datasets/nasa-nex-gddp-cmip6/nasa-nex-gddp-cmip6-example.ipynb

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    "## Accessing NEX-GDDP-CMIP6 data with the Planetary Computer STAC API\n",
    "\n",
    "The [NEX-GDDP-CMIP6 dataset](https://planetarycomputer.microsoft.com/dataset/nasa-nex-gddp-cmip6) offers global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four “Tier 1” greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.\n",
    "\n",
    "This dataset uses a Bias-Correction Spatial Disaggregation method to downscale the original General Circulation Model runs to the finer 0.25° resolution. See the [tech note](https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf) from the [product homepage](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6) for more details.\n",
    "\n",
    "The NEX-GDDP-CMIP6 files are stored as NetCDF in Azure Blob Storage. Each STAC Item in this collection describes a single year for one scenario for one model."
   ]
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   "source": [
    "import planetary_computer\n",
    "import xarray as xr\n",
    "import fsspec\n",
    "import pystac_client"
   ]
  },
  {
   "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."
   ]
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   "id": "32470ec3-5395-4337-a967-63c5315468d2",
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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",
    ")"
   ]
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  {
   "cell_type": "markdown",
   "id": "929c3993-8efa-4c36-a8a9-9e70c54e5f82",
   "metadata": {},
   "source": [
    "### Understanding the metadata\n",
    "\n",
    "The STAC metadata on the Collection, items, and assets provide information on what data is available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "collection = catalog.get_collection(\"nasa-nex-gddp-cmip6\")"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "As usual, the collection object contains information about the dataset including its spatio-temporal extent, license, and so on. We also have information unique to CMIP6. The collection is organized by `{model}-{scenario}-{year}`: there's is a single STAC item for each (valid) combination (data is not available for some; see Table 1 in the [tech note](https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf) for more). The valid values for each of these are stored in the collection's summaries:"
   ]
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   "id": "375ec285-5149-4ed7-9b0b-6e29f67cd7ab",
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    {
     "data": {
      "text/plain": [
       "['ACCESS-CM2', 'ACCESS-ESM1-5', 'BCC-CSM2-MR', 'CESM2', 'CESM2-WACCM']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the models. There are ~30 in total.\n",
    "collection.summaries.get_list(\"cmip6:model\")[:5]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "['historical', 'ssp245', 'ssp585']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the scenarios\n",
    "collection.summaries.get_list(\"cmip6:scenario\")"
   ]
  },
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   "source": [
    "The \"historical\" scenario covers the years 1950 - 2014 (inclusive). The \"ssp245\" and \"ssp585\" cover the years 2015 - 2100 (inclusive).\n",
    "\n",
    "Each item includes a handful of assets, one per variable, where each asset is a single NetCDF file with the data for that variable for that model-scenario-year."
   ]
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   "cell_type": "code",
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    {
     "data": {
      "text/plain": [
       "['hurs', 'huss', 'pr', 'rlds', 'rsds', 'sfcWind', 'tas', 'tasmax', 'tasmin']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# list the variables\n",
    "collection.summaries.get_list(\"cmip6:variable\")"
   ]
  },
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   "cell_type": "markdown",
   "id": "e39e8df4-82f0-4833-b7b0-f7f63761a38d",
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   "source": [
    "### Querying the STAC API\n",
    "\n",
    "Each STAC item covers the same spatial region, so when using the STAC API you're likely filtering on some combination of time, model, and scenario. For example, we can get the STAC items for the \"ACCESS-CM2\" model for the years 1950 - 2000."
   ]
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   "cell_type": "code",
   "execution_count": 7,
   "id": "0b87d961-c786-4a3a-9272-6563fe8fcc68",
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    {
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      "text/plain": [
       "51"
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     "execution_count": 7,
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   ],
   "source": [
    "search = catalog.search(\n",
    "    collections=[\"nasa-nex-gddp-cmip6\"],\n",
    "    datetime=\"1950/2000\",\n",
    "    query={\"cmip6:model\": {\"eq\": \"ACCESS-CM2\"}},\n",
    ")\n",
    "items = search.get_all_items()\n",
    "len(items)"
   ]
  },
  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "Eacah of these items has nine assets, one per variable, which point to the NetCDF files in Azure Blob Storage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "79f2daa6-3d90-432b-bd58-1d6403679341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pr', 'tas', 'hurs', 'huss', 'rlds', 'rsds', 'tasmax', 'tasmin', 'sfcWind']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "item = items[0]\n",
    "list(item.assets)"
   ]
  },
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   "cell_type": "markdown",
   "id": "fd77db07-d331-477f-aaf3-b3d1d822f2f6",
   "metadata": {},
   "source": [
    "### Loading data\n",
    "\n",
    "Once you have a STAC item or items, you can load the data directly from Blob Storage using xarray."
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
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       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 366, lat: 600, lon: 1440)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2000-01-01T12:00:00 ... 2000-12-31T12:00:00\n",
       "  * lat      (lat) float64 -59.88 -59.62 -59.38 -59.12 ... 89.38 89.62 89.88\n",
       "  * lon      (lon) float64 0.125 0.375 0.625 0.875 ... 359.1 359.4 359.6 359.9\n",
       "Data variables:\n",
       "    hurs     (time, lat, lon) float32 ...\n",
       "Attributes: (12/22)\n",
       "    activity:              NEX-GDDP-CMIP6\n",
       "    contact:               Dr. Rama Nemani: rama.nemani@nasa.gov, Dr. Bridget...\n",
       "    Conventions:           CF-1.7\n",
       "    creation_date:         2021-10-04T13:59:35.374080+00:00\n",
       "    frequency:             day\n",
       "    institution:           NASA Earth Exchange, NASA Ames Research Center, Mo...\n",
       "    ...                    ...\n",
       "    history:               2021-10-04T13:59:35.374080+00:00: install global a...\n",
       "    disclaimer:            This data is considered provisional and subject to...\n",
       "    external_variables:    areacella\n",
       "    cmip6_source_id:       ACCESS-CM2\n",
       "    cmip6_institution_id:  CSIRO-ARCCSS\n",
       "    cmip6_license:         CC-BY-SA 4.0</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-4e2ebe01-4c31-4116-bf0a-b5c1e308fc14' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4e2ebe01-4c31-4116-bf0a-b5c1e308fc14' 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>: 366</li><li><span class='xr-has-index'>lat</span>: 600</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-fdc2ba93-bc93-4de6-84f8-0b8c5a12b23f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fdc2ba93-bc93-4de6-84f8-0b8c5a12b23f' 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'>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'>2000-01-01T12:00:00 ... 2000-12-...</div><input id='attrs-d75dadaf-4bcc-4005-8044-d25e0673334c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d75dadaf-4bcc-4005-8044-d25e0673334c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1a648ba-0d0b-4bb0-9f3f-866f371b2180' class='xr-var-data-in' type='checkbox'><label for='data-e1a648ba-0d0b-4bb0-9f3f-866f371b2180' 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>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;2000-01-01T12:00:00.000000000&#x27;, &#x27;2000-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;2000-01-03T12:00:00.000000000&#x27;, ..., &#x27;2000-12-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2000-12-30T12:00:00.000000000&#x27;, &#x27;2000-12-31T12:00:00.000000000&#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'>-59.88 -59.62 ... 89.62 89.88</div><input id='attrs-5b90dacb-850c-4109-a752-df4d21c0a54b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b90dacb-850c-4109-a752-df4d21c0a54b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-357d4795-77ce-44c0-832a-4f9a67f40a0d' class='xr-var-data-in' type='checkbox'><label for='data-357d4795-77ce-44c0-832a-4f9a67f40a0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-59.875, -59.625, -59.375, ...,  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'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-0885f22a-144f-4e12-862b-dd1049a28576' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0885f22a-144f-4e12-862b-dd1049a28576' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f57b383-c279-4ddb-b1ae-3db53262dc68' class='xr-var-data-in' type='checkbox'><label for='data-4f57b383-c279-4ddb-b1ae-3db53262dc68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
       "       3.59875e+02])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-05c6c184-3173-4622-a8d6-891fe6556d19' class='xr-section-summary-in' type='checkbox'  checked><label for='section-05c6c184-3173-4622-a8d6-891fe6556d19' 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>hurs</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-afb5e289-1242-4e1b-b390-e49b6a4f909b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afb5e289-1242-4e1b-b390-e49b6a4f909b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-181d69a9-c774-4483-8ab0-b0cd665b0e32' class='xr-var-data-in' type='checkbox'><label for='data-181d69a9-c774-4483-8ab0-b0cd665b0e32' 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>relative_humidity</dd><dt><span>long_name :</span></dt><dd>Near-Surface Relative Humidity</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>comment :</span></dt><dd>The relative humidity with respect to liquid water for T&gt; 0 C, and with respect to ice for T&lt;0 C.</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>[316224000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a4c31fb-d10a-4c54-85e0-e5c6e5bdfc59' class='xr-section-summary-in' type='checkbox'  ><label for='section-6a4c31fb-d10a-4c54-85e0-e5c6e5bdfc59' class='xr-section-summary' >Attributes: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>activity :</span></dt><dd>NEX-GDDP-CMIP6</dd><dt><span>contact :</span></dt><dd>Dr. Rama Nemani: rama.nemani@nasa.gov, Dr. Bridget Thrasher: bridget@climateanalyticsgroup.org</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>creation_date :</span></dt><dd>2021-10-04T13:59:35.374080+00:00</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>institution :</span></dt><dd>NASA Earth Exchange, NASA Ames Research Center, Moffett Field, CA 94035</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>product :</span></dt><dd>output</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>source :</span></dt><dd>BCSD</dd><dt><span>scenario :</span></dt><dd>historical</dd><dt><span>references :</span></dt><dd>BCSD method: Thrasher et al., 2012, Hydrol. Earth Syst. Sci.,16, 3309-3314. Ref period obs: latest version of the Princeton Global Meteorological Forcings (http://hydrology.princeton.edu/data.php), based on Sheffield et al., 2006, J. Climate, 19 (13), 3088-3111.</dd><dt><span>version :</span></dt><dd>1.0</dd><dt><span>tracking_id :</span></dt><dd>866d0659-683f-4f39-9347-7dd1ef5b3b36</dd><dt><span>title :</span></dt><dd>ACCESS-CM2, r1i1p1f1, historical, global downscaled CMIP6 climate projection data</dd><dt><span>resolution_id :</span></dt><dd>0.25 degree</dd><dt><span>history :</span></dt><dd>2021-10-04T13:59:35.374080+00:00: install global attributes</dd><dt><span>disclaimer :</span></dt><dd>This data is considered provisional and subject to change. This data is provided as is without any warranty of any kind, either express or implied, arising by law or otherwise, including but not limited to warranties of completeness, non-infringement, accuracy, merchantability, or fitness for a particular purpose. The user assumes all risk associated with the use of, or inability to use, this data.</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>cmip6_source_id :</span></dt><dd>ACCESS-CM2</dd><dt><span>cmip6_institution_id :</span></dt><dd>CSIRO-ARCCSS</dd><dt><span>cmip6_license :</span></dt><dd>CC-BY-SA 4.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 366, lat: 600, lon: 1440)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2000-01-01T12:00:00 ... 2000-12-31T12:00:00\n",
       "  * lat      (lat) float64 -59.88 -59.62 -59.38 -59.12 ... 89.38 89.62 89.88\n",
       "  * lon      (lon) float64 0.125 0.375 0.625 0.875 ... 359.1 359.4 359.6 359.9\n",
       "Data variables:\n",
       "    hurs     (time, lat, lon) float32 ...\n",
       "Attributes: (12/22)\n",
       "    activity:              NEX-GDDP-CMIP6\n",
       "    contact:               Dr. Rama Nemani: rama.nemani@nasa.gov, Dr. Bridget...\n",
       "    Conventions:           CF-1.7\n",
       "    creation_date:         2021-10-04T13:59:35.374080+00:00\n",
       "    frequency:             day\n",
       "    institution:           NASA Earth Exchange, NASA Ames Research Center, Mo...\n",
       "    ...                    ...\n",
       "    history:               2021-10-04T13:59:35.374080+00:00: install global a...\n",
       "    disclaimer:            This data is considered provisional and subject to...\n",
       "    external_variables:    areacella\n",
       "    cmip6_source_id:       ACCESS-CM2\n",
       "    cmip6_institution_id:  CSIRO-ARCCSS\n",
       "    cmip6_license:         CC-BY-SA 4.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hurs = xr.open_dataset(fsspec.open(item.assets[\"hurs\"].href).open())\n",
    "hurs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53de3988-3b98-43c5-ac5c-7d90a2ad7701",
   "metadata": {},
   "source": [
    "Or you can use `xarray.open_mfdataset` to load all the variables for an item, which will combine each of the varaibles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1b003e5-edc1-485d-8d72-932616b6abd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.14 s, sys: 241 ms, total: 1.38 s\n",
      "Wall time: 5.54 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ds = xr.open_mfdataset(\n",
    "    [fsspec.open(asset.href).open() for asset in item.assets.values()]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fe4d732-c664-4de2-8f33-c9abada97525",
   "metadata": {},
   "source": [
    "*Note that opening all those variables is relatively slow. See [below](#Using-a-Reference-File) for an alternative.*\n",
    "\n",
    "We can plot all the variables for a single day with xarray, matplotlib, and cartopy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "60c0a50b-da43-4254-b7b2-11d4623ef08b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/datasets-nasa-nex-gddp-cmip6-nasa-nex-gddp-cmip6-example.ipynb/11.png\"/>"
      ],
      "text/plain": [
       "<Figure size 1152x648 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", message=\"__len__\")\n",
    "warnings.filterwarnings(\"ignore\", message=\"Iteration\")\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(\n",
    "    figsize=(16, 9),\n",
    "    ncols=3,\n",
    "    nrows=3,\n",
    "    subplot_kw=dict(projection=ccrs.Robinson()),\n",
    "    sharex=True,\n",
    "    sharey=True,\n",
    ")\n",
    "\n",
    "day = ds.isel(time=0)\n",
    "\n",
    "for i, (v, data) in enumerate(day.data_vars.items()):\n",
    "    ax = axes.ravel()[i]\n",
    "    r = data.plot(ax=ax, transform=ccrs.PlateCarree(), add_colorbar=False)\n",
    "    ax.set(title=v)\n",
    "\n",
    "fig.suptitle(pd.to_datetime(day.time.data).strftime(\"%Y-%m-%d\"))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23331e61-be08-40fb-89a3-9ef1a731e315",
   "metadata": {},
   "source": [
    "#### Creating a timeseries\n",
    "\n",
    "Each STAC item represents a single year. You can also use `xarray.open_mfdataset` to concatenate data for multiple years into a timeseries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2ec9ae5a-d870-4409-95f6-d0b9ad4b71da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.06 s, sys: 1.07 s, total: 8.13 s\n",
      "Wall time: 33.7 s\n"
     ]
    },
    {
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       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
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       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
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       "\n",
       ".xr-var-dtype {\n",
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       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-preview {\n",
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       "  display: none;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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       "\n",
       ".xr-var-data > table {\n",
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "\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.DataArray &#x27;hurs&#x27; (time: 18628, lat: 600, lon: 1440)&gt;\n",
       "dask.array&lt;concatenate, shape=(18628, 600, 1440), dtype=float32, chunksize=(366, 600, 1440), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1950-01-01T12:00:00 ... 2000-12-31T12:00:00\n",
       "  * lat      (lat) float64 -59.88 -59.62 -59.38 -59.12 ... 89.38 89.62 89.88\n",
       "  * lon      (lon) float64 0.125 0.375 0.625 0.875 ... 359.1 359.4 359.6 359.9\n",
       "Attributes:\n",
       "    standard_name:  relative_humidity\n",
       "    long_name:      Near-Surface Relative Humidity\n",
       "    units:          %\n",
       "    comment:        The relative humidity with respect to liquid water for T&gt;...\n",
       "    cell_methods:   area: time: mean\n",
       "    cell_measures:  area: areacella</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'hurs'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 18628</li><li><span class='xr-has-index'>lat</span>: 600</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-31b33f76-d4a6-4d12-8b34-86a826ebdd92' class='xr-array-in' type='checkbox' checked><label for='section-31b33f76-d4a6-4d12-8b34-86a826ebdd92' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(365, 600, 1440), meta=np.ndarray&gt;</span></div><div class='xr-array-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> 59.96 GiB </td>\n",
       "                        <td> 1.18 GiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (18628, 600, 1440) </td>\n",
       "                        <td> (366, 600, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 153 Tasks </td>\n",
       "                        <td> 51 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "       &#x27;1950-01-03T12:00:00.000000000&#x27;, ..., &#x27;2000-12-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2000-12-30T12:00:00.000000000&#x27;, &#x27;2000-12-31T12:00:00.000000000&#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'>-59.88 -59.62 ... 89.62 89.88</div><input id='attrs-93bcdaca-3927-417d-bd10-1731cc8f8a2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93bcdaca-3927-417d-bd10-1731cc8f8a2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9561860-dba1-4fb6-95c0-446260c0eead' class='xr-var-data-in' type='checkbox'><label for='data-c9561860-dba1-4fb6-95c0-446260c0eead' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-59.875, -59.625, -59.375, ...,  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'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-c7647573-a512-473c-9244-3a2205028276' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7647573-a512-473c-9244-3a2205028276' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a155845-52e7-436a-b94e-34da8f35d36c' class='xr-var-data-in' type='checkbox'><label for='data-5a155845-52e7-436a-b94e-34da8f35d36c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
       "       3.59875e+02])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3823cc32-58af-4488-962f-959de505d01a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3823cc32-58af-4488-962f-959de505d01a' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>relative_humidity</dd><dt><span>long_name :</span></dt><dd>Near-Surface Relative Humidity</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>comment :</span></dt><dd>The relative humidity with respect to liquid water for T&gt; 0 C, and with respect to ice for T&lt;0 C.</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'hurs' (time: 18628, lat: 600, lon: 1440)>\n",
       "dask.array<concatenate, shape=(18628, 600, 1440), dtype=float32, chunksize=(366, 600, 1440), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1950-01-01T12:00:00 ... 2000-12-31T12:00:00\n",
       "  * lat      (lat) float64 -59.88 -59.62 -59.38 -59.12 ... 89.38 89.62 89.88\n",
       "  * lon      (lon) float64 0.125 0.375 0.625 0.875 ... 359.1 359.4 359.6 359.9\n",
       "Attributes:\n",
       "    standard_name:  relative_humidity\n",
       "    long_name:      Near-Surface Relative Humidity\n",
       "    units:          %\n",
       "    comment:        The relative humidity with respect to liquid water for T>...\n",
       "    cell_methods:   area: time: mean\n",
       "    cell_measures:  area: areacella"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "ts = xr.open_mfdataset(\n",
    "    [fsspec.open(item.assets[\"hurs\"].href).open() for item in items],\n",
    ")\n",
    "ts[\"hurs\"]"
   ]
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   "id": "83c2a873-acb8-4986-a79d-a0866f695ade",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Using a Reference File\n",
    "\n",
    "*Note: the approach described here is experimental and may change without warning.*\n",
    "\n",
    "In the previous section, we created a single `xarray.Dataset` from many NetCDF files (either many variables, or a timeseries for a single variable). Reading the metadata of a NetCDF / HDF5 file over the network is somewhat slow, making the `open_mfdataset` operation take about 20-30 seconds, *just to read the metadata*.\n",
    "\n",
    "So in addition to the NetCDF files, we provide a **reference file** which stores the positions of each variable in each NetCDF file's binary stream. This reference file can be opened with `fsspec` and `zarr` and used normally with xarray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1408a919-18f4-4a48-8640-7e4d5a3403bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "references = requests.get(collection.assets[\"ACCESS-CM2.historical\"].href).json()\n",
    "references = planetary_computer.sign(references)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2894dc54-1e41-4310-a08c-2e43d7572967",
   "metadata": {},
   "source": [
    "This reference file contains links to the original NetCDF files, along with the positions and lengths of each variable in the files. As usual, we need to sign the URLs to include short-lived tokens so that we can read the data.\n",
    "\n",
    "We can pass that set of references to fsspec's `ReferenceFileSystem`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "db2f8167-4065-46cb-99b3-b9e6eae91549",
   "metadata": {},
   "outputs": [],
   "source": [
    "reference_filesystem = fsspec.filesystem(\"reference\", fo=references)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aed5eb24-8ecb-4f49-bf56-7dd5bbb84f0b",
   "metadata": {},
   "source": [
    "And (quickly!) open the referenced files with xarray and Zarr."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "892b6095-4045-4f78-be05-cf1241c28d56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 547 ms, sys: 7.75 ms, total: 554 ms\n",
      "Wall time: 661 ms\n"
     ]
    },
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 23741, lat: 600, lon: 1440)\n",
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       "  * lat      (lat) float64 -59.88 -59.62 -59.38 -59.12 ... 89.38 89.62 89.88\n",
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       "  * time     (time) datetime64[us] 1950-01-01T12:00:00 ... 2014-12-31T12:00:00\n",
       "Data variables:\n",
       "    hurs     (time, lat, lon) float32 dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    huss     (time, lat, lon) float32 dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    pr       (time, lat, lon) float32 dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    rlds     (time, lat, lon) float32 dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    rsds     (time, lat, lon) float32 dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    sfcWind  (time, lat, lon) float32 dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    tas      (time, lat, lon) float32 dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    tasmax   (time, lat, lon) float32 dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "    tasmin   (time, lat, lon) float32 dask.array&lt;chunksize=(1, 600, 1440), meta=np.ndarray&gt;\n",
       "Attributes: (12/22)\n",
       "    Conventions:           CF-1.7\n",
       "    activity:              NEX-GDDP-CMIP6\n",
       "    cmip6_institution_id:  CSIRO-ARCCSS\n",
       "    cmip6_license:         CC-BY-SA 4.0\n",
       "    cmip6_source_id:       ACCESS-CM2\n",
       "    contact:               Dr. Rama Nemani: rama.nemani@nasa.gov, Dr. Bridget...\n",
       "    ...                    ...\n",
       "    scenario:              historical\n",
       "    source:                BCSD\n",
       "    title:                 ACCESS-CM2, r1i1p1f1, historical, global downscale...\n",
       "    tracking_id:           16d27564-470f-41ea-8077-f4cc3efa5bfe\n",
       "    variant_label:         r1i1p1f1\n",
       "    version:               1.0</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-5ee9a14c-d081-41a4-af7b-5a0621037f7a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5ee9a14c-d081-41a4-af7b-5a0621037f7a' 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>: 23741</li><li><span class='xr-has-index'>lat</span>: 600</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-06b054f4-2f89-4d81-bca3-f1485dd160f3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-06b054f4-2f89-4d81-bca3-f1485dd160f3' 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'>-59.88 -59.62 ... 89.62 89.88</div><input id='attrs-27400cd8-d61d-4d8c-83f3-b96e2d9ed735' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27400cd8-d61d-4d8c-83f3-b96e2d9ed735' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b0c9f6f-22d6-4249-a14a-6a11ab47034b' class='xr-var-data-in' type='checkbox'><label for='data-9b0c9f6f-22d6-4249-a14a-6a11ab47034b' 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>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([-59.875, -59.625, -59.375, ...,  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'>0.125 0.375 0.625 ... 359.6 359.9</div><input id='attrs-8206ea0d-85ba-4ed9-a1b7-3499620d741b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8206ea0d-85ba-4ed9-a1b7-3499620d741b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbdeb7b6-b4a1-4a32-b110-0c93c4cdcfeb' class='xr-var-data-in' type='checkbox'><label for='data-fbdeb7b6-b4a1-4a32-b110-0c93c4cdcfeb' 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>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([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,\n",
       "       3.59875e+02])</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[us]</div><div class='xr-var-preview xr-preview'>1950-01-01T12:00:00 ... 2014-12-...</div><input id='attrs-6003ad81-1125-4b9a-bdbf-39d34e3050b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6003ad81-1125-4b9a-bdbf-39d34e3050b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a20dde9-096f-4007-962b-4a592f9e7145' class='xr-var-data-in' type='checkbox'><label for='data-2a20dde9-096f-4007-962b-4a592f9e7145' 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>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;1950-01-01T12:00:00.000000&#x27;, &#x27;1950-01-02T12:00:00.000000&#x27;,\n",
       "       &#x27;1950-01-03T12:00:00.000000&#x27;, ..., &#x27;2014-12-29T12:00:00.000000&#x27;,\n",
       "       &#x27;2014-12-30T12:00:00.000000&#x27;, &#x27;2014-12-31T12:00:00.000000&#x27;],\n",
       "      dtype=&#x27;datetime64[us]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c4ae81db-ecbc-41aa-b908-31a9419af870' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c4ae81db-ecbc-41aa-b908-31a9419af870' class='xr-section-summary' >Data variables: <span>(9)</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>hurs</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, 600, 1440), meta=np.ndarray&gt;</div><input id='attrs-6f9f8b24-b3ef-4ee8-a6c0-23b26dd5acf7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f9f8b24-b3ef-4ee8-a6c0-23b26dd5acf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53faaf24-93ec-424a-bfc1-c1ab72e95c17' class='xr-var-data-in' type='checkbox'><label for='data-53faaf24-93ec-424a-bfc1-c1ab72e95c17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>The relative humidity with respect to liquid water for T&gt; 0 C, and with respect to ice for T&lt;0 C.</dd><dt><span>long_name :</span></dt><dd>Near-Surface Relative Humidity</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd><dt><span>units :</span></dt><dd>%</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> 76.41 GiB </td>\n",
       "                        <td> 3.30 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (23741, 600, 1440) </td>\n",
       "                        <td> (1, 600, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 23742 Tasks </td>\n",
       "                        <td> 23741 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>huss</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, 600, 1440), meta=np.ndarray&gt;</div><input id='attrs-20dea65a-37ae-4b8c-91d8-81ca6885fd98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20dea65a-37ae-4b8c-91d8-81ca6885fd98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51cffb0a-165a-4299-a21a-7e6b2030bf6c' class='xr-var-data-in' type='checkbox'><label for='data-51cffb0a-165a-4299-a21a-7e6b2030bf6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>Near-surface (usually, 2 meter) specific humidity.</dd><dt><span>long_name :</span></dt><dd>Near-Surface Specific Humidity</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 76.41 GiB </td>\n",
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       "                        <th> Count </th>\n",
       "                        <td> 23742 Tasks </td>\n",
       "                        <td> 23741 Chunks </td>\n",
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       "  <text x=\"136.266578\" y=\"85.775669\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,136.266578,85.775669)\">600</text>\n",
       "  <text x=\"35.294118\" y=\"85.668985\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,85.668985)\">23741</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f5267e8d-0af1-4f37-8347-b52bcf6b7d05' class='xr-section-summary-in' type='checkbox'  ><label for='section-f5267e8d-0af1-4f37-8347-b52bcf6b7d05' class='xr-section-summary' >Attributes: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>activity :</span></dt><dd>NEX-GDDP-CMIP6</dd><dt><span>cmip6_institution_id :</span></dt><dd>CSIRO-ARCCSS</dd><dt><span>cmip6_license :</span></dt><dd>CC-BY-SA 4.0</dd><dt><span>cmip6_source_id :</span></dt><dd>ACCESS-CM2</dd><dt><span>contact :</span></dt><dd>Dr. Rama Nemani: rama.nemani@nasa.gov, Dr. Bridget Thrasher: bridget@climateanalyticsgroup.org</dd><dt><span>creation_date :</span></dt><dd>2021-10-04T13:59:21.654137+00:00</dd><dt><span>disclaimer :</span></dt><dd>This data is considered provisional and subject to change. This data is provided as is without any warranty of any kind, either express or implied, arising by law or otherwise, including but not limited to warranties of completeness, non-infringement, accuracy, merchantability, or fitness for a particular purpose. The user assumes all risk associated with the use of, or inability to use, this data.</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>history :</span></dt><dd>2021-10-04T13:59:21.654137+00:00: install global attributes</dd><dt><span>institution :</span></dt><dd>NASA Earth Exchange, NASA Ames Research Center, Moffett Field, CA 94035</dd><dt><span>product :</span></dt><dd>output</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>references :</span></dt><dd>BCSD method: Thrasher et al., 2012, Hydrol. Earth Syst. Sci.,16, 3309-3314. Ref period obs: latest version of the Princeton Global Meteorological Forcings (http://hydrology.princeton.edu/data.php), based on Sheffield et al., 2006, J. Climate, 19 (13), 3088-3111.</dd><dt><span>resolution_id :</span></dt><dd>0.25 degree</dd><dt><span>scenario :</span></dt><dd>historical</dd><dt><span>source :</span></dt><dd>BCSD</dd><dt><span>title :</span></dt><dd>ACCESS-CM2, r1i1p1f1, historical, global downscaled CMIP6 climate projection data</dd><dt><span>tracking_id :</span></dt><dd>16d27564-470f-41ea-8077-f4cc3efa5bfe</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>version :</span></dt><dd>1.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 23741, lat: 600, lon: 1440)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -59.88 -59.62 -59.38 -59.12 ... 89.38 89.62 89.88\n",
       "  * lon      (lon) float64 0.125 0.375 0.625 0.875 ... 359.1 359.4 359.6 359.9\n",
       "  * time     (time) datetime64[us] 1950-01-01T12:00:00 ... 2014-12-31T12:00:00\n",
       "Data variables:\n",
       "    hurs     (time, lat, lon) float32 dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    huss     (time, lat, lon) float32 dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    pr       (time, lat, lon) float32 dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    rlds     (time, lat, lon) float32 dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    rsds     (time, lat, lon) float32 dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    sfcWind  (time, lat, lon) float32 dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    tas      (time, lat, lon) float32 dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    tasmax   (time, lat, lon) float32 dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "    tasmin   (time, lat, lon) float32 dask.array<chunksize=(1, 600, 1440), meta=np.ndarray>\n",
       "Attributes: (12/22)\n",
       "    Conventions:           CF-1.7\n",
       "    activity:              NEX-GDDP-CMIP6\n",
       "    cmip6_institution_id:  CSIRO-ARCCSS\n",
       "    cmip6_license:         CC-BY-SA 4.0\n",
       "    cmip6_source_id:       ACCESS-CM2\n",
       "    contact:               Dr. Rama Nemani: rama.nemani@nasa.gov, Dr. Bridget...\n",
       "    ...                    ...\n",
       "    scenario:              historical\n",
       "    source:                BCSD\n",
       "    title:                 ACCESS-CM2, r1i1p1f1, historical, global downscale...\n",
       "    tracking_id:           16d27564-470f-41ea-8077-f4cc3efa5bfe\n",
       "    variant_label:         r1i1p1f1\n",
       "    version:               1.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "ds = xr.open_dataset(\n",
    "    reference_filesystem.get_mapper(\"/\"),\n",
    "    engine=\"zarr\",\n",
    "    backend_kwargs={\"consolidated\": False},\n",
    "    chunks={},\n",
    ")\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27b8189b-07b9-4d90-9343-028f216e2786",
   "metadata": {},
   "source": [
    "This reference file system includes all the variables for all the years covered by the specific scenario."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "448e1b8b-94b4-46b7-8757-2cb0326447a9",
   "metadata": {},
   "source": [
    "### Next Steps\n",
    "\n",
    "For more on the NEX-GDDP-CMIP6 dataset, visit the [dataset's homepage](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6). If you're working with large subsets of this data, you might want to [scale with Dask](https://planetarycomputer.microsoft.com/docs/quickstarts/scale-with-dask/)."
   ]
  }
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