be hard to see any obvious trends. So let's try grouping by year and plotting that timeseries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-terraclimate-terraclimate-example.ipynb/10.png\"/>"
      ],
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temperature.groupby(\"time.year\").mean().plot(figsize=(12, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the increase in temperature is obvious, even when averaged across the entire domain.\n",
    "\n",
    "Now, let's see how those changes are different in different parts of the world. And let's focus just on summer months in the northern hemisphere, when it's hottest. Let's take a climatological slice at the beginning of the period and the same at the end of the period, calculate the difference, and map it to see how different parts of the world have changed differently.\n",
    "\n",
    "First we'll just grab the summer months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.5 s, sys: 425 ms, total: 1.93 s\n",
      "Wall time: 47.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import dask\n",
    "\n",
    "summer_months = [6, 7, 8]\n",
    "summer = ds.tmax.where(ds.time.dt.month.isin(summer_months), drop=True)\n",
    "\n",
    "early_period = slice(\"1958-01-01\", \"1988-12-31\")\n",
    "late_period = slice(\"1988-01-01\", \"2018-12-31\")\n",
    "\n",
    "early, late = dask.compute(\n",
    "    summer.sel(time=early_period).mean(dim=\"time\"),\n",
    "    summer.sel(time=late_period).mean(dim=\"time\"),\n",
    ")\n",
    "increase = (late - early).coarsen(lat=8, lon=8).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-terraclimate-terraclimate-example.ipynb/12.png\"/>"
      ],
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 10), subplot_kw=dict(projection=ccrs.Robinson()))\n",
    "\n",
    "increase.plot(ax=ax, transform=ccrs.PlateCarree(), robust=True)\n",
    "ax.coastlines();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows us that changes in summer temperature haven't been felt equally around the globe.  Note the enhanced warming in the polar regions, a phenomenon known as \"Arctic amplification\"."
   ]
  }
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